diff --git a/.gitignore b/.gitignore
index b679827..254c77e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,13 @@
tmp.bib
-docs/.DS_Store
-docs/*/.DS_Store
+.DS_Store
+
+_build/
+_deploy/
+
+~$*
+
+.venv/
+venv/
+
+.ipynb_checkpoints/
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..ba0a9d6
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "MUSE_OS"]
+ path = MUSE_OS
+ url = https://github.com/EnergySystemsModellingLab/MUSE_OS.git
diff --git a/.markdownlint.json b/.markdownlint.json
new file mode 100644
index 0000000..dd30538
--- /dev/null
+++ b/.markdownlint.json
@@ -0,0 +1,5 @@
+{
+ "MD013": false,
+ "MD025": false,
+ "MD045": false
+}
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..0ecf27f
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,27 @@
+repos:
+ - repo: https://github.com/pre-commit/pre-commit-hooks
+ rev: v4.6.0
+ hooks:
+ - id: end-of-file-fixer
+ - repo: https://github.com/igorshubovych/markdownlint-cli
+ rev: v0.41.0
+ hooks:
+ - id: markdownlint-fix
+ - repo: https://github.com/codespell-project/codespell
+ rev: v2.3.0
+ hooks:
+ - id: codespell
+ args: [--skip, "*.bib"]
+ - repo: https://github.com/astral-sh/ruff-pre-commit
+ rev: v0.6.7
+ hooks:
+ - id: ruff-format
+ types_or: [python, pyi, jupyter]
+ - id: ruff
+ types_or: [python, pyi, jupyter]
+ args: [--fix]
+ - repo: https://github.com/kynan/nbstripout
+ rev: 0.7.1
+ hooks:
+ - id: nbstripout
+ files: ".ipynb"
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..2f244ac
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,395 @@
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diff --git a/MUSE_OS b/MUSE_OS
new file mode 160000
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--- /dev/null
+++ b/MUSE_OS
@@ -0,0 +1 @@
+Subproject commit 9a6a9f939a10258e521b955b54af38509789fc60
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-
-
-
-
-
-
-
- Hands On Exercise 1: Installing MUSE
-
-
-
-
-
-
-
-
Hands On Exercise 1: Installing MUSE
-
-
Short description
-
This hands-on exercise will allow you to install MUSE on your computer. We will then take you though an example to run and visualise a default MUSE example.
-
If at any point you get stuck with these hands-on exercises, feel free to post a question in the purpose-made MUSE google, group if your question hasn’t already been answered there!:
In all of the hands-on, there is an accompanying video linked at the start of the lectuer. Here we show you how to do the hands-on. This should make the process simpler to follow.
Windows users and developers may need to install Windows Build Tools. These tools include C/C++ compilers which are needed to build some python dependencies.
-
MacOS includes compilers by default, hence no action is needed for Mac users.
-
Linux users may need to install a C compiler, whether GNU gcc or Clang, as well python development packages, depending on their distribution.
-
If you have MacOS or Linux you can skip this section and head to the next section below here.
Please select the “Build Tools for Visual Studio 2019 (version 16.9)”. Click on download. Save the vs_BuildTools.exe.
-
Run the installer
-
Install options: select only the “Windows 10 SDK” (assuming the computer is Windows 10)]. This will come up on the right-hand side of the screen.
-
-
The installation screen should look similar to the following:
-
-
Figure 1.1: Visual Studio Installer window
-
Installing MUSE
-
MUSE is developed using python, an open-source programming language, which means that there are two steps to the installation process. First, python should be installed. Then so should MUSE.
-
The simplest method to install python is by downloading the Anaconda distribution. Make sure to choose the appropriate operating system (e.g. windows), python version 3.9, and the 64 bit installer. Once this has been done follow the steps for the anaconda installer, as prompted.
-
Open the Anaconda prompt for Windows machines or terminal if on MacOS or Linux and create a new environment hosting python 3.8.
After python is installed we can install MUSE. MUSE can be installed via the Anaconda Prompt or CMD.exe (or any terminal on Mac and Linux). This is a command-line interface to python and the python eco-system. In the anaconda prompt, run:
Click on the green Code button in the top right-hand corner and then click on Download ZIP. Figure 2.1 shows how to do this, once you are on the relevant page.
-
-
Figure 2.1: How to download MUSE
-
Once you have downloaded the source code, unzip the folder and move it to a location that is convenient for you.
-
We will place ours on the desktop for simplicity, but feel free to make a folder in your documents or otherwise.
-
To run MUSE, we must open the anaconda prompt for Windows machines or terminal if on MacOS or Linux. Then we must navigate to the directory using the prompt or terminal to find the MUSE examples. Ours is in Desktop, so we will run the following command:
-
python -m muse -model --default
-
Alternatively, once we have navigated to the directory containing the example settings (settings.toml) we can run the simulation using the following command in the anaconda prompt or terminal:
-
python -m muse settings.toml
-
If running correctly, your prompt should output text similar to the following:
-
-- 2020-11-03 15:58:29 - muse.sectors.register - INFO
-Sector legacy registered.
-
--- 2020-11-03 15:58:29 - muse.sectors.register - INFO
-Sector preset registered, with alias presets.
-
--- 2020-11-03 15:58:29 - muse.sectors.register - INFO
-Sector default registered.
-
--- 2020-11-03 15:58:29 - muse.readers.toml - INFO
-Reading MUSE settings
-
--- 2020-11-03 15:58:29 - muse.readers.toml - INFO
- Default input values used: carbon_budget_control.commodities, carbon_budget_control.method, carbon_budget_control.debug, carbon_budget_control.control_undershoot, carbon_budget_control.control_overshoot, carbon_budget_control.method_options
-
Results
-
If the default MUSE example has run successfully, you should now have a folder called Results in the same directory as settings.toml.
-
This directory should contain results for each sector contained within this example (Gas, Power and Residential) as well as results for the entire simulation in the form of the files MCACapacity.csv and MCAPrices.csv.
-
MCACapacity.csv contains information about the energy capacity each agent has per technology per benchmark year. Each benchmark year is the modelled year in the settings.toml file. In our example, this is 2020, 2025, …, 2050.
-
MCAPrices.csv has the converged price of each commodity per benchmark year and timeslice. For example, it has the cost of electricity at night for electricity in 2020, and other similar results.
-
Within each of the sector result folders, there is an output for capacity for each commodity in each year. Future years, which the simulation has not run to, refers to the technology capacity as it retires. Within the Residential folder there is also a folder for Supply within each year. This refers to how much end-use commodity was output.
-
Some of these terms will not be familiar to you yet, but do not worry about this for now. This hands-on is just guiding you the basic process from installation to data visualisation and later hands-on material will give more information.
-
Visualisation
-
There are many different ways to visualise the results of MUSE. For example, you could use a programming language such as python or R. In this course, however, we will use Excel for simplicity.
-
There are also many different variables and combinations of data that we can plot. In this course we will primarily explore the capacity installed over the time horizon (2020 to 2050). Through this, we can see which of the technologies are invested in and understand the competition between technologies.
-
To start the visualisation process of the default example, navigate to the folder where you run the default example in anaconda prompt or CMD.exe. For instance:
-
cd {MUSE_download_location}/StarMuse/run/example/default/
-
Go into the folder called /Results/ and right click on the file called MCACapacity.csv and open it with Microsoft Excel. Once you’ve opened the file with Excel, we can begin the data visualisation process.
-
First, select the PivotChart button under the insert menu. Ensure that the “Select a table or range” highlights all of the data, and the “Choose where you want the PivotChart to be placed” is selected to “Existing worksheet” like in the figure above. Then within the “Table/Range” box, click the cell where you would like the figure to be placed. Click “OK” when finished.
-
You can choose to filter with “sector”, add “technology” to columns, “year” to rows, and display “capacity” as sum.
-
You can then display a barchart like the one in Figure 1.3, below:
-
-
Figure 1.3: Insert -> Create PivotChart
-
Summary
-
In this hands-on we have installed MUSE, learnt how to run a demo example and visualised the results of this demo example. In the next lectures and hands-on we will learn in detail the fundamentals of MUSE and how to edit the demo example for a case study of our choice.
-
-
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- Hands On Exercise 2: Modifying a service demand
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Hands On Exercise 2: Modifying a service demand
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This hands-on will allow users to define their own service demand for an exogenous sector.
As a quick example, in the residential sector a service demand could be cooking. Houses require energy to cook food and a technology to service this demand, such as an electric stove.
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We will start by looking at the default example. This can be found in your MUSE download at /src/muse/data/example/default/example, or you can download it at the following link:
The file you will need to start with is called default.zip, the finished version for your records is called final_version.zip
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Next, download this and place it in a convenient location on your computer. We will now start by adding a cooking demand to this example. The default example currently only has a service demand of heat, so we will need to do some editing.
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To achieve this, we will need to edit the Residential2020Consumption.csv and Residential2050Consumption.csvfiles found within the technodata/preset/ directory. The Residential2020Consumption.csv file allows us to specify the demand in 2020 for each region and technology per timeslice. The Residential2050Consumption.csv file does the same but for the year 2050. The datapoints between these are interpolated. We will explain further details on interpolation in lecture 5.
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Firstly, we must add the new service demand cook as a column in these two files. Next, we add the demand. We can do this in Excel, or an editor of your choice. This is how it may look like for you when you open the Residential2020Consumption.csv file:
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Figure 2.1: Residential2020Consumption file opened in Excel.
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We will add a new column called cook and enter some values for each timeslice. This can be seen through the addition of a positive number in the cook column.
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Figure 2.2: Modified Residential2020Consumption file opened in Excel.
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The process is very similar for the Residential2050Consumption.csv file, however, for this example, we often placed larger numbers to indicate higher demand in 2050.
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Next, we must edit the files within the input folder. For this, we must add the cook service demand to each of these files.
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First, we will amend the BaseYearExport.csv and BaseYearImport.csv files. For this, we say that there is no import or export of the cook service demand. A brief example is outlined below for BaseYearExport.csv:
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Figure 2.3: Modified BaseYearImport file opened in Excel.
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The same is true for the BaseYearImport.csv file:
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Figure 2.4: Modified BaseYearExport file opened in Excel.
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Next, we must edit the GlobalCommodities.csv file. This is where we define the new commodity cook. It tells MUSE the commodity type, name, emissions factor of CO2 and heat rate, amongst other things.
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The default version used for this tutorial is below:
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Figure 2.5: Non-edited GlobalCommodities file opened in Excel.
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We then add a new row at the bottom to include the cook commodity:
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Figure 2.6: Edited GlobalCommodities file opened in Excel.
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The CommodityName column must be consistent internally within the model. Whereas the Commodity column is for your reference.
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Finally, the Projections.csv file must be changed. This is a large file which details the expected future costs of the technology in the first benchmark year of the simulation, the subsequent and actual simulated costs will be calculated during the running of the model. We have highlighted in bold the changed column for this example.
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Figure 2.7: Edited Projections file opened in Excel.
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Addition of a cooking technology
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Next, we must add a technology to service this new demand. During this process we must be careful to specify the end-use of the technology as cook, which is case-sensitive.
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For this example, we will add two competing technologies to the residential sector to service the cooking demand: electric_stove and gas_stove to the Technodata.csv file in /technodata/residential/Technodata.csv.
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For this, we copy the gasboiler row for R1 and paste it for the new electric_stove. For gas_stove we copy and paste the data for heatpump from region R1. In the figure below we show this, but only show the first few columns for the interest of space. We will also relax the growth constraints to ensure that the growth in technologies can meet demand.
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The growth constraints are:
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MaxCapacityAddition: The maximum absolute capacity that the technology can grow in a single year.
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MaxCapacityGrowth: The maximum percentage that the technology can grow in a particular year.
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TotalCapacityLimit: The total absolute number that cannot be exceeded for a particular technology.
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Due to space constraints we can’t show all the values as the technodata file is very wide, but we can set the parameters to be the following for both technologies:
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MaxCapacityAddition: 100 PJ
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MaxCapacityGrowth: 20 PJ
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TotalCapacityLimit: 120 PJ
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Figure 2.8: Edited technodata file opened in Excel.
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As can be seen we have added two technologies with different cap_par costs to each other. We specified their respective fuels, and the enduse for both is cook.
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We must also add the data for these new technologies to the following files:
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CommIn.csv
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CommOut.csv
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ExistingCapacity.csv
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The CommIn.csv file details the input commodities for each technology. In this case, the inputs are gas and electricity. The CommOut file details the outputs of the technology, which will be the cook commodity.
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We must add the input to each of the technologies (gas and electricity for electric_stove and gas_stove respectively), outputs of cook for both and the existing capacity for each technology.
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Figure 2.9: Edited CommIn file opened in Excel.
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Notice in Figure 2.9 that we had to add a column for the new cook. We must also do the same for the CommOut file, below:
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Figure 2.10: Edited CommOut file opened in Excel.
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We must do this for the gas and power sector as well. This is just for consistency within MUSE.
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Next, we must edit the residsential/ExistingCapacity.csv file to detail how much existing capacity there is in the base year and beyond. The existing capacity details power plants, or other technologies, which are already installed in the real world and therefore not invested in by the model. It is important to have a clear idea about the real world system in the base year before we run MUSE.
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Figure 2.11: Edited ExistingCapacity file opened in Excel.
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Due to the additional demand for gas and electricity brought on by the new cook demand, it is necessary to relax the growth constraints for gassupply1 in the technodata/gas/technodata.csv file. For this example, we set this file as follows (see bold cells):
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Figure 2.12: Edited gas/technodata file opened in Excel.
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We must also ensure there are no 0 in the ExistingCapacity.csv for any of the sectors. This is because the MUSE model will produce an error. For error debugging it is helpful to go to the MUSE google groups. So to do this, go through the gas/ExistingCapacity.csv and power/ExistingCapacity.csv and replace them with a non-zero value, such as 0.01. Below is an example for the gas sector:
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Figure 2.13: Edited gas/ExistingCapacity.csv file opened in Excel.
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Next, we must run the simulation with our modified input files using the following command in the directory where you saved the default example. To do this follow the instructions shown in hands-on 1:
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python -m pip muse settings.toml
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The figure below shows the results for this new demand in the residential sector:
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Figure 2.14: Capacity results for the residential sector.
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We can see that electric_stove takes over completely. This is because of the lower cap_par value when compared to gas_stove. Do not be surprised if your results differ from this, as the MUSE model will change over time. The important thing is to understand the outputs from the inputs.
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For the final example input data (final_version.zip) showed in this tutorial and results spreadsheet, please refer to the link below:
Hands On Exercise 3: Productions constraints by timeslice
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In this hands-on we explain how to add constraints to outputs of technologies at certain timeslices. This could either by a maximum constraint, for instance with the solar PV example mentioned in the previous lecture (lecture 2). Or, this could be a minimum constraint, where we expect a minimum amount of output by a nuclear power plant at all times.
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Learning objectives
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Learn how to add min/max production constraints in MUSE
In this tutorial we will be amending the same default example (default.zip) as in hands-on 2, which you can find in the following zenodo link: https://zenodo.org/record/6346284#.YisfUS-l1pQ
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Firstly, we will be imposing a minimum service factor for gasCCGT (combined cycle gas turbine) in the power sector. This is the minimum that a technology can output per timeslice.
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To do this, we will need to create a new csv file that specifies the minimum service factor per timeslice.
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An example of the file, which also contains values for windturnine can be seen below and in the zenodo link.
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Figure 3.1: TechnodataTimeslices.csv file for the power sector.
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Notice that we have to specify the following columns: ProcessName, RegionName, Time, month, day, hour, UtilizationFactor, MinimumServiceFactor.
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The majority of these columns are self explanatory, and correspond to the columns in other csv files - for instance, ProcessName, RegionName and Time. The timeslice based columns, however, are dynamic and will match the levels as defined in the toml file.
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The majority of these columns are self explanatory, and correspond to the columns in other csv files - for instance, ProcessName, RegionName and Time. The timeslice based columns, however, are dynamic and will match the levels as defined in the settings.toml file in the main default folder.
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We need to link the TechnodataTimeslices.csv file to the MUSE model. So to do this, we must enter into the settings.toml file and under the [sectors.power] add the line technodata_timeslices = '{path}/technodata/power/TechnodataTimeslices.csv' as shown below. Although we must ensure that the TechnodataTimeslices.csv is in the /technodata/power/ folder of the default example.
Once this has been completed, we are able to run MUSE as before, with the following command:
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python -m muse settings.toml
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We can then view the results as before using Excel.
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Maximum timeslice constraint
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Next, we want to ensure that the supply of windturbine does not exceed a certain value during the day. This may be because, for example, there is reduced wind during the day. We will, therefore, modify the TechnodataTimeslices.csv file by changing the values of UtilizationFactor. This is shown in the figure below, where we change the morning and afternoon timeslices to be 0.5, as an example.
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Figure 3.2: Edited TechnodataTimeslices file opened in Excel.
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Once this has been saved, we can run the model again (python -m muse settings.toml). We can then visualise our results as before.
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Summary
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In this hands-on we have introduced the TechnodataTimeslices.csv file, and linked it to the settings.toml file. This has allowed us to vary the output of various energy technologies by their characteristics.
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- Hands On Exercise 4: Adding a technology
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In this section, we will add solar photovoltaics to the default model. We will be starting from scratch and not continuing with the examples from hands-on 2 and 3. Therefore, to achieve this, we must modify the input files in the default example (default.zip) which can be found in the zenodo link provided below.
We must note, before starting, that we require consistency in input and output units. For example, if capacity is in PJ, the same basis would be needed for the output files CommIn.csv and CommOut.csv. In addition, across sectors a commodity needs to maintain the same unit. In these examples, we use the unit petajoule (PJ).
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Next, we will edit the CommIn.csv file in the power sector, which specifies the commodities consumed by solar photovoltaics.
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The table below shows the original CommIn.csv version in normal text, and the added column and row in bold.
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Figure 4.1: Modified CommIn.csv file for the power sector
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We must first add a new row at the bottom of the file, to indicate the new solar photovoltaic technology:
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we call this technology solarPV
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place it in region R1
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the data in this row is associated to the year 2020
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the input type is fixed
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solarPV consumes solar
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As the solar commodity has not been previously defined, we must define it by adding a column, which we will call solar. We fill out the entries in the solar column, ie. that neither gasCCGT nor windturbine consume solar.
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We repeat this process for the file: CommOut.csv. This file specifies the output of the technology. In our case, solar photovoltaics only output electricity. This is unlike gasCCGT which also outputs CO2f, or carbon dioxide.
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Figure 4.2: Modified CommOut.csv file for the power sector
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Similar to the the CommIn.csv, we create a new row, and add in the solar commodity. We must ensure that we call our new commodity and technologies the same as the previous file for MUSE to successfully run, i.e. solar and solarPV. Please note that the commodity names are case-sensitive.
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Please note that we use flat forward extension of the values when only one value is defined. For example, in the CommOut.csv we only provide data for the year 2020. Therefore for the benchmark years, 2025, 2030, 2035… we assume the data remains unchanged from 2020.
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The next file to modify is the ExistingCapacity.csv file. This file details the existing capacity of each technology, per benchmark year. For this example, we will set the existing capacity to be 0.5 for all technologies in the base year and 0 for the remaining years. Please note, that the model interpolates between benchmark years linearly.
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Figure 4.3: Modified ExistingCapacity.csv file for the power sector
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Finally, the technodata.csv contains parametrisation data for the technology, such as the cost, growth constraints, lifetime of the power plant and fuel used. The technodata file is too long for it all to be displayed here, so we will truncate the full version.
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Here, we will only define the parameters: processName, RegionName, Time, Level,cap_par, Fuel, EndUse, Agent2 and Agent1
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We shall copy the existing parameters from the windturbine technology for the remaining parameters that can be seen in the technodata.csv file for brevity. You can see the full file at the zenodo link, below:
Again, flat forward extension is used here. Therefore, as in this example we only provide data for the benchmark year 2020, 2025 and the following benchmark years will keep the same characteristics, e.g. costs, for each benchmark year of the simulation.
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Figure 4.4: Modified Technodata.csv file for the power sector
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Notice that we have hidden the cells between F and T. These are the same as the windturbine technology, but we’ve changed the cap_par input to 30 and the Fuel technology to solar.
The BaseYearExport.csv file defines the exogenous exports for commodities. For our example we add a column to indicate that there is no export for solar. However, it is important that a column exists for our new commodity.
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It is noted, however, that the BaseYearImport.csv as well as the BaseYearExport.csv files are optional files to define exogenous imports and exports; all values are set to zero if they are not used.
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Figure 4.5: Modified BaseYearExport.csv file for the power sector
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The BaseYearImport.csv file defines the imports in the base year. Similarly to BaseYearExport.csv, we add a column for solar in the BaseYearImport.csv file. Again, we indicate that solar has no imports.
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Figure 4.6: Modified BaseYearImport.csv file for the power sector
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The GlobalCommodities.csv file is the file which defines the commodities. Here we give the commodities a commodity type, CO2 emissions factor and heat rate. For this file, we will add the solar commodity, with zero CO2 emissions factor and a heat rate of 1.
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Figure 4.7: Modified GlobalCommodities.csv file for the power sector
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The projections.csv file details the initial market prices for the commodities. The market clearing algorithm will update these throughout the simulation; however, an initial estimate is required to start the simulation. As solar irradiance as a fuel is free, we will indicate this by adding a final column.
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Please note that the unit row is not read by MUSE, but used as a reference for the user. The units should be consistent across all input files for MUSE; MUSE does not carry out any unit conversion.
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Figure 4.8: Modified projections.csv file for the power sector
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Running our customised simulation
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Now we are able to run our simulation with the new solar power technology.
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To do this we run the same run command as previously in the anaconda command prompt:
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python -m muse settings.toml
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If the simulation has run successfully, you should now have a folder in the same location as your settings.toml file called Results. It must be noted, however, that if you update a value and re-run the model, the results folder will be overwritten.
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The next step is to visualise the results using Excel.
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We will use the PivotChart, similar to that shown in hands-on 1. The file to be used is the MCACapacity.csv file. For our visualisation we have selected a stacked area chart, but you are free to choose the type you like.
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Figure 4.9: Visualisation with new technology.
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The power sector now shows us the new solarPV technology.
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- Hands On Exercise 5: Adding a service demand by correlation
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Hands On Exercise 5: Adding a service demand by correlation
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In hands-on 2, we added an exogenous service demand. That is, we explicitly specified what the demand would be per year.
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However, we may not know what the electricity demand is for each year into the future. Instead, we may conclude that our electricity demand is a function of the GDP and population of a particular region.
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To accommodate such scenarios, MUSE enables us to choose a regression function that estimates service demands from GDP and population, which may be more certain in your case. In this hands-on we find out how this can be done.
We recommend that you download these files and save them to a location convenient to you, as we will be amending these throughout this tutorial.
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Similarly to before, we must amend the preset folder for this. However, we no longer require the Residential2020Consumption.csv and Residential2050Consumption.csv files. These files set the exogenous service demand for the residential sector.
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We must replace these files, with the following files:
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A macrodrivers file. This contains the drivers of the service demand that we want to model. For this example, these will include GDP based on purchasing power parity (GDP PPP) and the population that we expect from 2010 to 2110.
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A regression parameters file. This file will set the function type we would like to use to predict the service demand and the respective parameters of this regression file per region. We will not go into detail about the different functions that you can choose, but for more information, please refer to the documentation: https://muse-docs.readthedocs.io/en/latest/
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A timeslice share file. This file sets how the demand is shared between timeslices.
Download these files and save them within the preset folder.
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Next, we must amend our TOML file to include our new way of calculating the preset service demand.
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TOML file
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Editing the TOML file to include this can be done relatively quickly if we know the variable names. This just requires opening the TOML file in a text editor of your choice.
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In the second bottom section of the TOML file, you will see the following section:
This enables us to run the model in exogenous mode, but now we would like to run the model from the files previously mentioned. This can be done by linking new variables to the new files, as follows:
Figure 5.1, below, shows the power sector over the future horizon. We can see a significantly higher installed capacity, as the demand has increased due to the correlation of GDP PPP and population.
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Figure 5.1: Visualisation of the power sector
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Summary
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In this hands-on we added a service demand by correlation. Specifically, GDP purchasing power parity and population. We saw that we could make inferences on how the demand will grow based on these using seperate files in MUSE.
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Hands On Exercise 6: Adding an agent
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Now we will learn how to add a new agent to our example.
In this hands-on, we will add a new agent called A2. This agent will be slightly different to the other agents in the default example, in that it will make investments based upon a mixture of levelised cost of electricity (LCOE) and equivalent annual cost (EAC). Where EAC is the is the annual cost of owning, operating, and maintaining an asset over its entire life. These two objectives will be combined by calculating a weighted sum of the two when comparing potential investment options. We will give the LCOE a relative weight value of 0.5 and the EAC a relative weight value of 0.5.
To do this, we will add two new rows to the file. To simplify the process, we copy the data from the first two rows of agent A1, changing only the rows: AgentShare, Name, Objective1, Objective2, ObjData1, ObjData2, DecisionMethod and Quantity. The values we changed can be seen below. Notice how we edit the AgentShare column. This variable allows us to split the existing capacity between the two different agents. There is a set list of objectives that can be chosen from, with more information provided at the documentation: https://muse-docs.readthedocs.io/en/latest/ We will also need to edit the Agents.csv file to define these new AgentShares.
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Figure 6.1: Updated Agents.csv.
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Also notice that we amend the Quantity column. The reason for this is that we want to specify that Agent A1 makes up 50% of the population, and A2 makes up the remaining 50% of the population.
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We then edit all of the technodata files to split the existing capacity between the two agents by the proportions we like. As we now have two agents which take up 50% of the population each, we will split the existing capacity by 50% for each of the agents. Notice that we only require the columns Agent2 and Agent4 to define the retrofit agents.
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The new technodata file for the power sector will look like the following (we have hidden the middle columns as they remain the same):
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Figure 6.2: Edited power technodata file.
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However, remember you will have to make the same changes for the residential and gas sectors!
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We will now save these files and run the new simulation model using the following command in Anaconda prompt:
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python -m muse settings.toml
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Figure 6.3 shows us the results of these two agents. We can see a divergence between technologies invested in by the agents dependent on their objectives
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Figure 6.3: Visualisation of the two different agents - a) agent = A1, b) agent = A2.
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For all the files explored in this hands-on, please refer to the following link: https://zenodo.org/record/6323453#.Yh-QWi-l1pQ
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Summary
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In this hands-on we added a new agent which had different characteristics to the original agent and saw that this led to a dramatic change in the technologies invested in.
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- Hands On Exercise 7: Adding a region
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Hands On Exercise 7: Adding a region
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Now we will learn how to add a new region to our example.
The next step is to add a region which we will call R2, however, this could equally be called USA or India. These regions do not have any energy trade. This requires us to undertake a similar process as in the previous hands-on when modifying the input simulation data. However, this time we will also have to change the settings.toml file to achieve this.
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The process to change the settings.toml file is relatively simple. We just have to add our new region to the regions variable, in the 4th line of the settings.toml file, like so:
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regions = ["R1", "R2"]
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The process to change the input files, however, takes a bit more time. To achieve this, there must be data for each of the sectors for the new region. This, therefore, requires the modification of every input file.
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Due to space constraints, we will not show you how to edit all of the files. However, you can access the modified files at the zenodo link below: https://zenodo.org/record/6327789#.YiI1ri-l1pQ
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Effectively, for this example, we will copy and paste the results for each of the input files from region R1, and change the name of the region for the new rows to R2.
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However, as we are increasing the demand by adding a region, as well as modifying the costs of technologies, it may be the case that a higher growth in technology is required. For example, there may be no possible solution to meet demand without increasing the windturbine maximum allowed limit. We will therefore increase the allowed limits for windturbine in region R2.
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We have placed two examples as to how to edit the power sector below. Again, the edited data are highlighted in bold, with the original data in normal text.
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The following file is the modified /technodata/power/CommIn.csv file:
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Figure 7.1: Updated CommIn.csv.
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Whereas the following file is the modified /technodata/power/ExistingCapacity.csv file:
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Figure 7.2: Updated ExistingCapacity.csv.
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Below is the reduced /technodata/power/technodata.csv file, showing the new windturbine in R2. For this, we highlight only the elements we changed from the rows in R1. The rest of the elements are the same for R1 as they are for R2.
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Figure 7.3: Updated Technodata.csv.
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Now, go ahead and amend all of the other input files for each of the sectors, the Agents file and the input files BaseYearExport, BaseYearImport and Projections.csv by copying and pasting the rows from R1 and replacing the RegionName with R2 for the new rows. All of the edited input files can be seen at the zenodo link: https://zenodo.org/record/6327789#.YiI1ri-l1pQ
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Again, we will run the results using the python -m pip muse settings.toml in anaconda prompt, and analyse the data using excel as follows:
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Figure 7.4: Capacity visualisation for both regions in the power sector - a) Region = R1, b) Region = R2.
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Summary
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In this hands-on we added a new fictional region with the same characteristics for both of these regions. We see that the output of the two regions in the power sector are the same. This is because the characteristics in both regions are identical.
This mini-lecture will provide an overview of how the course is structured.
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Learning objectives
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To present the structure of the course for both the lecture content and the hands-on sessions
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To obtain an introductory insight into the content delivered within this course.
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Introduction and structure of the course
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This course is made up of 8 lectures and 7 hands-on exercises. All the lectures have an accompanying hands-on exercise, apart from this lecture. Each of the lectures are made up of four, related mini-lectures. Whilst the lectures will give you a background to energy modelling, you will also learn how to practically build a case study using the MUSE model.
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To build up a good understanding of MUSE, you will be required to understand why we use energy systems modelling and the main features that make up a good energy systems model. You will learn of the trade-offs between different decisions, such as increasing data granularity versus overall model run time.
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The overall lecture structure is presented below:
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Energy systems modelling
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Introduction to MUSE
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Energy demands
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Timeslices
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Technologies
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Sectors
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Agents
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Regions
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First, you will have an overview of energy systems modelling. Then we will introduce you to the agent-based model, MUSE. The next lectures detail the key components that make up MUSE. Some of these, such as energy demands and timeslices are common to all energy systems models, and some are unique to MUSE’s agent-based modelling type, such as regions.
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The accompanying hands-on exercises are:
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Installing and running MUSE
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Adding a service demand
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Production constraints by timeslice
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Adding a new technology
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Adding a service demand by correlation
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Adding an agent
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Adding a region
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As you can see, through the hands-on exercises you will pick up the key skills needed to design an energy systems model with MUSE.
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Bibliography
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- Mini-Lecture 1.2 – Sustainable Development Goals and the global climate agenda.
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Mini-Lecture 1.2 – Sustainable Development Goals and the global climate agenda.
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Short description
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This mini-lecture will provide a background to sustainable development, the global agendas of the Sustainable Development Goals (SDGs) and an introduction to the role energy systems can play in achieving a range of sustainable development outcomes.
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Learning objectives
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Learn the importance of sustainable development and how it frames the major global development agendas
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Identify the general principles of the Sustainable Development Goals
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Lecture content
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Introduction
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The Sustainable Development Goals (SDGs) are a set of universal goals for every country. The SDGs are focused on ending poverty, improving quality of life and protecting the environment. These goals were agreed on in 2015, with 2030 set as the target year for their achievement. The Sustainable Development Goals aim to tackle multiple issues. Some of the goals are based around poverty, environmental protection, climate action, justice and more. The SDGs can be seen in Figure 1.2.1. These goals are designed to be thought of together, rather than in isolation. There are, therefore, many links between different goals.
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Figure 1.2.1: The 17 Sustainable Development Goals (United Nations 2015)
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In this mini-lecture we will explore these links, global progress on the SDGs and how to factor these goals into national planning and modelling efforts.
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Sustainable Development Goal 7
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Sustainable Development Goal 7 (SDG7) specifically focuses on the energy sector. Each SDG has more specific targets. The first target set in SDG7 is universal access to affordable, reliable and modern energy services. This includes electricity and access to clean cooking. Next, SDG7 calls for a substantial increase in the share of renewables in the global energy mix. This is to reduce greenhouse gas emissions and increase the sustainability of energy supplies. Another important element of improving the sustainability of the energy sector is energy efficiency. Therefore, the next SDG7 target requires a doubling of the rate of global improvement in energy efficiency. Finally, SDG7 targets improved cooperation to increase access to clean energy research and technology to promote investment, as well as expanded and improved modern energy infrastructure in developing countries. All of these targets are set in pursuit of reduced poverty and greater sustainability. Whilst SDG7 focuses explicitly on the energy sector, we will see later that all of the goals are strongly interlinked as energy is important for multiple other goals.
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Figure 1.2.2: Sustainable Development Goal 7 (United Nations 2015)
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Progress on SDG7: Access to Electricity and Clean Cooking
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According to the tracking SDG7 report, there has been progress in improving access to both electricity and clean cooking in recent years. This report states that electricity access increased from 83% in 2010 to 90% in 2018, whilst clean cooking increased from 56% to 63%. The report finds, however, that the current rates of progress are insufficient to meet the targets set by SDG7 by 2030. There are also key regions where progress has been substantially slower. For example, the population without access to electricity is concentrated in Sub-Saharan Africa – with an overall access rate of 47%.
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There are several ways in which energy modelling can be used to aid with these goals. For instance, geospatial electrification modelling can be used to assess which access solutions are the most economical for different regions or sub-regions. This also includes capacity expansion planning to assess how supply can be increased whilst minimising economic and environmental impacts.
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Progress on SDG7: Renewable Energy and Energy Efficiency
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The graph below shows the share of electricity, heat and transport demands met by renewables (such as solar, wind, hydro and geothermal energy) globally. We can see that there has been some progress in increasing the share of renewables in the electricity and transport sectors as well as in the heat sector when traditional biomass use is excluded. According to the SDG7 Tracking Report, the share of renewable energy in Total Final Energy Consumption reached 17.3% in 2017, up from 16.3% in 2010.
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The electricity sector has observed the most progress. This is largely due to the growth in solar photovoltaic (PV) and wind energy. However, more progress is required to achieve SDG7, with most scenarios requiring the decarbonisation of end-use sectors: for example, the electrification of transport and heat, sectors which have observed relatively slow progress.
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Figure 1.2.4: Renewable energy and energy efficiency progress (United Nations 2015)
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For energy efficiency, global reductions in primary energy intensity have slowed in recent years. Where energy intensity is the quantity of energy required per unit output or activity, so that using less energy to produce a product reduces the intensity. This is despite progress still being greater than in the period before 2010. The SDG7 Tracking Report analysis shows that the transport sector has seen an increase in energy intensity improvement since 2010, while other sectors have seen a decrease. Differences between regions are observed, with Sub-Saharan Africa having the highest energy intensity and Latin America and the Caribbean having the lowest.
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If the SDG7 target of doubling the rate of global improvement in energy efficiency by 2030 is to be met, energy efficiency measures must be prioritised in policy making and investment planning.
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Links between SDGs
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The SDGs are highly interlinked, with synergies and trade-offs between these targets. It is important that these interactions are understood so that policymakers can make plans to maximise synergies and minimise trade-offs. Modelling can help in the making of informed decisions by providing a better understanding of these interactions. This is because models can be used to develop cross-sectoral scenarios and can help project the impacts of decisions.
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As an example, let us explore the links between the energy system and the SDG targets. One study (Nerini et al. 2018) found that at least 113 of the 169 SDG targets require changes in energy systems. Examples of this include targets in SDG13, which focus on climate change action. This requires decarbonisation of the energy system. In addition SDG1, which focuses on ending poverty, requires improved energy infrastructure to increase modern energy access. Nerini et al. (2018) highlight that we cannot think in silos and must use integrated planning approaches with a long-term perspective. Energy modelling tools are a key enabler of such approaches.
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Summary
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In this mini-lecture we have explored the Sustainable Development Goals and how these apply to the energy systems and energy modelling domain. We discovered that the Sustainable Development Goals are highly interlinked, with the existence of different synergies and trade-offs between these different goals.
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While much progress has been made, significant further progress is required to meet many of these goals. Modelling approaches can be utilised to aid this aim by exploring integrated planning approaches with a long-term perspective.
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Bibliography
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-Nerini, Francesco Fuso, Julia Tomei, Long Seng To, Iwona Bisaga, Priti Parikh, Mairi Black, Aiduan Borrion, et al. 2018. “Mapping Synergies and Trade-Offs Between Energy and the Sustainable Development Goals.”Nature Energy 3 (1): 10–15.
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-United Nations. 2015. “Sustainable Development Goals kick off with start of new year.”
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In this mini-lecture we will explore the benefits of energy planning and why energy modelling is needed. We will discover why different countries require different solutions and how a continuous, iterative process is required to perform analyses.
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Learning objectives
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Learn the principles of energy planning
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Learn why energy modelling is needed
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Lecture content
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Introduction
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Energy has become a fundamental commodity over the last 100 years. It has allowed society to make significant progress in some of the Sustainable Development Goals. For instance, it has brought millions out of poverty across the world. However, it has required the development of other development goals such as SDG13, which focuses on climate action. In addition, economic and sustainable development have grown in an inequitable manner, with certain regions prospering more than others.
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This mini-lecture will introduce the concepts of energy planning in the context of the Sustainable Development Goals. In particular, we will explore how energy planning can be used to tackle some of the most difficult issues humanity faces.
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Why plan an energy system?
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The traditional methods of supplying energy come with some major pitfalls. One of the most significant issues is the emissions of carbon emissions, leading to climate change. However, we require energy to sustain the current global population; no energy would lead to mass starvation and population decline.
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Thus, a fundamental transformation of the energy system is required. Energy systems, however, are highly complex and capital intensive. In addition, these systems are constantly interacting with many other critical systems, such as the environment, natural resource systems and infrastructure. Thus, comprehensive, systematic analyses are required to avoid expensive stop-gap measures and long-term “lock-in” (Rodriguez, Delgado, and Sohns 2017).
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Importance of energy planning
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An additional complication is that there is no single solution that can be applied to all energy systems. Different geographies have differing needs and resources. For instance, the UK has access to lots of offshore wind, whilst Kenya has access to lots of geothermal energy. The energy demand profiles and challenges of these two countries are also very different.
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In developing countries, access to affordable energy services is important to combat energy poverty. This is especially true for rural areas, but it is becoming increasingly true for large metropolitan areas as urbanisation accelerates.
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In contrast, developed countries, such as those in Europe, North America and Asia, struggle with the replacement of ageing plants and equipment. It is estimated that 40% of existing capacity stock is scheduled for retirement by 2040.
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Therefore, investment decisions are required to alleviate these different issues. However, due to the long-term nature of these investments, these private sector dominated energy markets rely on clear and consistent government energy-environment policy to reduce uncertainty.
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What is energy planning?
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Energy planning is the act of assessing the ability of a local, national or regional system to provide dependable energy services under constantly changing conditions. For example, variables such as the cost of materials and fuels, investment costs in technologies, demand levels and distribution requirements may all change.
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Drawing on the field of operations research, electricity planning applies advanced analytical methods and tools to make better decisions when faced with complex decisions. This process, however, must be done iteratively due to the fast transformations that can take place over a very limited time period.
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Energy planning models help discern the most cost-effective way of delivering energy to the final consumer. Of course, the most effective way of providing energy is different in different parts of the world. However, quantitative energy modelling offers a promising tool to make better decisions under uncertain conditions.
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There is a requirement in developing countries for energy planning due to growing populations and a lack of access to electricity. However, the main barriers to energy models in developing countries are the lack of adequate data and a shortage of skilled human resources to perform the analyses. As a result, investment decisions are often based on ill-informed policy targets and the need for ad-hoc stop-gap measures. These measures, therefore, tend to focus on cheap and quick-to-build technologies. This can result in higher environmental and operating costs. It is often the case that such actions serve the supply shortfalls of already-connected consumers, and so, increasing access to energy is rarely part of the strategy.
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Acting on energy planning
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Energy planning is not an end in itself. Energy planning requires more than solely the mastering of energy modelling tools. Implementation based on the information provided by energy planning models is required. However, implementation requires a functional institutional framework to ensure the availability of funding, the timely readiness of energy infrastructure investments and a mechanism to oversee progress and quality control.
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However, sound project economics can mobilise the necessary finance, which is particularly true for large infrastructure investments. Finally, the physical deployment of infrastructure needs to match schedule logistics. For example, the introduction of a large hydropower plant may exceed current electricity demand. This may make it difficult to pay dividends and service debt. However, shortages may also results when electricity demand grows faster than supply, which leads to stop-gap measures and delayed economic development. Energy planning and energy modelling can help ensure the right level of investment in energy systems takes place.
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Benefits of energy systems models
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Quantitative models of complex systems can help decision makers in multiple ways. From a technical perspective, they allow analysts to compare different system configurations without incurring the upfront costs of building them. This allows for the mitigation of uncertainty.
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From a practical perspective, quantitative models facilitate the design of systems in a way that accounts for local resources, demands and the constraints that are placed upon real-life electricity systems. This enables the minimisation of consumer electricity bills by allowing governmental institutions to structure tariffs optimally.
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The development of scenarios can serve as an effective communication tool for non-partisan political commitment, which will help both to garner and mobilise private sector support, and to solicit agreement from society at large.
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For developing countries, the impact of even minor system improvements often can have disproportionally high positive economic and environmental returns.
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Summary
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In this mini-lecture we have covered the reasons that energy planning is important, how it can help with the Sustainable Development Goals and the various pitfalls that could occur without energy planning. We discovered that with sound energy planning, uncertainty can be reduced and greater stability can be provided to private sector investments. This can lead to sustainable economic growth if done in an optimal manner.
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Bibliography
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-Rodriguez, Diego, Anna Delgado, and Antonia Averill Sohns. 2017. “ENERGY ACCESS AND THE ENERGY–WATER NEXUS.” The World Bank.
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Mini-Lecture 1.4 – Energy systems model classifications
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Short description
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Energy systems models can take many different forms, as they can be designed for global long-term energy markets, short-term energy dispatch markets or local markets. In this mini-lecture we will explore the different types of models that fit into different classifications.
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Learning objectives
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Classify different types of energy systems models
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Identify the pros and cons of selected energy systems models
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Lecture content
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Introduction: Typical classifications
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Energy systems models can be broken down into four different categories in a typical classification. These include the time scales in which they model, the geographies in which they model, the analytical approach and the methodology (Pfenninger, Hawkes, and Keirstead 2014). The different classifications are shown below:
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Time Scale
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Short
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Medium
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Long
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Geography
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Global
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National
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Sub-national
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Analytical Approach
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Top-down
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Bottom-up
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Methodology
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Optimisation
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Simulation
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Time
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The classification of time leads to models used for different applications. For instance, a model which takes into account short-term timesteps may be able to look at weather conditions every 30 minutes or less for a single year. This means that the output, or dispatch of a particular system can be investigated for the short-term. A long-term model which models 50 years into the future, however, needs to make assumptions as it will not be able to go into the same level of detail regarding timeslices. These types of models, however, are better at considering the performance of long-term investments.
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Geography
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Different models take into account different geographies. Models which take into account the global system need to make assumptions and reduce complexity in other ways. This could be, for example, by reducing the amount of energy demand data that is modelled. However, models which only consider national or even sub-national geographies are able to incorporate more detail.
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Analytical approach
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Different models can take different approaches when modelling energy systems. These largely fall into the bottom-up or top-down methodologies. Bottom-up methodologies capture the technological details of the energy system, which allow for the modelling of competition between different technologies and technological progress. An example of this is the modelling of falling costs of solar photovoltaics when compared to coal power plants. Over time, competition between these technologies can be modelled to better understand the costs required to increase solar photovoltaics in the energy system. For example, if an energy planner only considers current costs, they may conclude that a coal power plant is more cost-effective. However, over a 20-year time period, if solar PV energy costs are projected to fall relative to coal power, then the most economic option may be to install solar PV in the future.
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A different, top-down perspective can be taken, however. Rather than looking at the technoeconomic details of technologies, economic relationships between energy systems and the economy can be explored. This covers the interactions across sectors and regions through the calibration of historical data.
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Methodology
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Within energy systems models there exist at least two broad methodologies which underpin the models. The first is optimisation. This is where an energy system is minimised or maximised by a certain metric. For example, we can find the energy system which has the lowest cost, or a system which maximises welfare of consumers.
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Simulations, on the other hand, are computer programs which describe system evolutions. These represent the behaviour of the main players in the energy system. This does not necessarily lead to a minimisation or maximisation of an objective, and it can take into account different uncertainties of what may occur as opposed to what should occur.
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Summary
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This mini-lecture has covered the different classifications that energy models can fit into. We have seen that a range of different models are required to cover the whole requirements of different energy systems, with models suited for different needs.
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Bibliography
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-Pfenninger, Stefan, Adam Hawkes, and James Keirstead. 2014. “Energy systems modeling for twenty- first century energy challenges.”Renewable and Sustainable Energy Reviews 33: 74–86.
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- Mini-Lecture 2.1 – MUSE (ModUlar energy system Simulation Environment)
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Mini-Lecture 2.1 – MUSE (ModUlar energy system Simulation Environment)
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Short description
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In this mini-lecture we will give an introduction into the energy systems model, MUSE (ModUlar energy system Simulation Environment). We will cover the differences between MUSE and intertemporal optimisation models. We will also address the advantages and disadvantages of using MUSE.
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Learning objectives
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Learn the difference between MUSE and intertemporal optimisation models
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Explore the pros and cons of using the energy systems model MUSE
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Introduction to MUSE
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We will first classify MUSE as per the classifications defined in the previous lecture. MUSE falls into the following categories:
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Long-term
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Global, national and local
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Bottom-up
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Simulation model
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Therefore, MUSE is mainly designed to understand how long-term energy markets may evolve on both a national and global scale. MUSE explicitly models technoeconomic data on various technologies and therefore is a bottom-up model. Finally, MUSE is a simulation model, and can model various competing objectives to display what could happen under certain scenarios.
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What are MUSE’s unique features?
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MUSE is a generalisable agent-based modelling environment that simulates energy transitions from the point of view of the investor and consumer agents (Sachs et al. 2019). This means that users can define their own agents based upon their needs and data. In addition, each of these agents can have different objectives. For instance, a proportion of the population may have higher disposable incomes, which allows them to spend more on heating and cooling rather than requiring cost minimisation. Another proportion may prefer to spend less on heating and cooling while still having high disposable incomes. This features differs from the optimisation-based approaches which can, for instance, minimise costs or maximise welfare from a central perspective.
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Another aspect that differs from optimisation models is the ability to model imperfect information and limited foresight. Optimisation models require full knowledge of the system at the beginning of the simulation. For example, such a model needs to know what the demand will be in 2050 at the beginning of the simulation in 2020. MUSE does not give this information to the investing agents at the beginning of the simulation, and therefore they must makes their investments under uncertainty. This adds a level of realism to MUSE and is a unique feature of agent-based models when compared to intertemporal optimisation models.
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Benefits and disadvantages of MUSE
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MUSE comes with a number of advantages and disadvantages when compared to other models. The benefits include, as discussed, the ability to model heterogeneous and diverse agents as well as to model limited foresight and imperfect information. Another one of the benefits of MUSE is its flexibility in designing a case study. Users can model anything from a single region to the global scale with trade occurring between regions. In addition, MUSE is able to model a single sector (such as the transport sector) to a whole energy systems approach. This flexibility allows for many different applications to be devised for interesting research and applications.
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However, this flexibility and simulation approach comes with a number of disadvantages when compared to other models. The first disadvantage is the complexity of the model. While building a case study is similar to the process for other models, the inner workings of MUSE can be complicated. This is due to its simulation-based method which relies on rule-based behaviours, as opposed to optimisation. Another disadvantage is that the computation time of MUSE can increase with the complexity of the case study. Therefore, it becomes important to make decisions based on the sectors, timeslicing and other characteristics that are modelled. For instance, it may not be feasible to model every single sector in an energy system, and instead the model should be limited to a subset of relevant sectors.
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Summary
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In this mini-lecture we were introduced to the energy systems model, MUSE. We learnt of its unique features, such as heterogeneous agent behaviour, limited foresight and imperfect information. We also discovered the advantages and disadvantages of MUSE. For example, its flexible nature, which allows many different types of case-studies, can also make the model increasingly complex.
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Bibliography
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-Sachs, Julia, Yiming Meng, Sara Giarola, and Adam Hawkes. 2019. “An agent-based model for energy investment decisions in the residential sector.”Energy 172: 752–68. https://doi.org/10.1016/j.energy.2019.01.161.
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In this mini-lecture we will give you an overview of how MUSE works. This will include the different sectors that make up MUSE, including primary supply sectors, conversion sectors and demand sectors. We will discover how these sectors are interlinked through a market clearing algorithm, which ultimately decides the prices of energy commodities and the final energy system, according to MUSE.
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Learning objectives
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Identify the key components which make up MUSE
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Understand the fundamental underpinning of how MUSE works
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MUSE Visualisation
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MUSE is made up of various components which interact to give a projected energy system as an output. Figure 2.2.1 displays these major components. The key sections of MUSE include:
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Primary supply sectors
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Conversion sectors
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Demand sectors
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Climate model (in the current model this is simplified by the use of a carbon budget.)
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Market clearing algorithm (MCA)
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Figure 2.2.1: Different components which make up MUSE.
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MUSE works by iterating between the sectors shown in Figure 2.2.1 to ensure that energy demands are met by the technologies chosen by the agents. Next, we will detail the calculations made by MUSE throughout the simulation.
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The service demand is calculated. For example, how much electricity, gas and oil demand is there for the energy services of cooking, building space heating and lighting in the residential sector? It must be noted that this is only known after the energy service demand sector is solved and the technologies invested in are decided.
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A demand sector is solved. That is, agents choose end-use technologies to serve the demands in the sector. For example, they compare electric stoves to gas stoves to meet the energy service demand of cooking. They then choose between these technologies based upon their:
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Search space (which technologies are they willing to consider?)
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Their objectives (which metrics do they consider important?)
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Their decision rules (how do they choose to combine their metrics if they have multiple?)
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The decisions made by the agents in the demand sectors then lead to a certain level of demand for energy commodities, such as electricity, gas and oil, as a whole. This demand is then passed to the MCA. The MCA then sends these demands to the sectors that supply these energy commodities (primary supply or conversion sectors).
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The supply and conversion sectors are solved: agents in these sectors use the same approach (i.e., search space, objectives, decision rules) to decide which technologies to invest in to serve the energy commodity demand. For example, agents in the power sector may decide to invest in solar photovoltaics, wind turbines and gas power plants to service the electricity demand.
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As a result of these decisions in the supply and conversion sectors, a price for each energy commodity is formed. This price is formed based on the levelised cost of energy of the marginal technology, that is, the technology which produces the most expensive unit of an energy commodity. This price is then passed to the MCA.
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The MCA then sends these prices back to the demand sectors, which are solved again as above.
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This process repeats itself until commodity supply and demand converges for each energy commodity for each region. Once these converge, the model has found a partial equilibrium in the energy system, and it moves forward to the next time period.
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Summary
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This mini-lecture provided key information to understand the underlying mechanics of MUSE. We learnt how MUSE is made up of different sectors, which are linked by a market clearing algorithm to simulate how prices are calculated. This mechanism closely models the real-life global electricity market.
Mini-Lecture 2.3 – Benefits of an Agent-Based Approach
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Mini-lecture 2.3 provides an overview of the benefits of using an agent-based modelling and simulation when applied to energy systems analysis. We will learn how we can more closely model real-life by relaxing some of the assumptions necessary in other energy systems models.
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Learning objectives
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Understand the concept of limited foresight
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Understand the concept of imperfect information
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Introduction
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As previously discussed, different modelling approaches have different advantages and disadvantages. One of the main differences that MUSE has, which it is able to model through its agent-based simulation approach, is its ability to model both limited foresight and imperfect information. These are significant relaxations when compared to optimisation-based models. In this mini-lecture we will explore these concepts in more detail and discover how these relate to MUSE and energy systems specifically.
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Imperfect information
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Imperfect information is an economic term which is the opposite of perfect information. With perfect information in a market, all consumers and producers have perfect and instantaneous knowledge of all market prices, their own utility and cost functions. However, in real-world energy markets, this is not the case. Some information is hidden or unknown, such as other player’s cost functions.
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With some models it is necessary to make this assumption of perfect information. For example the bids of all the agents in the market are known at all times. This is a significant assumption and can influence the final outcome of the model. By using the agent-based simulation methodology, we can avoid making this assumption and allow information to be hidden between agents, as happens in decentralised energy markets.
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Limited foresight
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Limited foresight specifies how players within a game understand how the future may evolve. In the real-world, prediction and forecasting are difficult problems to solve, particularly within the uncertainty of energy markets. This become even more challenging when trying to make long-term predictions.
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Within MUSE long-term predictions must be made by investor agents. For example, if a company wanted to invest in a power plant, they would need to predict the amount of money they can sell their electricity for over the lifetime of the power plant, or in other words the market price for electricity. In some cases power plants operate for 30 years or more and so electricity prices 30 years into the future are required!
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MUSE makes a simplified assumption about the future prices expected by investors: they know what the price will be in the next five years. However, they assume a flat forward extension of the prices from this period. Or in other words, the energy prices over the entire lifetime of the plant are the same as the known price in the next five years. However, this assumption that the investors make will more than likely not be correct, leading to errors in their predictions, just like in the real world.
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In contrast to perfect foresight, where variables such as prices, demand and technology costs in all the future time periods are known from the beginning of the simulation, using the limited foresight period, agents make investments under expectations of the market, which may be wrong.
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Figure 2.3.1, below, details how MUSE runs. Firstly, the initial capacity, price trajectory and demand trajectory are known and set by the user. These variables are exogenous to the model, which is to say that they are fixed and imposed on the model. These are used to initialise the MCA convergence algorithm. The MCA convergence algorithm finds a suitable set of investments which equilibrate supply and demand. Once equilibrium has been reached, the technologies are decided and the commodity prices are set. These commodity prices reflect the technology marginal costs, or the costs required to generate or create 1 unit of commodity, excluding capital costs. The investments balance asset retirements and the increase in demand, ensuring that supply meets demand.
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This whole process repeats itself at every timestep (t) until the specified number of milestone years have run.
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Figure 2.3.1: MUSE iteration process
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Summary
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This mini-lecture provided an introduction to the terms limited foresight and imperfect information. We learnt how these assumptions have been integrated into the MUSE model and what this means for the modelling process. In the next mini-lecture we will explore the key components that make up MUSE.
In this mini-lecture we will explore the key components which make up MUSE. These key components include the service demand, technologies, agents and sectors. We will now explore what these components do and how they interact.
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Learning objectives
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Understand what the key components of MUSE are
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Understand what these terms mean in the context of MUSE
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Understand how these components interact within MUSE
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Service Demand
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The energy service demand is a user input which defines the demand that an end-use sector has. An example of this is the service demand commodity of heat or cooling that the residential sector requires. End-use, in this case, refers to the energy which is utilised at the very final stage, after both extraction and conversion.
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The estimate of the energy service demand is the first step. This estimate can be an exogenous input derived from the user, or correlations of GDP and population which reflect the socio-economic development of a region or country.
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Technologies
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Users are able to define any technology they wish for each of the energy sectors. Examples include power generators such as coal power plants, buses in the transport sector or lighting in the residential sector.
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Each of the technologies are placed in their regions of interest, such as the USA or India. They are then defined by the following, but not limited to, technoeconomic variables:
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Capital costs
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Fixed costs
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Maximum capacity limit
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Maximum capacity growth
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Lifetime of the technology
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Utilisation factor
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Interest rate
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Technologies, and their parameters, are defined in a specific file called the Technodata file.
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Sectors
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Sectors typically group areas of economic activity together, such as the residential sector, which might include all energy consuming activities of households. Possible examples of sectors are:
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Gas sector
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Power sector
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Residential sector
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Industrial sector
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Each of these sectors contain their respective technologies which consume energy commodities. For example, the residential sector may consume electricity, gas or oil for a variety of different energy demands such as lighting, cooking and heating.
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Each of the technologies, which consume a commodity, also output a different commodity or service. For example, a gas boiler consumes gas, but outputs heat and hot water.
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Agents
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Agents represent the investment decision makers in an energy system, for example consumers or companies. They invest in technologies that meet service demands, like heating, or produce other needed energy commodities, like electricity. These agents can be heterogenous, meaning that their investment priorities have the ability to differ.
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As an example, a generation company could compare potential power generators based on their levelised cost of electricity, their net present value, by minimising the total capital cost, a mixture of these and/or any user-defined approach. This approach more closely matches the behaviour of real-life agents in the energy market, where companies, or people, have different priorities and constraints.
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Market Clearing Algorithm
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The market clearing algorithm (MCA) is the central component between the different supplies and demands of the energy system in question. The MCA iterates between the demand and supply of each of these sectors. Its role is to govern the endogenous price of commodities over the course of a simulation. In other words, it calculates the prices based on the supply and demand of the various technologies and regions.
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For a hypothetical example, the price of electricity is set in a first iteration to $70/MWh. However, at this price, the majority of residential agents prefer to heat their homes using gas. As a result of this, residential agents consume less electricity and more gas. This reduction in demand reduces the electricity price to $50/MWh in the second iteration. However, at this lower electricity price, some agents decide to invest in electric heating as opposed to gas. Eventually, the price converges on $60/MWh, where supply and demand for both electricity and gas are equal.
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This is the principle of the MCA. It finds an equilibrium by iterating through each of the different sectors until an overall equilibrium is reached for each of the commodities. It is possible to run the MCA in a carbon budget mode, as well as an exogenous mode. The carbon budget mode ensures that an endogenous carbon price is calculated to limit the emissions of the energy system to be below a user-defined value. Whereas, the exogenous mode allows the carbon price to be set by the user.
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Summary
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In this mini-lecture we have explored the different components which make up MUSE. We have explored the:
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Service Demand
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Technologies
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Sectors
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Agents
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Market Clearing Algorithm
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All of these components interact, for example the agents in a particular sector invest in technologies to meet a certain service demand. Finally, the market clearing algorithm brings these different components together to find an ultimate price on all the different factors of the particular case study.
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We will provide more information on agents and their capabilities in lecture 7.
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Bibliography
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- Mini-Lecture 3.1 – Energy demands in energy systems modelling
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Mini-Lecture 3.1 – Energy demands in energy systems modelling
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To begin lecture 3, this mini-lecture provides an overview of energy demands within an energy system. We will cover differences in energy demands by sector, time and population classes. We will also begin to explore why these differences are important within energy models. Lecture 3 will take you through the basics for modelling energy demand in MUSE, the different options available to do so, and some specific examples
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Learning objectives
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Learn what energy demands are in an energy modelling context
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Understand how demands can change based on different variables
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Introduction
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Everyone needs energy for many different purposes. The form in which this energy should be delivered is dependent on the specific application. These demands for energy come from all sectors of society such as:
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The residential sector (rural and urban)
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Cooking
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Heating
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Cooling
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Lighting
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Appliances
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Industry
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Chemical processes
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Steam production
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Heating
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Commerce
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Lighting
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Heating
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Cooling buildings
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Keeping products at low temperatures
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Transport
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Cars
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Trucks
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Buses
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Aviation
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Shipping
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Trains
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Agriculture
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Tractors
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Machinery
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Pumping water
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Variations in daily energy demand
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These energy demands can vary on hourly, daily, weekly and monthly timescales. This mainly reflects the schedule of consumers’ activities. For example, on a monthly timescale more cooling will be used in summer and more heating in winter. However, these energy demands can also vary by sector, as shown by Figure 3.1.1.
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Figure 3.1.1: Variations of energy demand by sector in a hypothetical example (Taliotis et al. 2018).
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Figure 3.1.1 shows us that the magnitude of demand varies by sector, with agricultural demand significantly lower than residential and commercial demand, in this example. The reason that the commercial and residential sectors consume more is because their activities are more energy intensive or they are simply larger.
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We can also see that the daily profile of demand varies by sector. For example, in Figure 3.1.1 we can see that there is a clear evening peak in residential demand, whereas agricultural and industrial demand remains flat throughout the day. This is because agricultural and industrial demands are consistent throughout the day. This is likely because the industrial and agricultural sector operate constantly, whereas energy use in homes peaks in the evening when consumers use more electricity for cooking, lighting and appliances when they return from work or other business.
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Sector specific demands
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The differences between sectors means that it can sometimes be important to model demands separately by each sector. This feature allows the models to consider the specific characteristics of each demand.
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Within each of these sectors, the energy demand varies over time and across different types of consumers. For example, within the residential sector, demands can differ between rural and urban households, as shown in Figure 3.1.2. This can also be true between grid-connected and off-grid areas. Energy planners must ensure that energy demand is always met for all types of consumers. Therefore, it is important that the key characteristics of different demands are represented in energy models.
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Figure 3.1.2: Variations of energy demand for the residential sector by population types (Olaniyan et al. 2018)
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Long-term variations in energy demands
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A major challenge in energy planning is that energy demands can change over time. This could be due to population growth or the creation of new industries. Figure 3.1.3 displays historical variations in energy demands. It is likely that these demands are correlated to changes in society. For example, increases in energy demand likely reflect increased industrial activity. For energy planning, we must also think about how energy demands are likely to change in the future.
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We can often forecast energy demand, such as with future projections as shown in Figure 3.1.3. These forecasts can be created using estimates of the key influencers of energy demand, such as population growth and economic activity. Future projections are often based on how energy demands have changed historically.
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Figure 3.1.3: Long-term energy consumption by source
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Capacity expansion planning
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One of the key purposes of MUSE is for capacity expansion. Figure 3.1.4 displays this key issue which MUSE can address. Essentially, if total demand increases (green line) and existing system capacities are retired (blue line), how can we invest to meet the energy capacity needed to supply demand (red line)?
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Figure 3.1.4: Capacity expansion (Taliotis et al. 2018)
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You may notice that the red line is higher than the green line at all points. This is due to losses due to lower generating efficiencies. The gap between the red and blue lines demonstrates the required capacity expansion over time. MUSE enables us to plan such a capacity expansion whilst considering technical, economic and environmental constraints.
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Summary
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In this mini-lecture we covered the differences between energy demands in different population types, sectors and timescales. We learnt why it is important to model these differences in demand in energy systems models. We also explored how energy systems models can be used to meet a changing demand profile in the future.
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Bibliography
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-Olaniyan, Kayode, Benjamin C. McLellan, Seiichi Ogata, and Tetsuo Tezuka. 2018. “Estimating residential electricity consumption in Nigeria to support energy transitions.”Sustainability (Switzerland) 10 (5). https://doi.org/10.3390/su10051440.
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-Taliotis, Constantinos, Francesco Gardumi, Abhishek Shivakumar, Vignesh Sridharan, Eunice Ramos, Agnese Beltramo, Holger Rogner, and Mark Howells. 2018. “Defining final energy demands in OSeMOSYS,” no. January.
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Mini-lecture 3.2 outlines the general requirements for defining energy demands and how modelling different scenarios can help assess potential future energy demand.
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Learning objectives
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Understand how to define energy demands
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Understand why we need scenario analysis
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Introduction
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Within modelling we can break up the previously defined energy demands by sector. Electricity comes from the power sector and can be used to fulfil demand from each of the final service sectors. For example, the residential, commercial or industrial sector.
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These sectors can have different electricity demands and needs and which can evolve over time as was seen in the last mini-lecture. We will now explore how these energy demands can be defined.
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Defining energy demands
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When defining an energy demand for energy systems models, it is important to identify the following:
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The energy carrier which the demand arises for. For example, electricity, gasoline for transportation or biomass for cooking.
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The sector the demand arises from. For example, residential (urban and/or rural, off- or on-grid), industrial or commercial.
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The average variability of the demand within a year. This is usually expressed using average demand profiles, which are explained in more detail later in this lecture.
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The current and expected future annual average demand.
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However, it is very difficult to predict future demand, and there will always be uncertainty in our predictions. Due to this it is important to model different scenarios.
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Defining our own energy demand
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As has just been seen, when we want to define our own energy demand, we need to identify a number of different features. Let’s say, for example, that we want to define the demand for electricity in urban homes. To do this, we need to define:
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The energy carrier for which the demand arises for. In this case it is electricity.
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The sector the demand arises from. In this example it is the residential sector, or the urban residential sector if you would like to be more specific.
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The average variability of the demand over the year. In this example we can look at daily and yearly electricity demand profiles for a residential urban area. This will tell us how the demand varies on a daily and seasonal scale.
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Current and predicted future demand. For this, we can look at an energy balance (covered in more detail later) to get data for the current and historical residential electricity demand. We can use these data as a baseline, and we could combine it with an estimate of population growth to create a future projection for the demand.
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Scenario analysis
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Within energy systems modelling, we must explore different possibilities of what could happen in the future. This is known as scenario analysis. We do this as the future is uncertain, particularly over the long-term horizon. We therefore might want to consider multiple scenarios to assess how demand could vary in the future.
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For example, for different scenarios, key predictors of energy demand, such as population growth, economic development and energy policy can be varied across the scenarios. This would mean that each scenario has a different energy demand projection.
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Since we can not be certain of the scenario which will be the best predictor of the future, it is useful to model several scenarios and consider the implications of each of them to give useful insights for policymaking. This allows policy makers to assess which of the different policies and mixes suit their needs based upon likelihoods and risk tolerances.
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Summary
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Mini-lecture 3.2 provided an overview of energy demands, how we can define them and the details which make them up. We also explored how we can perform scenario analysis with energy demands, to understand what could happen in the future.
Following mini-lecture 3.2, this mini-lecture provides an insight into how to model service demand within MUSE. There are two possible methods to model service demand in MUSE, from user input and by correlation. In this mini-lecture we will learn what the difference is between these.
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Learning objectives
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Understand how to input exogenous service demand
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Understand what service demand by correlation is
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Lecture content
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Service Demand
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A service demand is a term used to describe the consumption of energy by human activity. This could be, for instance, energy for lighting or cooking in the residential sector, personal vehicles in the transportation sector or machine usage in the industrial sector. The service demand drives the entire energy system, and it influences the total amount of energy used, the location of use and the types of fuels used in the energy supply system. It also includes the characteristics of the end-use technologies that consume energy.
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Exogenous service demand
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Within MUSE we must set the energy demand exogenously. That means that the model does not calculate how much the service demand is. Effectively, this means that the user must make an assumption on how much electricity is consumed in, for example, the residential sector for a particular region in the model.
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We can change this per scenario, but these values will not change during a simulation run, even if the price for all fuels increases significantly, for instance. We are able to define the exogenous service demand by year, sector, region and timeslice.
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Service demand by correlation.
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In the previous section we learnt about the exogenous service demand. That is, we can explicitly specify what the demand would be per year, sector, region and timeslice. However, it may be the case that we do not know what the electricity demand is per year, especially in the future. We may instead conclude that our electricity demand is a function of the GDP and population of a particular region, as previously discussed.
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To accommodate such a scenario, MUSE enables us to choose a regression function that estimates service demands from GDP and population projections, which may be more predictable or have more accessible data in your case. A regression function is simply a mathematical model which fits a linear model to your data to predict what may happen in the future.
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Sources for energy demand data
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We can get publicly available energy balance data and/or demand projections from the following sources:
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International Energy Agency
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International Renewable Energy Agency
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United Nations Statistics
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Asia-Pacific Economic Cooperation
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Energy balances tell us the amount that each energy commodity is used in a country or region in a given year. This is usually broken down by sector.
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Summary
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In this mini-lecture we introduced service demands, and the way we can input these into MUSE. The two ways we can input service demands are: - Exogenous service demand - Service demand by correlation
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We also learned where we can get energy data from for various countries.
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In the hands-on we will see how we can actually do this within MUSE.
Mini-lecture 3.4 explains how we can use timeslices to approximate the real-world demand profile. We will look into the difference between power and energy. Finally, we will learn how to convert units to ensure we are consistent within MUSE.
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Learning objectives
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Understand how timeslices can be used in the context of demand
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Understand the difference between power and energy
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Know the units to use within MUSE and how to convert these
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Demand profile
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Figure 3.1.5 shown an example demand profile for electricity that could be used in MUSE. In this demand profile there are 96 bars: one for each of the timeslices used in MUSE. These timeslices are split into 16 different sections – seasonal and into day and night. This is because there are four different seasons, which are split into day and night (twice). The demand profile is used to represent the proportion of demand occurring in each timeslice.
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Figure 3.4.1: Example demand profile for MUSE
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The chart shows us that electricity demand, in this example, is highest during the day in winter, while it is lowest during the night in spring. However, it is important to note that this is a simplification: in reality demand varies in the season and with each hour of the day. This simplification means that we model one representative day for each season, and we assume equal demand within days and nights of those seasons.
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Whilst this is a simplification, it allows us to consider the variation in demand across seasons and days without having an incredibly complex model structure. This reduces the amount of time required to run a full model relative to having timeslices for each hour and day of the year, as well as reducing the data input requirements.
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Units
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We must ensure that during our data input process we are consistent with our units. Usually we will use the petajoules unit as this is the unit for energy for different sectors. If you were just modelling the power sector, you could use megawatt hours.
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Power vs. Energy
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When using energy modelling tools it is important to remember the difference between power and energy. Sometimes these terms are used interchangeably. However, there is an important difference between the two:
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Energy is the total amount of work done or the total capacity for doing work
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Power is the rate at which this energy is supplied or used.
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Therefore, energy and power have different units. For example, energy is often measured in Joules, while power is often measured in Joules per Second (or Watts).
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For example, providing the weight stays the same, lifting a weight requires the exact same amount of energy no matter how quickly we lift it. However, if we lift the weight more quickly, the power has increased. We used the same amount of energy, but over a shorter amount of time.
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Units for demand
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It is important that we convert our data from different sources to petajoules (PJ) when we include it in MUSE.
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Here are some example conversion factors:
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1 Petajoule (PJ) = 1000 Terajoules (TJ)
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1 Petajoule (PJ) = 1,000,000 Gigajoules (GJ)
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3.6 Petajoules (PJ) = 1 Terawatt hour (TWh)
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0.0036 Petajoules (PJ) = 1 Gigawatt hour (GWh)
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We must ensure that we are consistent with the units we use within MUSE.
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Summary
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In this lecture we have learnt the difference between power and energy. We have also learnt how to use timeslicing to speed up our model and reduce complexity. Finally, we learnt that we must use consistent units.
Mini-Lecture 4.1 – Timeslicing in energy systems modelling
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This mini-lecture provides an overview of timeslicing in energy systems modelling.
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Learning objectives
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Learn why we use timeslices in energy systems models
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Understand the importance of representative days
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Introduction
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With energy systems models we must model how demand is met by supply. However, over the course of a year, or even over the course of 30 years we have large variations in demand and supply. For instance, the weather changes between years, seasons, and days. This all has an effect on the amount of energy that can be supplied by renewable energy sources such as solar and wind.
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It is also true that this variation in demand has a large impact on the demand. In a particularly cold year, or on a particular cold day, energy demand may significantly increase as consumers use more energy for heating. The same may be true during a particularly warm period if people need energy for cooling systems. We therefore need to model this variability.
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Representative days
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As you can probably imagine, matching supply and demand for every 30 minutes in a year is very costly in terms of computation time. If we must match supply and demand for every 30 minutes for 30 years (or more), we may end up with a very slow model in return for some gains in accuracy.
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However, it may be the case that we do not need to model a year in such high detail. In most cases, for long-term energy systems models, we can reduce the amount of detail to significantly increase the speed of the model, without losing significant accuracy (Kell, Forshaw, and McGough 2020).
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A common approach is to model 4 days for each year. Each day corresponds to a season of the year and is split into 24 timeslices (which equates to a timeslice representing one hour). Therefore, we maintain the variability within a day, but also within seasons. We will lose some of the extremely hot or cold days, but that matters less when we’re considering the long-term planning horizon.
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We do not always have to take into account entire days, to reduce the complexity further. For instance, we could have 8 days, but with only 2 timeslices (day and night). This will make the model run quickly, but may lose some detail. It is up to you, as the modeller, to find a sweet spot between accuracy and speed of computation. Various papers have been published to find this sweet spot, which you can look into in your own time (Poncelet et al. 2017).
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Summary
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In this mini-lecture we discovered why long-term energy models consider timeslices and representative days. Through this approach we are able to maintain high accuracy whilst also reducing computation time.
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Bibliography
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-Kell, Alexander J. M., Matthew Forshaw, and A. Stephen McGough. 2020. “Long-Term Electricity Market Agent Based Model Validation using Genetic Algorithm based Optimization.”The Eleventh ACM International Conference on Future Energy Systems (e-Energy’20).
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-Poncelet, Kris, Hanspeter Hoschle, Erik Delarue, Ana Virag, and William Drhaeseleer. 2017. “Selecting representative days for capturing the implications of integrating intermittent renewables in generation expansion planning problems.”IEEE Transactions on Power Systems 32 (3): 1936–48.
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In this mini-lecture we describe how different technologies can have different characteristics by timeslices.
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Learning objectives
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Understand the different characteristics of technologies by timeslice
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Understand how to characterise technologies by timeslice
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Introduction
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In the previous lecture we discovered the importance of timeslices. In this mini-lecture we will learn about how different technologies have different characteristics when it comes to timeslices, and how this can be modelled within MUSE.
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Technologies by timeslices
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Different technologies and supply sectors have different characteristics when it comes to timeslices. For instance, solar photovoltaics do not produce any energy when it is dark (for instance, at night) and produce less in the winter. Wind, on the other hand, has a completely different profile and is largely dependent on geography. Therefore, it would make sense to provide a maximum output of the technologies at different times. For instance, it would be useful if the model limited solar output at night time in the form of a maximum utilization factor. Where utilization factor is the ratio of average amount of energy output to total possible output of an energy technology if it were to run 100% of time.
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However, it can be very difficult to turn off some technologies, such as a nuclear power plant. Nuclear power plants are expensive to turn on and can be unsafe if constantly varying their power. Also, their marginal cost, or the cost to produce 1MWh of electricity excluding capital costs, is usually much lower than other power plants such as gas or coal plants. It, therefore, makes sense that we place a minimum service factor, or minimum output allowed, on nuclear, to ensure their output does not fall below a certain level.
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Other technologies, however, such as gas power plants, can be turned on and off readily; therefore we can simply leave an average utilization factor for all the timeslices.
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All of these features exist in MUSE, and during this lecture’s hands-on, we will show you how to do this within MUSE.
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Summary
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In this mini-lecture we have explored the importance of characterising technologies not just by their economic data, but also by their physical characteristics. We discovered that different technologies have different outputs at different times, such as solar and wind. We also found out that nuclear power, for instance, must output a certain level to remain within a safety range.
Mini-Lecture 4.3 - Different energy demands by timeslice
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This mini-lecture will continue exploring the importance of timeslices in energy modelling; however, it will have a particular focus on energy demands, and how these can change by timeslice and over the years.
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In the previous lecture we explored energy demands and timeslices. In this lecture we will have a brief recap of this, and explore how energy demand can be represented within MUSE.
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Learning objectives
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Understand how energy demand can change by timeslice
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Learn how energy demand is represented in MUSE
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Energy demand
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Energy demand can come in various forms. For instance, the demand we model can be for heating or cooling in the residential sector. It is the case that these demands have different characteristics. For instance, they may have different magnitudes and different technologies which serve these demands as well as they may be able to run at different times.
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Within MUSE, similarly to the supply sectors, we can model this time varying capability with timeslices. For instance, if we have 4 representative days which refer to the different seasons, we can model the high heating demand in winter and cooling demand in summer. On top of this we can vary these demands by time of day.
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To do this, we must edit the demand in the preset/Residential2050Consumption.csv sector. An example of which is shown in Figure 4.3.1.
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Figure 4.3.1: Example input for the preset sector.
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In this small example we see that there is only a demand for heat in the residential sector. However, this demand changes per timeslice (which are listed in the leftmost column). For instance, there is low demand for heat in timeslice 0 and a high demand for heat in timeslice 4. These timeslices refer to a single representative day, and therefore timeslice 4 has the highest demand for heat as it is in the late-evening, when people generally come home from work and turn on their radiators.
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In your models you can use datasets to disaggregate the demand into different types, or you can aggregate demand to include all gas or electricity utilised in the residential sector. This is largely dependent on the data available and the complexity of the model you would like.
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Summary
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In this mini-lecture, we explored the importance of timeslicing for modelling demand in energy models. We also covered how this can be done within MUSE using the preset sector.
This mini-lecture explores the relevance of timeslicing to climate policy. We will explore how different timeslicing can affect modelling results, why it is important to consider realistic timeslicing and how these can affect policy decisions.
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Learning objectives
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Understand the impact of timeslicing on modelling outputs
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Learn how timeslicing can affect policy decisions
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Timeslicing and policy
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Timeslicing is a core component of an energy systems model as we have previously discussed. If one were to use an inappropriate number of timeslices in an energy systems model, it is likely that this would have major implications on the model outputs.
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Let’s look at an example: if we were to model solar panels with an average capacity factor for the entire time horizon of the model this would assume that the solar panels can be used at night and could displace other technologies, such as gas turbines. However, in reality, solar panels contribute to the grid during the day and produce nothing at night. Therefore, we need some sort of flexibility in the system to ramp up after the sun sets. This needs to be modelled explicitly within MUSE, so to allow gas (or other technologies) to fill this gap in supply.
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If we take this conclusion further, it is possible to see scenarios where the intermittency of solar and wind are not modelled, and therefore we observe scenarios with a majority in solar or wind. With current technologies this is not possible, and this therefore underscores the importance of timeslicing.
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If we do not use accurate timeslicing then the model outputs can skew resulting policy, and so due care must be taken for sourcing data from different geographies.
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Summary
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In this lecture we have looked into the implications of different timeslicing decisions made when creating an energy systems model. We learnt that if we do not get this right, the investments made could be skewed and unrealistic.
This lecture will introduce the various technologies and how we can represent them within MUSE. We will also learn about the supply chains in which these technologies exist. Finally, we will learn about the key characteristics of the different technologies in the context of MUSE.
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Learning objectives
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Understand the concepts of technologies and supply chains
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Learn how to represent technologies in MUSE
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Understand the key characteristics of technologies
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Introduction
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A technology in MUSE represents a process, or a group of processes, that:
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Converts energy from one form into another. For example, the conversion of crude oil to oil products, oil products to electricity or electricity to heat.
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Transfers, transmits or distributes a form of energy, for example electricity transmission technologies.
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Supplies or produces a form of energy, for example oil imports or extraction, or a hydropower plant generating electricity.
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Technology examples
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Now we will discuss specific technologies and their role in the energy system.
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Within the energy system there exists natural gas for the generation of electricity. However, we have to represent a technology which extracts natural gas in the system. We can call this technology “gas extraction,” which outputs natural gas. This technology does not have any input fuel as it is a primary energy supply technology.
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A coal power plant, on the other hand, has an input of coal and an output commodity of electricity. This technology is an energy conversion technology and converts the energy in coal to electricity.
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Similarly, an oil power plant converts the energy in oil to electricity. It therefore has an input fuel of oil and an output commodity of electricity.
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It must be noted that some technologies can have more than one input or output fuel, such as a refinery with oil as the input fuel, producing both gasoline and heavy fuel oil as output fuels.
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Parameters that define technologies
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There are three main groups of parameters that are used to define technologies. These can be seen in Figure 5.1.1 below. These include input commodities, which refer to the fuel supply to the technology. For instance, what is the input fuel, what is the price of this, and what is the availability? Crucially, it can also contain the greenhouse gas emissions associated with the fuel.
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Secondly, there is techno-economic and environmental characteristics of technologies. These include technology costs, efficiency, lifetime and availability.
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Finally, we need to define each technology’s output commodity. This is the commodity which it produces, such as electricity from solar PV. Important data on output commodities includes their demand, impacts and when it is needed.
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Figure 5.1.1: Technology definitions by example parameters (Taliotis et al. 2018)
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Representing technologies in MUSE
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Since models are abstractions of reality, we can define technologies at different levels of abstraction depending on the nature of our energy model. Within MUSE, for instance, a single technology can represent a single power plant, or a group of similar power plants (for example, a technology could represent all coal power plants in a region if they had similar characteristics). The information provided can create a model with more or less granular data based upon the requirements of the user. It must be noted, that with increased granularity, an increase in computation time will be observed.
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It is possible within MUSE to represent all power plants as a single technology. This is appropriate when technologies do not change significantly between power plants or extraction plants.
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Key characteristics of technologies
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There are a number of different important technology characteristics that should be considered in capacity expansion planning. MUSE allows for several of these characteristics to be included. Such as:
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Variation in the availability, efficiency and costs of a technology over short and long timescales. For example, it may be the case that solar power reduces in costs over the next 30 years. If this happens, we would like to model this process and see the long-term effect on the market.
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MUSE can consider the limits on production by technology and capacity constraints. For example, there may only be a certain amount of hydro resources in a particular country, based on the number of rivers etc. It is important that MUSE takes this into account to ensure that the results are aligned to the reality in a region or country.
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Finally, the emissions associated with technologies can be captured. For example, we may want to reduce the carbon dioxide emissions of an entire system. This would allow us to compare scenarios and enable us to understand how we can reduce these emissions to reduce the impact of climate change. MUSE is also able to impose a limit on emissions through a constraint.
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Summary
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In this mini-lecture we have learned the importance of technologies within MUSE. We learnt that a technology can refer to a single power plant, to all coal power plants, for example. This is largely based on the requirements of individual case studies. We also learnt that technologies can also be processes, such as the extraction of natural gas. All of these different technologies come together to build an entire energy system, which MUSE is able to model.
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Bibliography
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-Taliotis, Constantinos, Francesco Gardumi, Abhishek Shivakumar, Vignesh Sridharan, Eunice Ramos, Agnese Beltramo, Holger Rogner, and Mark Howells. 2018. “Defining final energy demands in OSeMOSYS,” no. January.
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This mini-lecture will describe the techno-economic data that defines technologies in MUSE. These technoeconomics are fundamental to the functioning of a good MUSE model. Most technologies can be characterised by their efficiencies, technoeconomics and inputs and outputs. This is because the technologies must be competitive against each other in an economic sense.
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Learning objectives
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Understand the main technoeconomic parameters
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Understand how these parameters can impact investment decisions
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Technology costs
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In this mini-lecture we will describe the different techno-economic parameters that MUSE defines, primarily in the Technoeconomic.csv file found in the different sector folders.
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Figure 5.2.1 displays the different cost types as defined in MUSE. The total costs are largely split into capital costs and annual costs. Capital costs, as shown by the figure, are the costs of depreciation, return on investment and other one-time fixed charges. This can include the initial costs of the technology such as construction.
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Then there are annual costs, which are split into variable and fixed costs. There is a distinction between these two types of costs, where fixed costs depend on the capacity of the power plant, whereas variable costs depend on the amount of energy output in a year. For instance, if a power plant does not output any electricity, it will not have to pay for fuel. However, it will still have to pay for salaries to look after the plant.
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Figure 5.2.1: Cost types (Taliotis et al. 2018)
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In MUSE, these are defined in the cap_par, cap_exp, fix_par, fix_exp, var_par, and var_exp variables where:
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– cap_par is the capital costs, and cap_exp is the exponential component of this. Effectively, the cap_exp defines the reduction in cost due to economies of scale as the investment into this technology and its capacity increases. This should be a number between 0 and 1. – fix_par is the fixed costs, and fix_exp is the exponential component similar to the exponential component in cap_exp. – var_par is the fixed costs, and var_exp is the exponential component.
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The exponential component can be chosen from relevant data, but can often by difficult to find. In that case it is okay to use a number such as 1 or 0.95 as a rough indication.
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Growth constraints
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As previously mentioned, it is important to place realistic constraints on the growth of technologies. For instance, there is only so much resource or land potential for renewable energy resources, such as offshore wind. If a country or region does not have any access to land offshore, the limit for offshore wind should be zero. On top of this, it may not be possible to grow and install technologies faster than a certain rate. For instance, there may not be enough resources, such as steel and labour, to double the capacity of wind in a certain country.
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The parameters which set these can be found in the Technodata.csv file and are called:
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MaxCapacityGrowth
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MaxCapacityAddition
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TotalCapacityLimit
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Other technoeconomic parameters
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Other technoeconomic parameters include the lifetime of a technology, scaling size and interest rate. A technology may become much more attractive if we are able to use it for a longer amount of time. For instance, the economics of nuclear power plants can be very sensitive to the length of time they can be used for due to their high capital costs. It is therefore important that we have good data on the lifetime of the plant. This is set by the TechnicalLife parameter.
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The scaling size defines how small a single unit can be. For instance, a single nuclear power plant outputs a lot more energy than a single solar photovoltaic panel. This detail can be set by the ScalingSize parameter.
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The interest rate is the parameter which defines the discount rate. For instance, a technology may have a 2% return on investment, which may seem good. But it could also be possible to put the money required to build a technology into a high interest savings account and have a 4% investment. Thus the 2% return would actually reflect a loss relative to the rate of interest. This opportunity cost is the interest rate defined in the InterestRate parameter.
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Inputs and outputs
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Finally, there are the input and output parameters. For a gas power plant, the input is gas and the end use is electricity. This can be set in the Fuel and EndUse parameters respectively.
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Summary
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In this mini-lecture we have discovered the main components which make up the Technodata sheet. We discovered the importance of properly defining the costs, lifetime and other characteristics which have a large impact on the final investment decisions.
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Bibliography
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-Taliotis, Constantinos, Francesco Gardumi, Abhishek Shivakumar, Vignesh Sridharan, Eunice Ramos, Agnese Beltramo, Holger Rogner, and Mark Howells. 2018. “Defining final energy demands in OSeMOSYS,” no. January.
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In this mini-lecture we will learn about the input and output commodities within MUSE. Specifically we will learn what the CommIn.csv and CommOut.csv files do and how these relate to the energy system.
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Learning objectives
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To learn the importance of input and output commodities
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To learn how we can modify these commodities in MUSE
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Introducing commodities
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Input commodities are the commodities consumed by each technology. This could be coal for a coal power plant, uranium for a nuclear power plant or electricity for an electric heater. This is dependent on the technology, and some technologies can have multiple inputs.
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Output commodities are similar, but are the outputs of technologies. For example the output of any power plant will be electricity, and for heaters the output will be heat. Again, this is dependent on the technology, and some technologies can have multiple outputs such as combined heat and power plants.
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The ratio between these two parameters is very important in MUSE and in energy modelling in general. This is because it defines the efficiency of the technology. For instance, if a coal power plant requires 1 PJ of energy stored in coal to output 0.8 PJ of electricity, the coal power plant has an efficiency of 0.8. The higher the efficiency the more economical the power plant is and the more competitive it will be when compared to different technologies.
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Editing the CommIn and CommOut files
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Within MUSE there are two files which one should change to edit these parameters: the CommIn.csv and CommOut.csv files. These files are found within the sector folders of the case study. For instance, in the power/CommIn.csv or gas/CommOut.csv directories.
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In this example we will look at the residential sectors CommIn.csv and CommOut.csv files. An example CommIn.csv file can be seen in the figure below:
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Figure 5.3.1: CommIn file for the residential sector
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Here we see two technologies: gasboiler and heatpump. They are both in region R1 and we are specifying the characteristics for the year 2020. The gasboiler only requires gas, but requires 1.16 PJ, whereas the heatpump requires only 0.4 PJ to produce some energy.
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However, it is important to note that these figures are meaningless without the CommOut.csv file. We need to know how much energy does the 1.16 PJ of energy produce in the gasboiler? As can be seen in the figure below showing an example CommOut.csv file, it is convention to select an output of 1. That way we only have to vary the CommIn.csv to change the efficiencies consistently.
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Figure 5.3.1: CommOut file for the residential sector
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Therefore, we can now conclude that the heatpump is much more efficient than the gasboiler as only 0.4 PJ are required to output 1 PJ of heat. If we divide 1 by 0.4, we get the efficiency of the heatpump, where 1/0.4= 2.5. Notice that the gasboiler also outputs carbon dioxide. It is important to take these emissions into account to have a complete understanding of the energy system. MUSE calculates these emissions endogenously.
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Summary
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This mini-lecture has explored the input and output commodities in MUSE. We have learnt that the CommIn.csv and CommOut.csv files relate to efficiencies when brought together in a ratio.
MUSE is flexible in its approach. It requires inputs for at least the base year, but does not necessarily need more than that to project forward. In this mini-lecture we will cover how MUSE deals with missing data and how to model future years
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Learning objectives
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Learn how to model costs in multiple years
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Understand how MUSE deals with missing data
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Understand interpolation
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Introduction
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Within the input sheets you may have noticed the Time column. In the default example this is set to 2020. However, what happens beyond these years if we do not specify a cost, for example? Also, what happens in 2030 if we only specify a cost in 2020 and 2040?
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Within MUSE, we make some assumptions. We assume that if there are no costs input into a model beyond a certain year, that the costs remain the same. This is known as a flat-forward extension. If, for example, we input costs in 2020 and 2040, we will interpolate the values in between these years linearly.
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An example of this is, say that the capital costs for a gas boiler is set to be 4 for a gas boiler in 2020 and 2 in 2040. We have not explicitly defined 2025, 2030 or 2035. Based on linear interpolation, MUSE will assume a value of 2.5 for 2025, 3 for 2030 (halfway between the year 2020 and 2040) and 3.5 for 2035.
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It must be noted, however, that MUSE does not allow a user to just update a single technology. For instance, if we want to specify the technology costs in 2035 for a coal power plant, we must also define the technology costs for every other technology in 2035 – although this cost need not be changed from the original value. We also do not need to define every year, however, as interpolation and a flat-forward extension can still be used.
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Practical example
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The figure below shows a snippet of the technodata file for the residential sector. We can see that we have data parametrising the technologies in 2020.
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Figure 5.4.1: Technodata for residential sector
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Let’s say that we want to update the capital costs (cap_par) for heat pumps in 2040, but do not want to update the prices for gasboilers. This is how we do it:
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Figure 5.4.2: Updated technodata for residential sector
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Notice that we need separate rows for both heatpump and gasboiler even though we are only making a change in the heatpump capital cost. If we do not do this we will encounter an error. In between 2020 and 2040 we will get interpolation.
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Summary
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In this mini-lecture we learned how to update costs in the time domain, and the assumptions MUSE makes if we do not give costs for every year. Namely, flat-forward extension and interpolation. We also learnt how to practically input these values in MUSE with the Technodata.csv file.
This mini-lecture introduces the concept of the residential sector
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Learning objectives
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Understand the role of the residential sector, its technologies and the main energy and societal challenges
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Overview of the residential sector and its demands?
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Energy is used for many different reasons in the residential sector, as shown by Figure 6.1.1. This image shows the share of residential energy by service demand. We can see that energy is used for many different purposes, from heating and cooking to cleaning and ironing. This split of energy demand will vary across different countries. Figure 6.1.1 shows residential energy demand in Italy, which will differ to countries in Asia, for instance. This is largely dependent on different climates, levels of development and lifestyles.
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Figure 6.1.1: Residential sector in Italy and the different demands (Mancini, Lo Basso, and De Santoli 2019). (Note: DHW refers to Domestic Hot Water).
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The total magnitude of energy demand varies by country as a total value, but also as energy demand per capita. This is strongly dependent on the level of electricity access and availability of other fuels in the country. Residential activities can use different forms of energy. For example, cooking can be met by burning biomass, oil products, natural gas or electricity. The fuels used vary by country.
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Residential sector technologies
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Some of the key residential technologies include lamps, cooking stoves, heating and air conditioning systems, as well as other electrical appliances. Some of these technologies can only use one fuel, such as electrical appliances and air conditioning which rely on electricity.
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However, in other cases multiple different fuels can be used for the same purpose. For example, heating. Heating can be met by burning biomass, natural gas, oil or electricity, for instance. These technologies have differing performance parameters. For example, electric stoves are usually much more efficient than biomass stoves. Different technological options also have different impacts on the environment and on human health. For example, the emissions from biomass can have detrimental impacts on human health, whereas electric stoves do not have emissions in the home.
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It is possible to model these different options in MUSE, which allows us to gain insights into their environmental and cost implications. Modelling can allow us to model the entire system as a whole, understand the trade-offs between certain technologies and make decisions on which policies to implement.
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Residential sector in MUSE
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Within MUSE we can model different technology options. For instance, if we are to model an electric stove and a biomass stove we would have different inputs (CommIn.csv file). However, we would have the same output (CommOut.csv file) of cooking demand. We can also model an increase in efficiency of a technology by lowering the value in the CommIn.csv file. It is possible to change the efficiency over time using interpolation or a flat-forward extension as explained in mini-lecture 5.4. We can also consider the costs of investing in more energy efficient appliances by increasing the cost of these high efficiency appliances relative to the low efficiency appliances. By doing this, we can understand where and when investments in energy efficiency might be economic.
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Summary
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In this lecture we have explored the residential sector. We considered the different demands that can reside within the residential sector and the different technologies that can be used to meet these demands. We also learnt of the difference in demands between countries and how we can model different technologies within MUSE.
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Bibliography
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-Mancini, Francesco, Gianluigi Lo Basso, and Livio De Santoli. 2019. “Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey.”Energies 12 (11). https://doi.org/10.3390/en12112055.
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This mini-lecture introduces the transport sector. We will explore the different demands and technologies within the transport sector and how we can model them within MUSE.
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Learning objectives
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The main characteristics of the transport sector
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How these can be modelled within MUSE
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Overview of the transport sector and its demands
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The transport sector is vital in the modern age. In the last few decades, the use of transport has increased significantly. This is as more people gain access to vehicles and develop lifestyles which rely on transport.
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Figure 6.2.1 shows different modes of transport. As can be seen, road transport is the most used transport mode. We can also see that over 90% of fuel used in the EU transport sector is petroleum based. This is similar across the world. However, this creates challenges due to the unsustainability of fossil fuels.
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Figure 6.2.1: Transport modes and fuel share in the EU (Arens et al. 2020).
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Due to the unsustainability of fossil fuels, other solutions have been taken up with support from governments around the world. For example, cars, motorbikes and buses can be fuelled by electricity. Electric vehicles have seen large reductions in cost and improvements in performance. Electric vehicles could play an important role in overcoming the sector’s challenges.
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It is possible to model the different technologies in MUSE, and observe competition between technologies based upon their technoeconomic parameters.
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Emissions
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The transport sector was estimated to be responsible for around 16% of global emissions in 2016 (Ritchie and Roser 2020). Thus, scenarios consistent with meeting global climate targets require transport sector emissions to decline rapidly. Therefore a rapid move towards sustainable technologies, such as electric vehicles is required. It is true, however, that some of the modes of transport are difficult to decarbonise. For example, it is difficult to decarbonise shipping and aviation technologies. This is because the energy density of lithium ion batteries and other technologies are lower than oil-based products. It is worth mentioning, however, that decarbonising transport is only useful if the energy sector increases its low-carbon electricity sources to supply the transport sector.
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Transport sector in MUSE
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Similar to the residential sector, we can define different technologies for the transport sector using technoeconomic parameters. For example, we can split road transport into three categories:
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Cars
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Motorcycles
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Buses
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We can then split these three categories into their propulsion system. For instance:
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Electric vehicles
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Conventional vehicles
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We can source road transport data from national energy balances such as from the IEA, and divide this between cars, motorcycles and buses based on the split of transport by mode in the country.
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We can then run a MUSE model with the different parameters and see the effect of these different parameters on agent investment decisions. These parameters could be fuel prices, technology costs or performance parameters. We can also run the model with a carbon limit, which places a tax on carbon emissions, allowing us to work out how to pick a desirable policy depending on what we are trying to achieve.
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Summary
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In this mini-lecture we have considered the transport sector and how we can model this within MUSE. We discussed the emissions of the transport sector, and how different technologies can be used to reduce these emissions.
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Bibliography
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-Arens, Stefan, Sunke Schlüters, Benedikt Hanke, Karsten von Maydell, and Carsten Agert. 2020. “Sustainable Residential Energy Supply: A Literature Review-Based Morphological Analysis.”Energies 13 (2). https://doi.org/10.3390/en13020432.
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-Ritchie, Hannah, and Max Roser. 2020. “Co₂ and Greenhouse Gas Emissions.”Our World in Data.
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Mini-Lecture 6.3 – The industrial and commercial sectors
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This mini-lecture reflects on
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Learning objectives
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The main characteristics of the industrial and commercial sectors
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How these can be modelled within MUSE
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Overview of the industrial and commercial sectors
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Next, we will explore the industrial and commercial sectors and their respective energy demands. Figure 6.3.1. shows the energy consumption for different sectors, including industrial, by OECD (generally high-income countries) and non-OECD countries (generally low- and middle-income countries). It is evident that the industrial sector is responsible for a large share of energy consumption across the world. The industrial sector is forecast to rise in non-OECD countries significantly. We must also consider this growing expected demand in the modelling process and during policy design.
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Figure 6.3.1: Energy consumption by sector, OECD and non-OECD (Mendoza et al. 2020).
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Energy is used in industry for a number of different purposes. For instance, heating and cooling, running machinery and chemical processes. These processes use a large variety of fuels and depend on the purpose, location and the technoeconomics.
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The commercial sector has a lower energy demand when compared to the industrial sector. This is because commercial processes, typically, are less energy intense and on smaller scales. This demand is often lighting, heating and to run office equipment and appliances.
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Industrial and commercial technologies
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Commercial activities use many different technologies which require energy inputs. For example, office electronics, lighting and heating systems. Many of these technologies use electricity. However, for some demands natural gas is used, for example for heating commercial buildings.
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The industrial sector uses a wide range of technologies. This includes heavy machinery, boilers, heating and air conditioning. Again, a wide variety of fuels can be used for this. However, there exist a number of processes, such as steel manufacturing which requires very high temperatures. This is usually only done by burning fossil fuels, as it can be difficult to reach these high temperatures with electricity.
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Modelling industrial and commercial sectors in MUSE
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Similarly to the residential and transport sectors, we can use an energy balance (IEA 2021) to estimate industry demands – for instance, for industry heating demands. There are different technologies available for industrial heating. These can be grouped in a way that makes sense for your case study. However, as an example we can group these into high heat and low heat, which are modelled as separate demands. This is because generating very high temperatures requires different technologies and processes to generating low heat.
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Again, we can group the technologies by their input fuel, such as biomass, coal, oil products or electricity with the CommIn.csv file. Through modelling with MUSE we can understand the emissions and economics of different technologies.
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In addition, the commercial sector will have a different demand load profile to the residential sector. This is because, typically, the demand will follow office times for the specific region, whereas the residential sector will follow the inverse of the office schedule.
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Summary
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In this mini-lecture we explored the industrial and commercial sectors. We learnt the difference between these two sectors in terms of demand and the different types of technologies used in these sectors. We saw that demand for the industrial sector is expected to rise significantly in non-OECD countries. Finally, we learnt how we can model different technologies in MUSE.
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Bibliography
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-IEA. 2021. “World Energy Balances: Overview.”IEA.
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-Mendoza, Daniel L., Carlo Bianchi, Jermy Thomas, and Zahra Ghaemi. 2020. “Modeling County-Level Energy Demands for Commercial Buildings Due to Climate Variability with Prototype Building Simulations.”World 1 (2): 67–89. https://doi.org/10.3390/world1020007.
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In this mini-lecture we will investigate the role of electrification in different sectors, as well as find out what sector coupling is.
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Learning objectives
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Understand the importance of sector electrification
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Understand the need for sector coupling
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Sector electrification
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Electrification is becoming increasingly important in all sectors of the economy in order to achieve decarbonisation goals. As we saw earlier, electrification can be used to decarbonise the residential, transport, industrial and commercial sectors. However, some sectors are likely to be easier to electrify than other sectors. We have seen rapid progress with electric vehicles in parts of the transport sector, but sectors such as shipping and steel, which are harder to decarbonise, still have a way to go.
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However, different options exist for the decarbonisation of steel, for example. This can be done by retrofitting blast furnaces and adding carbon capture and storage (CCS) or scaling up hydrogen-based direct reduced iron. However, this will require innovation and further research on the key technologies, such as CCS.
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Sector coupling
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We have seen that we must decarbonise to meet global climate targets. However, this is not a straightforward process. A large reason for this is the inflexibility of intermittent renewable resources such as solar and wind technologies. One method of mitigating this variability and inflexibility is through sector coupling. Sector coupling is where we connect energy demands and processes across differing sectors and increase the efficiency and flexibility of energy use. This would allows us to use renewable energy for all sectors.
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One way this could be achieved is through power to gas conversion. When there is a high supply of renewable power, excess electricity could be used to produce hydrogen and methane. This would allow us to store this energy for later use across multiple sectors. This would enable sectors that are difficult to electrify to be based on renewable energy.
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It is possible to model this sector coupling process within MUSE and to understand the tipping points which would make sector coupling possible. This could be based on the price and capacity of renewable energy, as well as the price of generating hydrogen or methane compared to the incumbent technologies.
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Summary
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In this lecture we have covered the importance of electrifying different sectors to reduce carbon emissions and meet some of the Sustainable Development Goals. We have also learnt of the importance of sector coupling to address hard to decarbonise sectors.
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Bibliography
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Mini-Lecture 7.1 – Agents in energy systems models
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In this mini-lecture we will describe the importance of agents within MUSE and also within an energy systems modelling context.
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Learning objectives
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Understand why agents are important in an energy modelling context
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Understand how we can characterise these agents within MUSE
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Agents overview
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Within real-life energy systems there are many different objectives that investors or consumers have. These objectives may differ by sector, by investor type or by proportions of the population. For instance, a certain percentage of the population may be willing to be spend more money on heating their homes than others.
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It is straightforward to specify these objectives and characteristics within MUSE. For instance, you may want to split a population based upon their geospatial and economic characteristics. This could be done by, for example, splitting a population into rural and urban categories. That would provide us with two groups. However, it is possible to go further, and we may want to split the rural and urban groups into different socioeconomic demographics, such as disposable income.
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Say for example, we only split the population into rural and urban. We can specify these groups as two agents within MUSE. Once we have specified the two agents, we would have to give them characteristics which differentiate them from the each other and define the proportion of the population that they make up. It must be noted, at this stage, that we do not need to have a separate agent for each individual or entity. It is perfectly fine to group and aggregate similar individuals or agents.
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Summary
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In this mini-lecture we understood the concept of agents and how they relate to an energy modelling context. We briefly understood how we can translate these concepts into MUSE. Urban populations might have greater energy needs or rural populations may not have access to the same energy sources. Giving the model a bit more detail will allow you to make sure that the model is both more accurate, and that its projections take into account different parts of society. In the hands-on we will learn how to add a new agent.
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Bibliography
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Mini-Lecture 7.2 – How to relate agent representations to the real world
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In this mini-lecture we will introduce some methods to translate socioeconomic data into MUSE with a quantitative approach.
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Learning objectives
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Discuss surveys and socioeconomic data and how these can relate to MUSE
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Discover ways that surveys can be used in quantitative modelling
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Qualitative representation in agent-based models
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Through the use of qualitative data, such as using qualitative surveys, it is possible to gain greater insight into the different characteristics of consumers or investors. One example of how this can be done was by Moya et al. (2020). In this paper the authors explore fuel-switching investment in the long-term energy transitions of India’s industry sector. They inform the modelled agents through a questionnaire that was carried out to inform MUSE.
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Some of the types of questions asked in the questionnaire to industrial companies are listed below:
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Geographical location
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Financial details
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Investment plans
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Type of fuels used
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Willingness to switch fuels
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Once these data have been collected, they can be used to find similar groups of investors and to start characterising the agents. For instance, if from the data it is clear that geographical location is an important consideration, the decision could be made to group companies by geographical region and form an agent on this basis. If the more important consideration is the investment plans, then a group can be made there.
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This approach is a more than efficient method of better understanding the characteristics of agents of a system, and it can help to inform a better modelling process. The work by Moya et al. ((Moya et al. 2020)) finds that the results represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. In other words, the survey results have an impact on the outcome of the energy system over the long-term.
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Summary
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In this mini-lecture we explored how surveys can be used to inform agents within MUSE. We also discovered how these results can affect the modelling outcomes of energy systems.
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Bibliography
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-Moya, Diego, Sara Budinis, Sara Giarola, and Adam Hawkes. 2020. “Agent-based scenarios comparison for assessing fuel-switching investment in long-term energy transitions of the India’s industry sector.”Applied Energy 274: 115295. https://doi.org/10.1016/j.apenergy.2020.115295.
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In this mini-lecture we will cover how agents and their characteristics can differ between sectors. We will also investigate the similarities between agents and sectors and consider the key parameters that make up agents.
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Learning objectives
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Understand the differences between agents of different sectors
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Understand the key parameters that differentiate agents
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Agent parameters
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Different sectors may mean having agents with different characteristics. For instance, within the residential sector socioeconomic data can be used to characterise the agents. We could use wealth to characterise our agents in different geographic locations. For example we could place a constraint on the Budget parameter for residential users, and split these agents into different proportions. For example, we could prohibit 70% of residential users from spending more than a certain amount on heating which could affect their technology choice. The other 30% of users would form an agent that was not constricted in this way, and thus their choices may end up being differet in the model.
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Another way we could classify residential agents is through the Maturity parameter. This would limit investments in novel technologies until the specified technology had a certain market share. This could be informed by the innovation adoption lifecycle, as shown by Figure 7.3.1. Where, for example, innovators make up 2.5% of the population but have no Maturity constraints. As we work our way up the curve from innovators to laggards, this Maturity constraint increases.
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Figure 7.3.1: Innovation adoption lifecycle
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Sectors
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In this mini-lecture we have focused on the residential sector and seen the way we can characterise agents. Although these characteristics may not directly translate to the power sector, in some cases investors in the power sector can have similar characteristics. For instance, some companies are larger, and are more willing to invest their capital, reflecting a larger Budget parameter. Others may be less willing to invest in new technologies. The differing objectives of agents will often be the reason behind differences with other agents. For instance, some agents may only want to minimise their costs, whereas others may want to reduce their capital expenditure. It is easy to change these characteristics within MUSE to create diverse energy scenarios.
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Summary
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In this mini-lecture we covered the differences between agents and the different parameters that can be used to inform these differences. We saw how the Maturity constraint maps to the innovation adoption lifecycle and how the Budget parameter can be informed by socioeconomic characteristics. These parameters lead to a large amount of possible scenarios that can be tested and run.
This mini-lecture explores all the major parameters that can define agents within MUSE.
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Learning objectives
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Understand the different agent parameters and their role within MUSE
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Overview agent parameters
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Within MUSE each agent can have their own objectives. MUSE is flexible enough to allow for up to 3 objectives, which can be summed together at various weightings. To input these objectives into MUSE one would use the Objective1, Objective2 and/or Objective3 parameters and select an objective such as comfort, lifetime_levelized_cost_of_energy or fixed_costs.
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Then we would select the weight of each of the objectives using the ObjData1, ObjData2, ObjData3 inputs. For example, if we had 3 objectives, we could make the objective of Objective1 dominant by setting ObjData1 to 0.5. This would mean it would make up 50% of the final objective.
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We can edit the SearchRule to reduce the space of technologies that those agents are likely to consider. For example, we could fill this with same_fuels, or same_enduse.
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The rest of the parameters include the parameters discussed in the previous lecture:
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MaturityThreshold
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Budget
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Summary
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In this mini-lecture we discovered the main parameters that are used by agents within MUSE. For a full breakdown of the parameters please refer to the MUSE documentation that can be found online.
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Bibliography
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Mini-Lecture 8.1 – Introduction to regions and aggregation
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This mini-lecture provides an overview of different regions within energy systems models and how these can be represented within MUSE.
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Learning objectives
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When to aggregate data into different regions
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Aggregation
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Regions within energy models play an important role. We often want to aggregate technoeconomic data from multiple regions into one. For example, the UK is made up of many different counties with different energy demands and supply. However, it could be the case that we do not have comprehensive data for each of these counties. We may, however, have plentiful data for the UK as a whole, or even for England, Scotland, Northern Ireland and Wales. We can therefore aggregate these data and make assumptions about the geographical locations of supply and demand.
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This is an example of aggregation and can make the modelling process more straightforward, whilst losing a small amount of accuracy. This is because we do not need to model each individual power plant, demand centre or end-use sector. This means we can use aggregated data which are often easier to access.
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We can also aggregate multiple countries into regions. For example, we can merge the European continent together. This would be especially useful if we are considering a global model. However, it must be noted that we would lose significant detail by aggregating up to a supranational level. It is up to you, the model user, to consider the trade-offs between aggregation and disaggregation. For example, if you only wanted to model a single country, it would be possible to have a single region. However, if you had good access to data at the local level, you could disaggregate the data further. It does not matter whether the region is a single country, a number of counties or at a supranational level. The regions depend on your case study and the data you have access to.
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Summary
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In this mini-lecture we learnt about the trade-offs between aggregation and disaggregation when defining regions. We learnt that the more aggregated the model, the less granular data are required. This can be helpful in cases where the data are not available at a local level, but available at a national level.
Mini-Lecture 8.2 – Disaggregation of regional data
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This mini-lecture introduces the concept of disaggregation of regions in further detail.
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Learning objectives
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When to disaggregate regional data in MUSE and energy systems models
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Disaggregation
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Disaggregation of regions can often be a good way of gaining a deeper understanding of the interactions between regions. For example, if you have a lot of technoeconomic data on the locations of supply and demand, then it may make sense to disaggregate regions. This will also allow the modeller to understand where there may be issues within a specific region or country.
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An interesting example of this would be for the Southeast Asia region. Laos has a good amount of hydropower availability, whereas Thailand has more solar and wind resources. If we modelled the Southeast Asia region as a single region in MUSE, we would lose information on the potential for trade between these two countries.
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It is also interesting to see energy flows between regions within a country, similar to the Southeast Asian example. For example, if a country has a large demand centre in the south of the country, but large energy resources in the north, it could be interesting to disaggregate this country into those two nodes.
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Similar to the previous mini-lecture, this disaggregation is largely dependent upon your requirements and the data available to you. There is no one solution for all areas, or even for the same area and different case studies. For example, one case study may only require the modelling of a country as a single region. Another case study, however, may require the modelling of that same country by many regions. It all depends on the question you are trying to answer and the data available to you. It must be noted, that a more disaggregated case study will take longer to run in MUSE.
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Summary
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In this mini-lecture we explored reasons for disaggregating a case study. We discovered that disaggregation (and aggregation) of regions depends largely on the data available to you and the questions you want to answer for your case study. However, we found out that the greater the disaggregation, the more detail the model may reveal, but the longer the model will take to run.
In this mini-lecture, we will explore the different ways that research can be communicated effectively.
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Learning objectives
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Understand how to communicate the results of your research to influence policy development
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Effective communication of research
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Throughout this course, we have explored the useful insights and analysis that can be provided by energy systems models. We have also explored the types of results that can lead to changes in the planning of energy systems, for example by taking a more holistic approach to investment planning.
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However, it is important that these results are communicated effectively to ensure that decision makers can fully understand the implications of these results. Effective communication also allows the methodology of the study to be better understood, which allows for the positives and limitations of the model to be explored.
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Presenting figures
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It is crucial to present figures in an understandable way. Figures are often the first thing that the audience will look at and try to understand. Figures can be used to convey the key results from your study in an impactful way. There are therefore some things that should be considered.
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The first of these is to design the figure with the target audience in mind. For example, if the audience is made of non-specialists, it may be sensible to ensure figures focus on the message without lots of technical jargon. For any audience, it is important that they understand the content of the figure, and so it is important to always include a figure caption, a legend (explaining colour coding and any symbols) and axis titles where appropriate. Finally, the colours chosen can have a large impact and so the colours should be chosen carefully with sufficient distinction between the colours.
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Common mistakes
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This section focuses on the commonly made mistakes when presenting figures in research. It can often be the case that figures are too confusing and contain too much data. This can often result in the message of the figure being unclear. It may be the case that by confusing your audience you reduce the impact of your research findings. Therefore, it is advisable to make figures as simple as possible to ensure that they are understandable.
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Other common mistakes include: - The use of inappropriate axis for graphs which can distort results - Lack of figure captions, axis titles, labels or legends
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Summary
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In this mini-lecture we explored the different ways that we can communicate our research for maximum impact and ways to make figures understandable to our target audience.
In this mini-lecture we will focus on effective oral communication of research.
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Learning objectives
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Implement tips for improved oral presentations to influence policy development
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Key features of presentations
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The key features of presentations are:
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Entry point: capture the audience’s attention
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Aim: focus on what you want to achieve with the presentation
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Structure: ensure consistency across the slides and tell a coherent story from beginning to end
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Audience: plan for your audience and their background
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Impact: identify key take-home points that the audience should remember
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Firstly, there is the entry point of the presentation. It is important to focus the audience’s attention. This ensures that they are interested in the presentation and understand what will be presented. This could take the form of presenting a question that you know will interest your audience, and telling them that by the end of the presentation they will know the answer.
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Throughout the presentation it is important to have the aim in mind. For example, you could be trying to increase engagement with a new department. For example, if you wish to demonstrate the advantages and disadvantages of building a new coal-power plant in a particular country, the figures and data you present should be focused on this particular situation, rather than providing information about scenarios that are not affected by a new coal power plant.
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The structure of the presentation can be tailored to your aim. It is important to have a clear beginning, middle and end. There should be consistency across the presentation to maximise the audience’s understanding.
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To further ensure that the audience understand and engage with the presentation, it should be designed with the audience’s backgrounds and motivations in mind (see more below).
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Finally, it is important to consider the impact of the presentation and identify key points or policy recommendations that you would like the audience to remember.
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Audiences
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It is important to understand the types of audience that you will be presenting to. For instance, they may be generalists or non-specialists. Or they could be scientists from different disciplines, or even scientists from the same discipline, but focusing on different topics.
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The presentation should be adapted depending on your audience in order to increase the audience’s understanding and engagement. Technical content, for example, can be explained in a simple and understandable manner if the audience contains non-specialists. If you think that your audience, on the other hand, will have technical expertise, you can spend less time on explaining technical content. The amount of technical detail you provide may also change: if you are speaking to a policymaker they may be more interested in the results and recommendations than the modelling process.
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The purpose of the presentation should be optimised throughout. For example, if you are aiming to create a partnership with a new department, the presentation should have a focus on the implications of your research for that department and the benefits of the proposed partnership for the audience.
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Summary
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In this mini-lecture we introduced some key tips for oral presentations. We explored why understanding your audience of importance, especially when introducing technical content. We also learnt that we can be strategic in our presentation planning and should optimise for the aims for which we want to achieve.
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This is the final lecture of the Agent-based energy systems modelling: MUSE course. After this lecture you should be in a good position to develop your own models through MUSE, which can then be used to assess the impact of different policy options.
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Thank you for engaging with this course, and we hope you have enjoyed the lectures, found them valuable, and find practical uses for MUSE in your research.
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Bibliography
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diff --git a/_deploy/lecture_07.zip b/_deploy/lecture_07.zip
deleted file mode 100644
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diff --git a/_deploy/lecture_08.zip b/_deploy/lecture_08.zip
deleted file mode 100644
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diff --git a/case-studies/hands-on-files/HO2/capacity_results.xlsx b/case-studies/hands-on-files/HO2/capacity_results.xlsx
deleted file mode 100644
index 111df56..0000000
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diff --git a/case-studies/hands-on-files/HO2/default.zip b/case-studies/hands-on-files/HO2/default.zip
deleted file mode 100644
index 2cf7149..0000000
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diff --git a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index fdbb2d2..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gassupply1,15.00000000000
-0,2025,R1,2020,gassupply1,15.00000000000
-0,2030,R1,2020,gassupply1,7.50000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index b130ea3..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gassupply1,2020,15.00000000000
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 9fd495c..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
-2,2035,R1,gassupply1,2030,13.12830000000
-2,2040,R1,gassupply1,2030,13.12830000000
-2,2045,R1,gassupply1,2030,13.12830000000
-2,2050,R1,gassupply1,2030,13.12830000000
-2,2064,R1,gassupply1,2030,13.12830000000
-2,2065,R1,gassupply1,2030,13.12830000000
-2,2069,R1,gassupply1,2030,13.12830000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 6aeac29..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2035,R1,2025,gassupply1,9.58580000000
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2035,R1,2030,gassupply1,13.12830000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index dbad909..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index 9012924..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index 98a9ae8..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2055,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2055,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/MCACapacity.csv b/case-studies/hands-on-files/HO2/default/Results/MCACapacity.csv
deleted file mode 100644
index 21b682f..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/MCACapacity.csv
+++ /dev/null
@@ -1,57 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity
-gasboiler,R1,R1,A1,residential,retrofit,2020,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2020,1.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2020,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2025,5.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2025,19.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2025,4.00000000000
-windturbine,R1,R1,A1,power,retrofit,2025,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2025,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,19.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,6.90000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,4.06670000000
-windturbine,R1,R1,A1,power,retrofit,2030,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,7.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,9.58580000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,6.90000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,25.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2035,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,25.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,16.09000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2040,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,6.38000000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,16.09000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,31.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2045,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2045,5.62000000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,13.12830000000
-heatpump,R1,R1,A1,residential,retrofit,2050,31.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2050,23.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2050,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2050,5.62000000000
-windturbine,R1,R1,A1,power,retrofit,2050,14.10000000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,13.12830000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/MCAPrices.csv b/case-studies/hands-on-files/HO2/default/Results/MCAPrices.csv
deleted file mode 100644
index 3f7c97d..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/MCAPrices.csv
+++ /dev/null
@@ -1,169 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,3.81600000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.22070000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,2.54400000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,2.14720000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,2.97130000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.88510000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,1.97120000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,0.50070000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.63520000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.86410000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,3.07850000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,0.50390000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.04230000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.73120000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,3.35160000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,0.36540000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.77910000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.79620000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,3.84390000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,0.39790000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,6.53190000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.76560000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,4.34650000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,0.35560000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2020.csv
deleted file mode 100644
index 62349ec..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,4.00000000000
-0,2030,R1,2020,gasCCGT,3.00000000000
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2025,R1,2020,windturbine,10.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2025.csv
deleted file mode 100644
index 61a7076..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,28 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasCCGT,2020,4.00000000000
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2025,R1,windturbine,2020,10.00000000000
-2,2030,R1,windturbine,2020,10.00000000000
-2,2035,R1,windturbine,2020,10.00000000000
-2,2040,R1,windturbine,2020,10.00000000000
-2,2045,R1,windturbine,2020,10.00000000000
-2,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2030.csv
deleted file mode 100644
index a4665a3..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,38 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2035,R1,gasCCGT,2030,5.33330000000
-2,2040,R1,gasCCGT,2030,5.33330000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-3,2030,R1,windturbine,2020,10.00000000000
-3,2035,R1,windturbine,2020,10.00000000000
-3,2040,R1,windturbine,2020,10.00000000000
-3,2045,R1,windturbine,2020,10.00000000000
-3,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2035.csv
deleted file mode 100644
index f6e6035..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,44 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2035,R1,2025,gasCCGT,4.06670000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2035,R1,2030,gasCCGT,5.33330000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2040.csv
deleted file mode 100644
index 4272c86..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
-9,2045,R1,2040,windturbine,5.62000000000
-9,2049,R1,2040,windturbine,5.62000000000
-9,2050,R1,2040,windturbine,5.62000000000
-9,2054,R1,2040,windturbine,5.62000000000
-9,2055,R1,2040,windturbine,5.62000000000
-9,2059,R1,2040,windturbine,5.62000000000
-9,2060,R1,2040,windturbine,5.62000000000
-9,2064,R1,2040,windturbine,5.62000000000
-9,2065,R1,2040,windturbine,5.62000000000
-9,2069,R1,2040,windturbine,5.62000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2045.csv
deleted file mode 100644
index cb08e79..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,55 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-6,2045,R1,windturbine,2020,10.00000000000
-6,2049,R1,windturbine,2020,10.00000000000
-9,2045,R1,windturbine,2035,6.38000000000
-9,2049,R1,windturbine,2035,6.38000000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2045,R1,windturbine,2040,5.62000000000
-10,2049,R1,windturbine,2040,5.62000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2050.csv
deleted file mode 100644
index 95dcafa..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,43 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index bcdf148..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gasboiler,2020,10.00000000000
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2025,R1,heatpump,2020,19.00000000000
-1,2030,R1,heatpump,2020,19.00000000000
-1,2034,R1,heatpump,2020,19.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 2eb0ec1..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-2,2025,R1,heatpump,2020,19.00000000000
-2,2030,R1,heatpump,2020,19.00000000000
-2,2034,R1,heatpump,2020,19.00000000000
-3,2030,R1,heatpump,2025,6.90000000000
-3,2034,R1,heatpump,2025,6.90000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index ca300fe..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-3,2030,R1,heatpump,2020,19.00000000000
-3,2034,R1,heatpump,2020,19.00000000000
-4,2030,R1,heatpump,2025,6.90000000000
-4,2034,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2025,6.90000000000
-4,2039,R1,heatpump,2025,6.90000000000
-5,2035,R1,heatpump,2030,25.00000000000
-5,2039,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2030,25.00000000000
-5,2044,R1,heatpump,2030,25.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 86d65e7..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasboiler,2025,4.10000000000
-0,2039,R1,gasboiler,2025,4.10000000000
-2,2040,R1,gasboiler,2035,0.91000000000
-2,2044,R1,gasboiler,2035,0.91000000000
-2,2045,R1,gasboiler,2035,0.91000000000
-2,2049,R1,gasboiler,2035,0.91000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2030,25.00000000000
-4,2039,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2030,25.00000000000
-4,2044,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2035,16.09000000000
-5,2044,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2035,16.09000000000
-5,2049,R1,heatpump,2035,16.09000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 268c65a..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2040,R1,gasboiler,2035,0.91000000000
-1,2044,R1,gasboiler,2035,0.91000000000
-1,2045,R1,gasboiler,2035,0.91000000000
-1,2049,R1,gasboiler,2035,0.91000000000
-3,2040,R1,heatpump,2030,25.00000000000
-3,2044,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2035,16.09000000000
-4,2044,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2035,16.09000000000
-4,2049,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2040,31.00000000000
-5,2049,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2040,31.00000000000
-5,2054,R1,heatpump,2040,31.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index 69ea873..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasboiler,2035,0.91000000000
-0,2049,R1,gasboiler,2035,0.91000000000
-3,2045,R1,heatpump,2035,16.09000000000
-3,2049,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2040,31.00000000000
-4,2049,R1,heatpump,2040,31.00000000000
-4,2050,R1,heatpump,2040,31.00000000000
-4,2054,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2045,23.00000000000
-5,2054,R1,heatpump,2045,23.00000000000
-5,2055,R1,heatpump,2045,23.00000000000
-5,2059,R1,heatpump,2045,23.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index 3cc1c8d..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-3,2050,R1,2040,heatpump,31.00000000000
-3,2054,R1,2040,heatpump,31.00000000000
-5,2050,R1,2045,heatpump,23.00000000000
-5,2054,R1,2045,heatpump,23.00000000000
-5,2055,R1,2045,heatpump,23.00000000000
-5,2059,R1,2045,heatpump,23.00000000000
-7,2055,R1,2050,heatpump,31.00000000000
-7,2059,R1,2050,heatpump,31.00000000000
-7,2060,R1,2050,heatpump,31.00000000000
-7,2064,R1,2050,heatpump,31.00000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2020.csv
deleted file mode 100644
index f851258..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,0,R1,gasboiler,2020,10.00000000000
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,1,R1,heatpump,2020,10.55560000000
-CO2f,2020,0,R1,gasboiler,2020,647.10000000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2025.csv
deleted file mode 100644
index 8ce7f70..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,2,R1,heatpump,2020,10.55560000000
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,2,R1,heatpump,2020,10.55560000000
-heat,2030,3,R1,heatpump,2025,3.83330000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2030.csv
deleted file mode 100644
index a6d2019..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,3,R1,heatpump,2020,10.55560000000
-heat,2030,4,R1,heatpump,2025,3.83330000000
-heat,2035,1,R1,gasboiler,2025,2.27780000000
-heat,2035,4,R1,heatpump,2025,3.83330000000
-heat,2035,5,R1,heatpump,2030,13.88890000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
-CO2f,2035,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 35ee123..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,0,R1,gasboiler,2025,2.27780000000
-heat,2035,3,R1,heatpump,2025,3.83330000000
-heat,2035,4,R1,heatpump,2030,13.88890000000
-heat,2040,2,R1,gasboiler,2035,0.50560000000
-heat,2040,4,R1,heatpump,2030,13.88890000000
-heat,2040,5,R1,heatpump,2035,8.93890000000
-CO2f,2035,0,R1,gasboiler,2025,147.39500000000
-CO2f,2040,2,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2040.csv
deleted file mode 100644
index 86233ac..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,1,R1,gasboiler,2035,0.50560000000
-heat,2040,3,R1,heatpump,2030,13.88890000000
-heat,2040,4,R1,heatpump,2035,8.93890000000
-heat,2045,1,R1,gasboiler,2035,0.50560000000
-heat,2045,4,R1,heatpump,2035,8.93890000000
-heat,2045,5,R1,heatpump,2040,17.22220000000
-CO2f,2040,1,R1,gasboiler,2035,32.71450000000
-CO2f,2045,1,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 492d52f..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,0,R1,gasboiler,2035,0.50560000000
-heat,2045,3,R1,heatpump,2035,8.93890000000
-heat,2045,4,R1,heatpump,2040,17.22220000000
-heat,2050,4,R1,heatpump,2040,17.22220000000
-heat,2050,5,R1,heatpump,2045,12.77780000000
-CO2f,2045,0,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2050.csv
deleted file mode 100644
index d89c6fe..0000000
--- a/case-studies/hands-on-files/HO2/default/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-commodity,year,asset,region,installed,technology,supply
-heat,2050,3,R1,2040,heatpump,17.22220000000
-heat,2050,5,R1,2045,heatpump,12.77780000000
-heat,2055,5,R1,2045,heatpump,12.77780000000
-heat,2055,7,R1,2050,heatpump,17.22220000000
diff --git a/case-studies/hands-on-files/HO2/default/input/BaseYearExport.csv b/case-studies/hands-on-files/HO2/default/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO2/default/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/input/BaseYearImport.csv b/case-studies/hands-on-files/HO2/default/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO2/default/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO2/default/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO2/default/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO2/default/input/Projections.csv b/case-studies/hands-on-files/HO2/default/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO2/default/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO2/default/settings.toml b/case-studies/hands-on-files/HO2/default/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO2/default/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO2/default/technodata/Agents.csv b/case-studies/hands-on-files/HO2/default/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO2/default/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO2/default/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO2/default/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO2/default/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO2/default/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO2/default/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO2/default/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO2/default/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO2/default/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO2/default/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO2/default/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO2/default/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO2/default/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO2/default/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO2/default/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO2/default/technodata/residential/CommOut.csv
deleted file mode 100644
index 7c84018..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO2/default/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO2/default/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO2/default/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO2/default/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO2/default_final.zip b/case-studies/hands-on-files/HO2/default_final.zip
deleted file mode 100644
index e452872..0000000
Binary files a/case-studies/hands-on-files/HO2/default_final.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index d16d187..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gassupply1,15.00000000000
-0,2025,R1,2020,gassupply1,15.00000000000
-0,2030,R1,2020,gassupply1,7.50000000000
-0,2035,R1,2020,gassupply1,0.01000000000
-0,2040,R1,2020,gassupply1,0.01000000000
-0,2045,R1,2020,gassupply1,0.01000000000
-0,2050,R1,2020,gassupply1,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index bca572c..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2025,R1,2020,gassupply1,15.00000000000
-0,2030,R1,2020,gassupply1,7.50000000000
-0,2035,R1,2020,gassupply1,0.01000000000
-0,2040,R1,2020,gassupply1,0.01000000000
-0,2045,R1,2020,gassupply1,0.01000000000
-0,2050,R1,2020,gassupply1,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 7d5a053..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gassupply1,7.50000000000
-0,2035,R1,2020,gassupply1,0.01000000000
-0,2040,R1,2020,gassupply1,0.01000000000
-0,2045,R1,2020,gassupply1,0.01000000000
-0,2050,R1,2020,gassupply1,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 441d0d4..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gassupply1,0.01000000000
-0,2040,R1,2020,gassupply1,0.01000000000
-0,2045,R1,2020,gassupply1,0.01000000000
-0,2050,R1,2020,gassupply1,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index e75f441..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gassupply1,0.01000000000
-0,2045,R1,2020,gassupply1,0.01000000000
-0,2050,R1,2020,gassupply1,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index b598757..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2045,R1,2020,gassupply1,0.01000000000
-0,2050,R1,2020,gassupply1,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index 306d825..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gassupply1,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/MCACapacity.csv b/case-studies/hands-on-files/HO2/default_final/Results/MCACapacity.csv
deleted file mode 100644
index e7dc3ae..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/MCACapacity.csv
+++ /dev/null
@@ -1,77 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity
-electric_stove,R1,R1,A1,residential,retrofit,2020,10.00000000000
-gas_stove,R1,R1,A1,residential,retrofit,2020,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2020,10.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2020,0.01000000000
-gasCCGT,R1,R1,A1,power,retrofit,2020,1.00000000000
-windturbine,R1,R1,A1,power,retrofit,2020,0.01000000000
-gassupply1,R1,R1,A1,gas,retrofit,2020,15.00000000000
-electric_stove,R1,R1,A1,residential,retrofit,2025,18.99000000000
-gas_stove,R1,R1,A1,residential,retrofit,2025,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2025,8.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2025,6.01050000000
-gasCCGT,R1,R1,A1,power,retrofit,2025,1.00000000000
-windturbine,R1,R1,A1,power,retrofit,2025,0.01000000000
-gassupply1,R1,R1,A1,gas,retrofit,2025,15.00000000000
-electric_stove,R1,R1,A1,residential,retrofit,2030,14.00000000000
-electric_stove,R1,R1,A1,residential,retrofit,2030,5.99000000000
-gas_stove,R1,R1,A1,residential,retrofit,2030,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,3.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,2.89500000000
-heatpump,R1,R1,A1,residential,retrofit,2030,6.01050000000
-heatpump,R1,R1,A1,residential,retrofit,2030,6.30050000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,0.01000000000
-windturbine,R1,R1,A1,power,retrofit,2030,0.01000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,7.50000000000
-electric_stove,R1,R1,A1,residential,retrofit,2035,0.01000000000
-electric_stove,R1,R1,A1,residential,retrofit,2035,5.99000000000
-electric_stove,R1,R1,A1,residential,retrofit,2035,14.99000000000
-gas_stove,R1,R1,A1,residential,retrofit,2035,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,2.89500000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,1.19680000000
-heatpump,R1,R1,A1,residential,retrofit,2035,0.01000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,6.30050000000
-heatpump,R1,R1,A1,residential,retrofit,2035,9.07720000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,0.01000000000
-windturbine,R1,R1,A1,power,retrofit,2035,0.01000000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,0.01000000000
-electric_stove,R1,R1,A1,residential,retrofit,2040,0.01000000000
-electric_stove,R1,R1,A1,residential,retrofit,2040,14.99000000000
-electric_stove,R1,R1,A1,residential,retrofit,2040,6.99000000000
-gas_stove,R1,R1,A1,residential,retrofit,2040,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,1.19680000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,1.24040000000
-heatpump,R1,R1,A1,residential,retrofit,2040,0.01000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,9.07720000000
-heatpump,R1,R1,A1,residential,retrofit,2040,9.24280000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,0.01000000000
-windturbine,R1,R1,A1,power,retrofit,2040,0.01000000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,0.01000000000
-electric_stove,R1,R1,A1,residential,retrofit,2045,0.01000000000
-electric_stove,R1,R1,A1,residential,retrofit,2045,6.99000000000
-electric_stove,R1,R1,A1,residential,retrofit,2045,15.99000000000
-gas_stove,R1,R1,A1,residential,retrofit,2045,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,1.24040000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,0.51290000000
-heatpump,R1,R1,A1,residential,retrofit,2045,0.01000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,9.24280000000
-heatpump,R1,R1,A1,residential,retrofit,2045,12.72620000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,0.01000000000
-windturbine,R1,R1,A1,power,retrofit,2045,0.01000000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,0.01000000000
-electric_stove,R1,R1,A1,residential,retrofit,2050,0.01000000000
-electric_stove,R1,R1,A1,residential,retrofit,2050,15.99000000000
-electric_stove,R1,R1,A1,residential,retrofit,2050,7.99000000000
-gas_stove,R1,R1,A1,residential,retrofit,2050,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2050,0.01000000000
-gasboiler,R1,R1,A1,residential,retrofit,2050,0.51290000000
-gasboiler,R1,R1,A1,residential,retrofit,2050,0.47230000000
-heatpump,R1,R1,A1,residential,retrofit,2050,0.01000000000
-heatpump,R1,R1,A1,residential,retrofit,2050,12.72620000000
-heatpump,R1,R1,A1,residential,retrofit,2050,10.13790000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,0.01000000000
-windturbine,R1,R1,A1,power,retrofit,2050,0.01000000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/MCAPrices.csv b/case-studies/hands-on-files/HO2/default_final/Results/MCAPrices.csv
deleted file mode 100644
index b5e91df..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/MCAPrices.csv
+++ /dev/null
@@ -1,211 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",cook,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",cook,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",cook,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",cook,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",cook,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",cook,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,21.93520000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,8.10640000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,0.92950000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",cook,R1,2.59100000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,21.93520000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,8.10640000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,1.39430000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",cook,R1,4.81180000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,21.93520000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,8.10640000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.92950000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",cook,R1,2.59100000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,21.93520000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,8.10640000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.39430000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",cook,R1,3.70140000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,21.93520000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,8.10640000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,2.78860000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",cook,R1,4.81180000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,21.93520000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,8.10640000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,1.85910000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",cook,R1,7.03260000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,26.50930000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,9.06020000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,1.07690000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",cook,R1,3.29000000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,26.50930000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,9.06020000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,1.61540000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",cook,R1,5.75750000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,26.50930000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,9.06020000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.07690000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",cook,R1,3.29000000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,26.50930000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,9.06020000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.61540000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",cook,R1,4.52380000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,26.50930000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,9.06020000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.23080000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",cook,R1,5.75750000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,26.50930000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,9.06020000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,2.15380000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",cook,R1,8.22500000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,26.51850000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,9.21910000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,1.08370000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",cook,R1,3.42830000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,26.51850000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,9.21910000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,1.62560000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",cook,R1,5.71390000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,26.51850000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,9.21910000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.08370000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",cook,R1,3.42830000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,26.51850000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,9.21910000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.62560000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",cook,R1,4.57110000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,26.51850000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,9.21910000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.25120000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",cook,R1,5.71390000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,26.51850000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,9.21910000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,2.16740000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",cook,R1,7.99950000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,23.85190000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,9.37800000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,0.99230000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",cook,R1,3.19120000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,23.85190000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,9.37800000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,1.48840000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",cook,R1,5.10600000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,23.85190000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,9.37800000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.99230000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",cook,R1,3.19120000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,23.85190000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,9.37800000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.48840000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",cook,R1,4.14860000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,23.85190000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,9.37800000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,2.97680000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",cook,R1,5.10600000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,23.85190000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,9.37800000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,1.98450000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",cook,R1,7.02070000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,23.97220000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,9.19380000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,0.98990000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",cook,R1,3.30040000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,23.97220000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,9.19380000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,1.48490000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",cook,R1,5.10060000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,23.97220000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,9.19380000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.98990000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",cook,R1,3.30040000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,23.97220000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,9.19380000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.48490000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",cook,R1,4.20050000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,23.97220000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,9.19380000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,2.96970000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",cook,R1,5.10060000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,23.97220000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,9.19380000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,1.97980000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",cook,R1,6.90090000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,24.06480000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,9.00960000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,0.98950000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'night')",cook,R1,3.39530000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,24.06480000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,9.00960000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,1.48430000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",cook,R1,5.09290000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,24.06480000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,9.00960000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.98950000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",cook,R1,3.39530000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,24.06480000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,9.00960000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.48430000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",cook,R1,4.24410000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,24.06480000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,9.00960000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,2.96850000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",cook,R1,5.09290000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,24.06480000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,9.00960000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,1.97900000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",cook,R1,6.79060000000,2050
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2020.csv
deleted file mode 100644
index e1f6ed4..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,1.00000000000
-0,2030,R1,2020,gasCCGT,0.01000000000
-0,2035,R1,2020,gasCCGT,0.01000000000
-0,2040,R1,2020,gasCCGT,0.01000000000
-0,2045,R1,2020,gasCCGT,0.01000000000
-0,2050,R1,2020,gasCCGT,0.01000000000
-1,2020,R1,2020,windturbine,0.01000000000
-1,2025,R1,2020,windturbine,0.01000000000
-1,2030,R1,2020,windturbine,0.01000000000
-1,2035,R1,2020,windturbine,0.01000000000
-1,2040,R1,2020,windturbine,0.01000000000
-1,2045,R1,2020,windturbine,0.01000000000
-1,2050,R1,2020,windturbine,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2025.csv
deleted file mode 100644
index c042731..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2025,R1,2020,gasCCGT,1.00000000000
-0,2030,R1,2020,gasCCGT,0.01000000000
-0,2035,R1,2020,gasCCGT,0.01000000000
-0,2040,R1,2020,gasCCGT,0.01000000000
-0,2045,R1,2020,gasCCGT,0.01000000000
-0,2050,R1,2020,gasCCGT,0.01000000000
-1,2025,R1,2020,windturbine,0.01000000000
-1,2030,R1,2020,windturbine,0.01000000000
-1,2035,R1,2020,windturbine,0.01000000000
-1,2040,R1,2020,windturbine,0.01000000000
-1,2045,R1,2020,windturbine,0.01000000000
-1,2050,R1,2020,windturbine,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2030.csv
deleted file mode 100644
index b48c33c..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gasCCGT,0.01000000000
-0,2035,R1,2020,gasCCGT,0.01000000000
-0,2040,R1,2020,gasCCGT,0.01000000000
-0,2045,R1,2020,gasCCGT,0.01000000000
-0,2050,R1,2020,gasCCGT,0.01000000000
-1,2030,R1,2020,windturbine,0.01000000000
-1,2035,R1,2020,windturbine,0.01000000000
-1,2040,R1,2020,windturbine,0.01000000000
-1,2045,R1,2020,windturbine,0.01000000000
-1,2050,R1,2020,windturbine,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2035.csv
deleted file mode 100644
index f15f7ac..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gasCCGT,0.01000000000
-0,2040,R1,2020,gasCCGT,0.01000000000
-0,2045,R1,2020,gasCCGT,0.01000000000
-0,2050,R1,2020,gasCCGT,0.01000000000
-1,2035,R1,2020,windturbine,0.01000000000
-1,2040,R1,2020,windturbine,0.01000000000
-1,2045,R1,2020,windturbine,0.01000000000
-1,2050,R1,2020,windturbine,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2040.csv
deleted file mode 100644
index 66845f8..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,0.01000000000
-0,2045,R1,2020,gasCCGT,0.01000000000
-0,2050,R1,2020,gasCCGT,0.01000000000
-1,2040,R1,2020,windturbine,0.01000000000
-1,2045,R1,2020,windturbine,0.01000000000
-1,2050,R1,2020,windturbine,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2045.csv
deleted file mode 100644
index 306c84c..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2045,R1,2020,gasCCGT,0.01000000000
-0,2050,R1,2020,gasCCGT,0.01000000000
-1,2045,R1,2020,windturbine,0.01000000000
-1,2050,R1,2020,windturbine,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2050.csv
deleted file mode 100644
index 1679924..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gasCCGT,0.01000000000
-1,2050,R1,2020,windturbine,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index d2cde5e..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,33 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,electric_stove,2020,10.00000000000
-0,2025,R1,electric_stove,2020,18.99000000000
-0,2030,R1,electric_stove,2020,14.00000000000
-0,2034,R1,electric_stove,2020,14.00000000000
-0,2035,R1,electric_stove,2020,0.01000000000
-0,2040,R1,electric_stove,2020,0.01000000000
-0,2045,R1,electric_stove,2020,0.01000000000
-0,2050,R1,electric_stove,2020,0.01000000000
-1,2020,R1,gas_stove,2020,0.01000000000
-1,2025,R1,gas_stove,2020,0.01000000000
-1,2030,R1,gas_stove,2020,0.01000000000
-1,2034,R1,gas_stove,2020,0.01000000000
-1,2035,R1,gas_stove,2020,0.01000000000
-1,2040,R1,gas_stove,2020,0.01000000000
-1,2045,R1,gas_stove,2020,0.01000000000
-1,2050,R1,gas_stove,2020,0.01000000000
-2,2020,R1,gasboiler,2020,10.00000000000
-2,2025,R1,gasboiler,2020,8.00000000000
-2,2030,R1,gasboiler,2020,3.01000000000
-2,2034,R1,gasboiler,2020,3.01000000000
-2,2035,R1,gasboiler,2020,0.01000000000
-2,2040,R1,gasboiler,2020,0.01000000000
-2,2045,R1,gasboiler,2020,0.01000000000
-2,2050,R1,gasboiler,2020,0.01000000000
-3,2020,R1,heatpump,2020,0.01000000000
-3,2025,R1,heatpump,2020,6.01050000000
-3,2030,R1,heatpump,2020,6.01050000000
-3,2034,R1,heatpump,2020,6.01050000000
-3,2035,R1,heatpump,2020,0.01000000000
-3,2040,R1,heatpump,2020,0.01000000000
-3,2045,R1,heatpump,2020,0.01000000000
-3,2050,R1,heatpump,2020,0.01000000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 441c927..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,45 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,electric_stove,2020,18.99000000000
-0,2030,R1,electric_stove,2020,14.00000000000
-0,2034,R1,electric_stove,2020,14.00000000000
-0,2035,R1,electric_stove,2020,0.01000000000
-0,2039,R1,electric_stove,2020,0.01000000000
-0,2040,R1,electric_stove,2020,0.01000000000
-0,2045,R1,electric_stove,2020,0.01000000000
-0,2050,R1,electric_stove,2020,0.01000000000
-1,2030,R1,electric_stove,2025,5.99000000000
-1,2034,R1,electric_stove,2025,5.99000000000
-1,2035,R1,electric_stove,2025,5.99000000000
-1,2039,R1,electric_stove,2025,5.99000000000
-2,2025,R1,gas_stove,2020,0.01000000000
-2,2030,R1,gas_stove,2020,0.01000000000
-2,2034,R1,gas_stove,2020,0.01000000000
-2,2035,R1,gas_stove,2020,0.01000000000
-2,2039,R1,gas_stove,2020,0.01000000000
-2,2040,R1,gas_stove,2020,0.01000000000
-2,2045,R1,gas_stove,2020,0.01000000000
-2,2050,R1,gas_stove,2020,0.01000000000
-3,2025,R1,gasboiler,2020,8.00000000000
-3,2030,R1,gasboiler,2020,3.01000000000
-3,2034,R1,gasboiler,2020,3.01000000000
-3,2035,R1,gasboiler,2020,0.01000000000
-3,2039,R1,gasboiler,2020,0.01000000000
-3,2040,R1,gasboiler,2020,0.01000000000
-3,2045,R1,gasboiler,2020,0.01000000000
-3,2050,R1,gasboiler,2020,0.01000000000
-4,2030,R1,gasboiler,2025,2.89500000000
-4,2034,R1,gasboiler,2025,2.89500000000
-4,2035,R1,gasboiler,2025,2.89500000000
-4,2039,R1,gasboiler,2025,2.89500000000
-5,2025,R1,heatpump,2020,6.01050000000
-5,2030,R1,heatpump,2020,6.01050000000
-5,2034,R1,heatpump,2020,6.01050000000
-5,2035,R1,heatpump,2020,0.01000000000
-5,2039,R1,heatpump,2020,0.01000000000
-5,2040,R1,heatpump,2020,0.01000000000
-5,2045,R1,heatpump,2020,0.01000000000
-5,2050,R1,heatpump,2020,0.01000000000
-6,2030,R1,heatpump,2025,6.30050000000
-6,2034,R1,heatpump,2025,6.30050000000
-6,2035,R1,heatpump,2025,6.30050000000
-6,2039,R1,heatpump,2025,6.30050000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index 63ef2c1..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,57 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,electric_stove,2020,14.00000000000
-0,2034,R1,electric_stove,2020,14.00000000000
-0,2035,R1,electric_stove,2020,0.01000000000
-0,2039,R1,electric_stove,2020,0.01000000000
-0,2040,R1,electric_stove,2020,0.01000000000
-0,2044,R1,electric_stove,2020,0.01000000000
-0,2045,R1,electric_stove,2020,0.01000000000
-0,2050,R1,electric_stove,2020,0.01000000000
-1,2030,R1,electric_stove,2025,5.99000000000
-1,2034,R1,electric_stove,2025,5.99000000000
-1,2035,R1,electric_stove,2025,5.99000000000
-1,2039,R1,electric_stove,2025,5.99000000000
-2,2035,R1,electric_stove,2030,14.99000000000
-2,2039,R1,electric_stove,2030,14.99000000000
-2,2040,R1,electric_stove,2030,14.99000000000
-2,2044,R1,electric_stove,2030,14.99000000000
-3,2030,R1,gas_stove,2020,0.01000000000
-3,2034,R1,gas_stove,2020,0.01000000000
-3,2035,R1,gas_stove,2020,0.01000000000
-3,2039,R1,gas_stove,2020,0.01000000000
-3,2040,R1,gas_stove,2020,0.01000000000
-3,2044,R1,gas_stove,2020,0.01000000000
-3,2045,R1,gas_stove,2020,0.01000000000
-3,2050,R1,gas_stove,2020,0.01000000000
-4,2030,R1,gasboiler,2020,3.01000000000
-4,2034,R1,gasboiler,2020,3.01000000000
-4,2035,R1,gasboiler,2020,0.01000000000
-4,2039,R1,gasboiler,2020,0.01000000000
-4,2040,R1,gasboiler,2020,0.01000000000
-4,2044,R1,gasboiler,2020,0.01000000000
-4,2045,R1,gasboiler,2020,0.01000000000
-4,2050,R1,gasboiler,2020,0.01000000000
-5,2030,R1,gasboiler,2025,2.89500000000
-5,2034,R1,gasboiler,2025,2.89500000000
-5,2035,R1,gasboiler,2025,2.89500000000
-5,2039,R1,gasboiler,2025,2.89500000000
-6,2035,R1,gasboiler,2030,1.19680000000
-6,2039,R1,gasboiler,2030,1.19680000000
-6,2040,R1,gasboiler,2030,1.19680000000
-6,2044,R1,gasboiler,2030,1.19680000000
-7,2030,R1,heatpump,2020,6.01050000000
-7,2034,R1,heatpump,2020,6.01050000000
-7,2035,R1,heatpump,2020,0.01000000000
-7,2039,R1,heatpump,2020,0.01000000000
-7,2040,R1,heatpump,2020,0.01000000000
-7,2044,R1,heatpump,2020,0.01000000000
-7,2045,R1,heatpump,2020,0.01000000000
-7,2050,R1,heatpump,2020,0.01000000000
-8,2030,R1,heatpump,2025,6.30050000000
-8,2034,R1,heatpump,2025,6.30050000000
-8,2035,R1,heatpump,2025,6.30050000000
-8,2039,R1,heatpump,2025,6.30050000000
-9,2035,R1,heatpump,2030,9.07720000000
-9,2039,R1,heatpump,2030,9.07720000000
-9,2040,R1,heatpump,2030,9.07720000000
-9,2044,R1,heatpump,2030,9.07720000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 7ea24b5..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,59 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,electric_stove,2020,0.01000000000
-0,2039,R1,electric_stove,2020,0.01000000000
-0,2040,R1,electric_stove,2020,0.01000000000
-0,2044,R1,electric_stove,2020,0.01000000000
-0,2045,R1,electric_stove,2020,0.01000000000
-0,2049,R1,electric_stove,2020,0.01000000000
-0,2050,R1,electric_stove,2020,0.01000000000
-1,2035,R1,electric_stove,2025,5.99000000000
-1,2039,R1,electric_stove,2025,5.99000000000
-2,2035,R1,electric_stove,2030,14.99000000000
-2,2039,R1,electric_stove,2030,14.99000000000
-2,2040,R1,electric_stove,2030,14.99000000000
-2,2044,R1,electric_stove,2030,14.99000000000
-3,2040,R1,electric_stove,2035,6.99000000000
-3,2044,R1,electric_stove,2035,6.99000000000
-3,2045,R1,electric_stove,2035,6.99000000000
-3,2049,R1,electric_stove,2035,6.99000000000
-4,2035,R1,gas_stove,2020,0.01000000000
-4,2039,R1,gas_stove,2020,0.01000000000
-4,2040,R1,gas_stove,2020,0.01000000000
-4,2044,R1,gas_stove,2020,0.01000000000
-4,2045,R1,gas_stove,2020,0.01000000000
-4,2049,R1,gas_stove,2020,0.01000000000
-4,2050,R1,gas_stove,2020,0.01000000000
-5,2035,R1,gasboiler,2020,0.01000000000
-5,2039,R1,gasboiler,2020,0.01000000000
-5,2040,R1,gasboiler,2020,0.01000000000
-5,2044,R1,gasboiler,2020,0.01000000000
-5,2045,R1,gasboiler,2020,0.01000000000
-5,2049,R1,gasboiler,2020,0.01000000000
-5,2050,R1,gasboiler,2020,0.01000000000
-6,2035,R1,gasboiler,2025,2.89500000000
-6,2039,R1,gasboiler,2025,2.89500000000
-7,2035,R1,gasboiler,2030,1.19680000000
-7,2039,R1,gasboiler,2030,1.19680000000
-7,2040,R1,gasboiler,2030,1.19680000000
-7,2044,R1,gasboiler,2030,1.19680000000
-8,2040,R1,gasboiler,2035,1.24040000000
-8,2044,R1,gasboiler,2035,1.24040000000
-8,2045,R1,gasboiler,2035,1.24040000000
-8,2049,R1,gasboiler,2035,1.24040000000
-9,2035,R1,heatpump,2020,0.01000000000
-9,2039,R1,heatpump,2020,0.01000000000
-9,2040,R1,heatpump,2020,0.01000000000
-9,2044,R1,heatpump,2020,0.01000000000
-9,2045,R1,heatpump,2020,0.01000000000
-9,2049,R1,heatpump,2020,0.01000000000
-9,2050,R1,heatpump,2020,0.01000000000
-10,2035,R1,heatpump,2025,6.30050000000
-10,2039,R1,heatpump,2025,6.30050000000
-11,2035,R1,heatpump,2030,9.07720000000
-11,2039,R1,heatpump,2030,9.07720000000
-11,2040,R1,heatpump,2030,9.07720000000
-11,2044,R1,heatpump,2030,9.07720000000
-12,2040,R1,heatpump,2035,9.24280000000
-12,2044,R1,heatpump,2035,9.24280000000
-12,2045,R1,heatpump,2035,9.24280000000
-12,2049,R1,heatpump,2035,9.24280000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 951eedd..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,51 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2040,R1,electric_stove,2020,0.01000000000
-0,2044,R1,electric_stove,2020,0.01000000000
-0,2045,R1,electric_stove,2020,0.01000000000
-0,2049,R1,electric_stove,2020,0.01000000000
-0,2050,R1,electric_stove,2020,0.01000000000
-1,2040,R1,electric_stove,2030,14.99000000000
-1,2044,R1,electric_stove,2030,14.99000000000
-2,2040,R1,electric_stove,2035,6.99000000000
-2,2044,R1,electric_stove,2035,6.99000000000
-2,2045,R1,electric_stove,2035,6.99000000000
-2,2049,R1,electric_stove,2035,6.99000000000
-3,2045,R1,electric_stove,2040,15.99000000000
-3,2049,R1,electric_stove,2040,15.99000000000
-3,2050,R1,electric_stove,2040,15.99000000000
-3,2054,R1,electric_stove,2040,15.99000000000
-4,2040,R1,gas_stove,2020,0.01000000000
-4,2044,R1,gas_stove,2020,0.01000000000
-4,2045,R1,gas_stove,2020,0.01000000000
-4,2049,R1,gas_stove,2020,0.01000000000
-4,2050,R1,gas_stove,2020,0.01000000000
-5,2040,R1,gasboiler,2020,0.01000000000
-5,2044,R1,gasboiler,2020,0.01000000000
-5,2045,R1,gasboiler,2020,0.01000000000
-5,2049,R1,gasboiler,2020,0.01000000000
-5,2050,R1,gasboiler,2020,0.01000000000
-6,2040,R1,gasboiler,2030,1.19680000000
-6,2044,R1,gasboiler,2030,1.19680000000
-7,2040,R1,gasboiler,2035,1.24040000000
-7,2044,R1,gasboiler,2035,1.24040000000
-7,2045,R1,gasboiler,2035,1.24040000000
-7,2049,R1,gasboiler,2035,1.24040000000
-8,2045,R1,gasboiler,2040,0.51290000000
-8,2049,R1,gasboiler,2040,0.51290000000
-8,2050,R1,gasboiler,2040,0.51290000000
-8,2054,R1,gasboiler,2040,0.51290000000
-9,2040,R1,heatpump,2020,0.01000000000
-9,2044,R1,heatpump,2020,0.01000000000
-9,2045,R1,heatpump,2020,0.01000000000
-9,2049,R1,heatpump,2020,0.01000000000
-9,2050,R1,heatpump,2020,0.01000000000
-10,2040,R1,heatpump,2030,9.07720000000
-10,2044,R1,heatpump,2030,9.07720000000
-11,2040,R1,heatpump,2035,9.24280000000
-11,2044,R1,heatpump,2035,9.24280000000
-11,2045,R1,heatpump,2035,9.24280000000
-11,2049,R1,heatpump,2035,9.24280000000
-12,2045,R1,heatpump,2040,12.72620000000
-12,2049,R1,heatpump,2040,12.72620000000
-12,2050,R1,heatpump,2040,12.72620000000
-12,2054,R1,heatpump,2040,12.72620000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index 8e40c34..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,43 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,electric_stove,2020,0.01000000000
-0,2049,R1,electric_stove,2020,0.01000000000
-0,2050,R1,electric_stove,2020,0.01000000000
-1,2045,R1,electric_stove,2035,6.99000000000
-1,2049,R1,electric_stove,2035,6.99000000000
-2,2045,R1,electric_stove,2040,15.99000000000
-2,2049,R1,electric_stove,2040,15.99000000000
-2,2050,R1,electric_stove,2040,15.99000000000
-2,2054,R1,electric_stove,2040,15.99000000000
-3,2050,R1,electric_stove,2045,7.99000000000
-3,2054,R1,electric_stove,2045,7.99000000000
-3,2055,R1,electric_stove,2045,7.99000000000
-3,2059,R1,electric_stove,2045,7.99000000000
-4,2045,R1,gas_stove,2020,0.01000000000
-4,2049,R1,gas_stove,2020,0.01000000000
-4,2050,R1,gas_stove,2020,0.01000000000
-5,2045,R1,gasboiler,2020,0.01000000000
-5,2049,R1,gasboiler,2020,0.01000000000
-5,2050,R1,gasboiler,2020,0.01000000000
-6,2045,R1,gasboiler,2035,1.24040000000
-6,2049,R1,gasboiler,2035,1.24040000000
-7,2045,R1,gasboiler,2040,0.51290000000
-7,2049,R1,gasboiler,2040,0.51290000000
-7,2050,R1,gasboiler,2040,0.51290000000
-7,2054,R1,gasboiler,2040,0.51290000000
-8,2050,R1,gasboiler,2045,0.47230000000
-8,2054,R1,gasboiler,2045,0.47230000000
-8,2055,R1,gasboiler,2045,0.47230000000
-8,2059,R1,gasboiler,2045,0.47230000000
-9,2045,R1,heatpump,2020,0.01000000000
-9,2049,R1,heatpump,2020,0.01000000000
-9,2050,R1,heatpump,2020,0.01000000000
-10,2045,R1,heatpump,2035,9.24280000000
-10,2049,R1,heatpump,2035,9.24280000000
-11,2045,R1,heatpump,2040,12.72620000000
-11,2049,R1,heatpump,2040,12.72620000000
-11,2050,R1,heatpump,2040,12.72620000000
-11,2054,R1,heatpump,2040,12.72620000000
-12,2050,R1,heatpump,2045,10.13790000000
-12,2054,R1,heatpump,2045,10.13790000000
-12,2055,R1,heatpump,2045,10.13790000000
-12,2059,R1,heatpump,2045,10.13790000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index 352390d..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,35 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,electric_stove,0.01000000000
-1,2050,R1,2020,gas_stove,0.01000000000
-2,2050,R1,2020,gasboiler,0.01000000000
-3,2050,R1,2020,heatpump,0.01000000000
-7,2050,R1,2040,electric_stove,15.99000000000
-7,2054,R1,2040,electric_stove,15.99000000000
-8,2050,R1,2040,gasboiler,0.51290000000
-8,2054,R1,2040,gasboiler,0.51290000000
-9,2050,R1,2040,heatpump,12.72620000000
-9,2054,R1,2040,heatpump,12.72620000000
-10,2050,R1,2045,electric_stove,7.99000000000
-10,2054,R1,2045,electric_stove,7.99000000000
-10,2055,R1,2045,electric_stove,7.99000000000
-10,2059,R1,2045,electric_stove,7.99000000000
-11,2050,R1,2045,gasboiler,0.47230000000
-11,2054,R1,2045,gasboiler,0.47230000000
-11,2055,R1,2045,gasboiler,0.47230000000
-11,2059,R1,2045,gasboiler,0.47230000000
-12,2050,R1,2045,heatpump,10.13790000000
-12,2054,R1,2045,heatpump,10.13790000000
-12,2055,R1,2045,heatpump,10.13790000000
-12,2059,R1,2045,heatpump,10.13790000000
-13,2055,R1,2050,electric_stove,16.01000000000
-13,2059,R1,2050,electric_stove,16.01000000000
-13,2060,R1,2050,electric_stove,16.01000000000
-13,2064,R1,2050,electric_stove,16.01000000000
-14,2055,R1,2050,gasboiler,0.16790000000
-14,2059,R1,2050,gasboiler,0.16790000000
-14,2060,R1,2050,gasboiler,0.16790000000
-14,2064,R1,2050,gasboiler,0.16790000000
-15,2055,R1,2050,heatpump,3.75590000000
-15,2059,R1,2050,heatpump,3.75590000000
-15,2060,R1,2050,heatpump,3.75590000000
-15,2064,R1,2050,heatpump,3.75590000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2020.csv
deleted file mode 100644
index 716ec3b..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,2,R1,gasboiler,2020,9.99000000000
-heat,2020,3,R1,heatpump,2020,0.01000000000
-heat,2025,2,R1,gasboiler,2020,7.61330000000
-heat,2025,3,R1,heatpump,2020,5.72000000000
-cook,2020,0,R1,electric_stove,2020,10.00000000000
-cook,2020,1,R1,gas_stove,2020,0.01000000000
-cook,2025,0,R1,electric_stove,2020,11.49390000000
-cook,2025,1,R1,gas_stove,2020,0.00610000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2025.csv
deleted file mode 100644
index 0e7a321..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,3,R1,gasboiler,2020,7.61330000000
-heat,2025,5,R1,heatpump,2020,5.72000000000
-heat,2030,3,R1,gasboiler,2020,2.75400000000
-heat,2030,4,R1,gasboiler,2025,2.64880000000
-heat,2030,5,R1,heatpump,2020,5.49930000000
-heat,2030,6,R1,heatpump,2025,5.76460000000
-cook,2025,0,R1,electric_stove,2020,11.49390000000
-cook,2025,2,R1,gas_stove,2020,0.00610000000
-cook,2030,0,R1,electric_stove,2020,8.75000000000
-cook,2030,1,R1,electric_stove,2025,3.74380000000
-cook,2030,2,R1,gas_stove,2020,0.00620000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2030.csv
deleted file mode 100644
index dab02a5..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,18 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,4,R1,gasboiler,2020,2.75400000000
-heat,2030,5,R1,gasboiler,2025,2.64880000000
-heat,2030,7,R1,heatpump,2020,5.49930000000
-heat,2030,8,R1,heatpump,2025,5.76460000000
-heat,2035,4,R1,gasboiler,2020,0.01000000000
-heat,2035,5,R1,gasboiler,2025,2.89500000000
-heat,2035,6,R1,gasboiler,2030,1.19680000000
-heat,2035,7,R1,heatpump,2020,0.01000000000
-heat,2035,8,R1,heatpump,2025,6.30050000000
-heat,2035,9,R1,heatpump,2030,9.07720000000
-cook,2030,0,R1,electric_stove,2020,8.75000000000
-cook,2030,1,R1,electric_stove,2025,3.74380000000
-cook,2030,3,R1,gas_stove,2020,0.00620000000
-cook,2035,0,R1,electric_stove,2020,0.00640000000
-cook,2035,1,R1,electric_stove,2025,3.85070000000
-cook,2035,2,R1,electric_stove,2030,9.63640000000
-cook,2035,3,R1,gas_stove,2020,0.00640000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 24fe44c..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,5,R1,gasboiler,2020,0.01000000000
-heat,2035,6,R1,gasboiler,2025,2.89500000000
-heat,2035,7,R1,gasboiler,2030,1.19680000000
-heat,2035,9,R1,heatpump,2020,0.01000000000
-heat,2035,10,R1,heatpump,2025,6.30050000000
-heat,2035,11,R1,heatpump,2030,9.07720000000
-heat,2040,5,R1,gasboiler,2020,0.01000000000
-heat,2040,7,R1,gasboiler,2030,1.19680000000
-heat,2040,8,R1,gasboiler,2035,1.24040000000
-heat,2040,9,R1,heatpump,2020,0.01000000000
-heat,2040,11,R1,heatpump,2030,9.07720000000
-heat,2040,12,R1,heatpump,2035,9.24280000000
-cook,2035,0,R1,electric_stove,2020,0.00640000000
-cook,2035,1,R1,electric_stove,2025,3.85070000000
-cook,2035,2,R1,electric_stove,2030,9.63640000000
-cook,2035,4,R1,gas_stove,2020,0.00640000000
-cook,2040,0,R1,electric_stove,2020,0.00660000000
-cook,2040,2,R1,electric_stove,2030,9.87980000000
-cook,2040,3,R1,electric_stove,2035,4.60700000000
-cook,2040,4,R1,gas_stove,2020,0.00660000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2040.csv
deleted file mode 100644
index 07728d0..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,5,R1,gasboiler,2020,0.01000000000
-heat,2040,6,R1,gasboiler,2030,1.19680000000
-heat,2040,7,R1,gasboiler,2035,1.24040000000
-heat,2040,9,R1,heatpump,2020,0.01000000000
-heat,2040,10,R1,heatpump,2030,9.07720000000
-heat,2040,11,R1,heatpump,2035,9.24280000000
-heat,2045,5,R1,gasboiler,2020,0.01000000000
-heat,2045,7,R1,gasboiler,2035,1.24040000000
-heat,2045,8,R1,gasboiler,2040,0.51290000000
-heat,2045,9,R1,heatpump,2020,0.01000000000
-heat,2045,11,R1,heatpump,2035,9.24280000000
-heat,2045,12,R1,heatpump,2040,12.72620000000
-cook,2040,0,R1,electric_stove,2020,0.00660000000
-cook,2040,1,R1,electric_stove,2030,9.87980000000
-cook,2040,2,R1,electric_stove,2035,4.60700000000
-cook,2040,4,R1,gas_stove,2020,0.00660000000
-cook,2045,0,R1,electric_stove,2020,0.00670000000
-cook,2045,2,R1,electric_stove,2035,4.71070000000
-cook,2045,3,R1,electric_stove,2040,10.77590000000
-cook,2045,4,R1,gas_stove,2020,0.00670000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2045.csv
deleted file mode 100644
index b0f4744..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,5,R1,gasboiler,2020,0.01000000000
-heat,2045,6,R1,gasboiler,2035,1.24040000000
-heat,2045,7,R1,gasboiler,2040,0.51290000000
-heat,2045,9,R1,heatpump,2020,0.01000000000
-heat,2045,10,R1,heatpump,2035,9.24280000000
-heat,2045,11,R1,heatpump,2040,12.72620000000
-heat,2050,5,R1,gasboiler,2020,0.01000000000
-heat,2050,7,R1,gasboiler,2040,0.51290000000
-heat,2050,8,R1,gasboiler,2045,0.47230000000
-heat,2050,9,R1,heatpump,2020,0.01000000000
-heat,2050,11,R1,heatpump,2040,12.72620000000
-heat,2050,12,R1,heatpump,2045,10.13790000000
-cook,2045,0,R1,electric_stove,2020,0.00670000000
-cook,2045,1,R1,electric_stove,2035,4.71070000000
-cook,2045,2,R1,electric_stove,2040,10.77590000000
-cook,2045,4,R1,gas_stove,2020,0.00670000000
-cook,2050,0,R1,electric_stove,2020,0.00690000000
-cook,2050,2,R1,electric_stove,2040,10.99310000000
-cook,2050,3,R1,electric_stove,2045,5.49310000000
-cook,2050,4,R1,gas_stove,2020,0.00690000000
diff --git a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2050.csv
deleted file mode 100644
index ef861f2..0000000
--- a/case-studies/hands-on-files/HO2/default_final/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-commodity,year,asset,region,installed,technology,supply
-heat,2050,2,R1,2020,gasboiler,0.01000000000
-heat,2050,3,R1,2020,heatpump,0.01000000000
-heat,2050,8,R1,2040,gasboiler,0.51290000000
-heat,2050,9,R1,2040,heatpump,12.72620000000
-heat,2050,11,R1,2045,gasboiler,0.47230000000
-heat,2050,12,R1,2045,heatpump,10.13790000000
-heat,2055,11,R1,2045,gasboiler,0.47230000000
-heat,2055,12,R1,2045,heatpump,10.13790000000
-heat,2055,14,R1,2050,gasboiler,0.16790000000
-heat,2055,15,R1,2050,heatpump,3.75590000000
-cook,2050,0,R1,2020,electric_stove,0.00690000000
-cook,2050,1,R1,2020,gas_stove,0.00690000000
-cook,2050,7,R1,2040,electric_stove,10.99310000000
-cook,2050,10,R1,2045,electric_stove,5.49310000000
-cook,2055,10,R1,2045,electric_stove,5.49310000000
-cook,2055,13,R1,2050,electric_stove,11.00690000000
diff --git a/case-studies/hands-on-files/HO2/default_final/input/BaseYearExport.csv b/case-studies/hands-on-files/HO2/default_final/input/BaseYearExport.csv
deleted file mode 100644
index ab2e1c2..0000000
--- a/case-studies/hands-on-files/HO2/default_final/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,PJ,PJ,PJ,kt,PJ,PJ
-R1,Exports,2010,0,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/input/BaseYearImport.csv b/case-studies/hands-on-files/HO2/default_final/input/BaseYearImport.csv
deleted file mode 100644
index 7c9e5c1..0000000
--- a/case-studies/hands-on-files/HO2/default_final/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,PJ,PJ,PJ,kt,PJ,PJ
-R1,Imports,2010,0,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO2/default_final/input/GlobalCommodities.csv
deleted file mode 100644
index 0a75fc0..0000000
--- a/case-studies/hands-on-files/HO2/default_final/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
-Cook,Energy,Ccook,0,1,PJ
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/input/Projections.csv b/case-studies/hands-on-files/HO2/default_final/input/Projections.csv
deleted file mode 100644
index aed50d1..0000000
--- a/case-studies/hands-on-files/HO2/default_final/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0,100
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0,100
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0,100
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0,100
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0,100
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0,100
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0,100
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0,100
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0,100
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0,100
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0,100
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0,100
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0,100
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0,100
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0,100
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0,100
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0,100
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0,100
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0,100
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/settings.toml b/case-studies/hands-on-files/HO2/default_final/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO2/default_final/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/Agents.csv b/case-studies/hands-on-files/HO2/default_final/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO2/default_final/technodata/gas/CommIn.csv
deleted file mode 100644
index 8f194b9..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO2/default_final/technodata/gas/CommOut.csv
deleted file mode 100644
index e6fd5e2..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO2/default_final/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 55817f5..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0.01,0.01,0.01,0.01
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO2/default_final/technodata/gas/Technodata.csv
deleted file mode 100644
index 57a5f60..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,100,5,500,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO2/default_final/technodata/power/CommIn.csv
deleted file mode 100644
index 59b9cc6..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO2/default_final/technodata/power/CommOut.csv
deleted file mode 100644
index bff0ecd..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0,0
-windturbine,R1,2020,fixed,1,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO2/default_final/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 55a9645..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0.01,0.01,0.01,0.01,0.01
-windturbine,R1,PJ/y,0.01,0.01,0.01,0.01,0.01,0.01,0.01
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO2/default_final/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO2/default_final/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 95de4da..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind,cook
-0,R1,gasboiler,1,0,0,1,0,0,1
-1,R1,gasboiler,2,0,0,1.5,0,0,2
-2,R1,gasboiler,3,0,0,1,0,0,1
-3,R1,gasboiler,4,0,0,1.5,0,0,1.5
-4,R1,gasboiler,5,0,0,3,0,0,2
-5,R1,gasboiler,6,0,0,2,0,0,3
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO2/default_final/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index 853fd36..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind,cook
-0,R1,gasboiler,1,0,0,3,0,0,2
-1,R1,gasboiler,2,0,0,4.5,0,0,3
-2,R1,gasboiler,3,0,0,3,0,0,2
-3,R1,gasboiler,4,0,0,4.5,0,0,2.5
-4,R1,gasboiler,5,0,0,9,0,0,3
-5,R1,gasboiler,6,0,0,6,0,0,4
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO2/default_final/technodata/residential/CommIn.csv
deleted file mode 100644
index e1e02d7..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0,0
-electric_stove,R1,2020,fixed,1.16,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,1.16,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO2/default_final/technodata/residential/CommOut.csv
deleted file mode 100644
index cadd2ed..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,cook
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,
-gasboiler,R1,2020,fixed,0,0,1,64.71,0,0
-heatpump,R1,2020,fixed,0,0,1,0,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0,1
-gas_stove,R1,2020,fixed,0,0,0,64.71,0,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO2/default_final/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index dc4d254..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0.01,0.01,0.01,0.01,0.01
-heatpump,R1,PJ/y,0.01,0.01,0.01,0.01,0.01,0.01,0.01
-electric_stove,R1,PJ/y,10,5,0.01,0.01,0.01,0.01,0.01
-gas_stove,R1,PJ/y,0.01,0.01,0.01,0.01,0.01,0.01,0.01
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO2/default_final/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO2/default_final/technodata/residential/Technodata.csv
deleted file mode 100644
index 53c1a3b..0000000
--- a/case-studies/hands-on-files/HO2/default_final/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
-gas_stove,R1,2020,fixed,3.8,1,0,1,0,1,100,20,120,10,1,0.00000189,86,0.1,energy,gas,cook,1
-electric_stove,R1,2020,fixed,8.866667,1,0,1,0,1,100,20,120,10,1,0.00000189,86,0.1,energy,electricity,cook,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO3/TechnodataTimeslices.csv b/case-studies/hands-on-files/HO3/TechnodataTimeslices.csv
deleted file mode 100644
index 0b0629b..0000000
--- a/case-studies/hands-on-files/HO3/TechnodataTimeslices.csv
+++ /dev/null
@@ -1,14 +0,0 @@
-ProcessName,RegionName,Time,ObjSort,month,day,hour,UtilizationFactor,MinimumServiceFactor
-Unit,-,Year,-,-,-,-,-,-
-gasCCGT,R1,2020,upper,all-year,all-week,night,1,1
-gasCCGT,R1,2020,upper,all-year,all-week,morning,1,2
-gasCCGT,R1,2020,upper,all-year,all-week,afternoon,1,3
-gasCCGT,R1,2020,upper,all-year,all-week,early-peak,1,4
-gasCCGT,R1,2020,upper,all-year,all-week,late-peak,1,5
-gasCCGT,R1,2020,upper,all-year,all-week,evening,1,6
-windturbine,R1,2020,upper,all-year,all-week,night,1,1
-windturbine,R1,2020,upper,all-year,all-week,morning,1,1
-windturbine,R1,2020,upper,all-year,all-week,afternoon,1,1
-windturbine,R1,2020,upper,all-year,all-week,early-peak,1,1
-windturbine,R1,2020,upper,all-year,all-week,late-peak,1,1
-windturbine,R1,2020,upper,all-year,all-week,evening,1,1
diff --git a/case-studies/hands-on-files/HO3/default.zip b/case-studies/hands-on-files/HO3/default.zip
deleted file mode 100644
index 54855ca..0000000
Binary files a/case-studies/hands-on-files/HO3/default.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO3/default/input/BaseYearExport.csv b/case-studies/hands-on-files/HO3/default/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO3/default/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/input/BaseYearImport.csv b/case-studies/hands-on-files/HO3/default/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO3/default/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO3/default/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO3/default/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO3/default/input/Projections.csv b/case-studies/hands-on-files/HO3/default/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO3/default/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO3/default/settings.toml b/case-studies/hands-on-files/HO3/default/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO3/default/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO3/default/technodata/Agents.csv b/case-studies/hands-on-files/HO3/default/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO3/default/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO3/default/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO3/default/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO3/default/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO3/default/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO3/default/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO3/default/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO3/default/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO3/default/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO3/default/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO3/default/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO3/default/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO3/default/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO3/default/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO3/default/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO3/default/technodata/residential/CommOut.csv
deleted file mode 100644
index 7c84018..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO3/default/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO3/default/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO3/default/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO3/default/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index fdbb2d2..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gassupply1,15.00000000000
-0,2025,R1,2020,gassupply1,15.00000000000
-0,2030,R1,2020,gassupply1,7.50000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index b130ea3..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gassupply1,2020,15.00000000000
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 9fd495c..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
-2,2035,R1,gassupply1,2030,13.12830000000
-2,2040,R1,gassupply1,2030,13.12830000000
-2,2045,R1,gassupply1,2030,13.12830000000
-2,2050,R1,gassupply1,2030,13.12830000000
-2,2064,R1,gassupply1,2030,13.12830000000
-2,2065,R1,gassupply1,2030,13.12830000000
-2,2069,R1,gassupply1,2030,13.12830000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 6aeac29..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2035,R1,2025,gassupply1,9.58580000000
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2035,R1,2030,gassupply1,13.12830000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index dbad909..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index 9012924..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index 98a9ae8..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2055,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2055,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/MCACapacity.csv b/case-studies/hands-on-files/HO3/default_final/Results/MCACapacity.csv
deleted file mode 100644
index 21b682f..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/MCACapacity.csv
+++ /dev/null
@@ -1,57 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity
-gasboiler,R1,R1,A1,residential,retrofit,2020,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2020,1.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2020,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2025,5.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2025,19.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2025,4.00000000000
-windturbine,R1,R1,A1,power,retrofit,2025,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2025,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,19.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,6.90000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,4.06670000000
-windturbine,R1,R1,A1,power,retrofit,2030,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,7.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,9.58580000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,6.90000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,25.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2035,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,25.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,16.09000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2040,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,6.38000000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,16.09000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,31.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2045,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2045,5.62000000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,13.12830000000
-heatpump,R1,R1,A1,residential,retrofit,2050,31.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2050,23.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2050,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2050,5.62000000000
-windturbine,R1,R1,A1,power,retrofit,2050,14.10000000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,13.12830000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/MCAPrices.csv b/case-studies/hands-on-files/HO3/default_final/Results/MCAPrices.csv
deleted file mode 100644
index 3f7c97d..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/MCAPrices.csv
+++ /dev/null
@@ -1,169 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,3.81600000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.22070000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,2.54400000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,2.14720000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,2.97130000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.88510000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,1.97120000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,0.50070000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.63520000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.86410000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,3.07850000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,0.50390000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.04230000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.73120000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,3.35160000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,0.36540000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.77910000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.79620000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,3.84390000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,0.39790000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,6.53190000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.76560000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,4.34650000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,0.35560000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2020.csv
deleted file mode 100644
index 62349ec..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,4.00000000000
-0,2030,R1,2020,gasCCGT,3.00000000000
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2025,R1,2020,windturbine,10.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2025.csv
deleted file mode 100644
index 61a7076..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,28 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasCCGT,2020,4.00000000000
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2025,R1,windturbine,2020,10.00000000000
-2,2030,R1,windturbine,2020,10.00000000000
-2,2035,R1,windturbine,2020,10.00000000000
-2,2040,R1,windturbine,2020,10.00000000000
-2,2045,R1,windturbine,2020,10.00000000000
-2,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2030.csv
deleted file mode 100644
index a4665a3..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,38 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2035,R1,gasCCGT,2030,5.33330000000
-2,2040,R1,gasCCGT,2030,5.33330000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-3,2030,R1,windturbine,2020,10.00000000000
-3,2035,R1,windturbine,2020,10.00000000000
-3,2040,R1,windturbine,2020,10.00000000000
-3,2045,R1,windturbine,2020,10.00000000000
-3,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2035.csv
deleted file mode 100644
index f6e6035..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,44 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2035,R1,2025,gasCCGT,4.06670000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2035,R1,2030,gasCCGT,5.33330000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2040.csv
deleted file mode 100644
index 4272c86..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
-9,2045,R1,2040,windturbine,5.62000000000
-9,2049,R1,2040,windturbine,5.62000000000
-9,2050,R1,2040,windturbine,5.62000000000
-9,2054,R1,2040,windturbine,5.62000000000
-9,2055,R1,2040,windturbine,5.62000000000
-9,2059,R1,2040,windturbine,5.62000000000
-9,2060,R1,2040,windturbine,5.62000000000
-9,2064,R1,2040,windturbine,5.62000000000
-9,2065,R1,2040,windturbine,5.62000000000
-9,2069,R1,2040,windturbine,5.62000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2045.csv
deleted file mode 100644
index cb08e79..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,55 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-6,2045,R1,windturbine,2020,10.00000000000
-6,2049,R1,windturbine,2020,10.00000000000
-9,2045,R1,windturbine,2035,6.38000000000
-9,2049,R1,windturbine,2035,6.38000000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2045,R1,windturbine,2040,5.62000000000
-10,2049,R1,windturbine,2040,5.62000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2050.csv
deleted file mode 100644
index 95dcafa..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,43 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index bcdf148..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gasboiler,2020,10.00000000000
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2025,R1,heatpump,2020,19.00000000000
-1,2030,R1,heatpump,2020,19.00000000000
-1,2034,R1,heatpump,2020,19.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 2eb0ec1..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-2,2025,R1,heatpump,2020,19.00000000000
-2,2030,R1,heatpump,2020,19.00000000000
-2,2034,R1,heatpump,2020,19.00000000000
-3,2030,R1,heatpump,2025,6.90000000000
-3,2034,R1,heatpump,2025,6.90000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index ca300fe..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-3,2030,R1,heatpump,2020,19.00000000000
-3,2034,R1,heatpump,2020,19.00000000000
-4,2030,R1,heatpump,2025,6.90000000000
-4,2034,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2025,6.90000000000
-4,2039,R1,heatpump,2025,6.90000000000
-5,2035,R1,heatpump,2030,25.00000000000
-5,2039,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2030,25.00000000000
-5,2044,R1,heatpump,2030,25.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 86d65e7..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasboiler,2025,4.10000000000
-0,2039,R1,gasboiler,2025,4.10000000000
-2,2040,R1,gasboiler,2035,0.91000000000
-2,2044,R1,gasboiler,2035,0.91000000000
-2,2045,R1,gasboiler,2035,0.91000000000
-2,2049,R1,gasboiler,2035,0.91000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2030,25.00000000000
-4,2039,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2030,25.00000000000
-4,2044,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2035,16.09000000000
-5,2044,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2035,16.09000000000
-5,2049,R1,heatpump,2035,16.09000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 268c65a..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2040,R1,gasboiler,2035,0.91000000000
-1,2044,R1,gasboiler,2035,0.91000000000
-1,2045,R1,gasboiler,2035,0.91000000000
-1,2049,R1,gasboiler,2035,0.91000000000
-3,2040,R1,heatpump,2030,25.00000000000
-3,2044,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2035,16.09000000000
-4,2044,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2035,16.09000000000
-4,2049,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2040,31.00000000000
-5,2049,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2040,31.00000000000
-5,2054,R1,heatpump,2040,31.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index 69ea873..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasboiler,2035,0.91000000000
-0,2049,R1,gasboiler,2035,0.91000000000
-3,2045,R1,heatpump,2035,16.09000000000
-3,2049,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2040,31.00000000000
-4,2049,R1,heatpump,2040,31.00000000000
-4,2050,R1,heatpump,2040,31.00000000000
-4,2054,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2045,23.00000000000
-5,2054,R1,heatpump,2045,23.00000000000
-5,2055,R1,heatpump,2045,23.00000000000
-5,2059,R1,heatpump,2045,23.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index 3cc1c8d..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-3,2050,R1,2040,heatpump,31.00000000000
-3,2054,R1,2040,heatpump,31.00000000000
-5,2050,R1,2045,heatpump,23.00000000000
-5,2054,R1,2045,heatpump,23.00000000000
-5,2055,R1,2045,heatpump,23.00000000000
-5,2059,R1,2045,heatpump,23.00000000000
-7,2055,R1,2050,heatpump,31.00000000000
-7,2059,R1,2050,heatpump,31.00000000000
-7,2060,R1,2050,heatpump,31.00000000000
-7,2064,R1,2050,heatpump,31.00000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2020.csv
deleted file mode 100644
index f851258..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,0,R1,gasboiler,2020,10.00000000000
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,1,R1,heatpump,2020,10.55560000000
-CO2f,2020,0,R1,gasboiler,2020,647.10000000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2025.csv
deleted file mode 100644
index 8ce7f70..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,2,R1,heatpump,2020,10.55560000000
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,2,R1,heatpump,2020,10.55560000000
-heat,2030,3,R1,heatpump,2025,3.83330000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2030.csv
deleted file mode 100644
index a6d2019..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,3,R1,heatpump,2020,10.55560000000
-heat,2030,4,R1,heatpump,2025,3.83330000000
-heat,2035,1,R1,gasboiler,2025,2.27780000000
-heat,2035,4,R1,heatpump,2025,3.83330000000
-heat,2035,5,R1,heatpump,2030,13.88890000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
-CO2f,2035,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 35ee123..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,0,R1,gasboiler,2025,2.27780000000
-heat,2035,3,R1,heatpump,2025,3.83330000000
-heat,2035,4,R1,heatpump,2030,13.88890000000
-heat,2040,2,R1,gasboiler,2035,0.50560000000
-heat,2040,4,R1,heatpump,2030,13.88890000000
-heat,2040,5,R1,heatpump,2035,8.93890000000
-CO2f,2035,0,R1,gasboiler,2025,147.39500000000
-CO2f,2040,2,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2040.csv
deleted file mode 100644
index 86233ac..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,1,R1,gasboiler,2035,0.50560000000
-heat,2040,3,R1,heatpump,2030,13.88890000000
-heat,2040,4,R1,heatpump,2035,8.93890000000
-heat,2045,1,R1,gasboiler,2035,0.50560000000
-heat,2045,4,R1,heatpump,2035,8.93890000000
-heat,2045,5,R1,heatpump,2040,17.22220000000
-CO2f,2040,1,R1,gasboiler,2035,32.71450000000
-CO2f,2045,1,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 492d52f..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,0,R1,gasboiler,2035,0.50560000000
-heat,2045,3,R1,heatpump,2035,8.93890000000
-heat,2045,4,R1,heatpump,2040,17.22220000000
-heat,2050,4,R1,heatpump,2040,17.22220000000
-heat,2050,5,R1,heatpump,2045,12.77780000000
-CO2f,2045,0,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2050.csv
deleted file mode 100644
index d89c6fe..0000000
--- a/case-studies/hands-on-files/HO3/default_final/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-commodity,year,asset,region,installed,technology,supply
-heat,2050,3,R1,2040,heatpump,17.22220000000
-heat,2050,5,R1,2045,heatpump,12.77780000000
-heat,2055,5,R1,2045,heatpump,12.77780000000
-heat,2055,7,R1,2050,heatpump,17.22220000000
diff --git a/case-studies/hands-on-files/HO3/default_final/input/BaseYearExport.csv b/case-studies/hands-on-files/HO3/default_final/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO3/default_final/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/input/BaseYearImport.csv b/case-studies/hands-on-files/HO3/default_final/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO3/default_final/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO3/default_final/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO3/default_final/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO3/default_final/input/Projections.csv b/case-studies/hands-on-files/HO3/default_final/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO3/default_final/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO3/default_final/settings.toml b/case-studies/hands-on-files/HO3/default_final/settings.toml
deleted file mode 100644
index 80de9a5..0000000
--- a/case-studies/hands-on-files/HO3/default_final/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "scipy" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "scipy"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "scipy"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/Agents.csv b/case-studies/hands-on-files/HO3/default_final/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO3/default_final/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO3/default_final/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO3/default_final/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO3/default_final/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO3/default_final/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO3/default_final/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO3/default_final/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO3/default_final/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/power/TechnodataTimeslices.csv b/case-studies/hands-on-files/HO3/default_final/technodata/power/TechnodataTimeslices.csv
deleted file mode 100644
index def18d5..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/power/TechnodataTimeslices.csv
+++ /dev/null
@@ -1,14 +0,0 @@
-ProcessName,RegionName,Time,ObjSort,month,day,hour,UtilizationFactor,MinimumServiceFactor
-Unit,-,Year,-,-,-,-,-,-
-gasCCGT,R1,2020,upper,all-year,all-week,night,1,1
-gasCCGT,R1,2020,upper,all-year,all-week,morning,1,2
-gasCCGT,R1,2020,upper,all-year,all-week,afternoon,1,3
-gasCCGT,R1,2020,upper,all-year,all-week,early-peak,1,4
-gasCCGT,R1,2020,upper,all-year,all-week,late-peak,1,5
-gasCCGT,R1,2020,upper,all-year,all-week,evening,1,6
-windturbine,R1,2020,upper,all-year,all-week,night,1,1
-windturbine,R1,2020,upper,all-year,all-week,morning,0.5,1
-windturbine,R1,2020,upper,all-year,all-week,afternoon,0.5,1
-windturbine,R1,2020,upper,all-year,all-week,early-peak,1,1
-windturbine,R1,2020,upper,all-year,all-week,late-peak,1,1
-windturbine,R1,2020,upper,all-year,all-week,evening,1,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO3/default_final/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO3/default_final/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO3/default_final/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO3/default_final/technodata/residential/CommOut.csv
deleted file mode 100644
index f32c59a..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO3/default_final/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO3/default_final/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO3/default_final/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO3/default_final/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO4/capacity_results.xlsx b/case-studies/hands-on-files/HO4/capacity_results.xlsx
deleted file mode 100644
index 732f8ac..0000000
Binary files a/case-studies/hands-on-files/HO4/capacity_results.xlsx and /dev/null differ
diff --git a/case-studies/hands-on-files/HO4/default.zip b/case-studies/hands-on-files/HO4/default.zip
deleted file mode 100644
index f96bb8d..0000000
Binary files a/case-studies/hands-on-files/HO4/default.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index fdbb2d2..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gassupply1,15.00000000000
-0,2025,R1,2020,gassupply1,15.00000000000
-0,2030,R1,2020,gassupply1,7.50000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index b130ea3..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gassupply1,2020,15.00000000000
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 9fd495c..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
-2,2035,R1,gassupply1,2030,13.12830000000
-2,2040,R1,gassupply1,2030,13.12830000000
-2,2045,R1,gassupply1,2030,13.12830000000
-2,2050,R1,gassupply1,2030,13.12830000000
-2,2064,R1,gassupply1,2030,13.12830000000
-2,2065,R1,gassupply1,2030,13.12830000000
-2,2069,R1,gassupply1,2030,13.12830000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 6aeac29..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2035,R1,2025,gassupply1,9.58580000000
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2035,R1,2030,gassupply1,13.12830000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index dbad909..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index 9012924..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index 98a9ae8..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2055,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2055,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/MCACapacity.csv b/case-studies/hands-on-files/HO4/default/Results/MCACapacity.csv
deleted file mode 100644
index 21b682f..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/MCACapacity.csv
+++ /dev/null
@@ -1,57 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity
-gasboiler,R1,R1,A1,residential,retrofit,2020,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2020,1.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2020,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2025,5.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2025,19.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2025,4.00000000000
-windturbine,R1,R1,A1,power,retrofit,2025,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2025,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,19.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,6.90000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,4.06670000000
-windturbine,R1,R1,A1,power,retrofit,2030,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,7.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,9.58580000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,6.90000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,25.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2035,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,25.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,16.09000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2040,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,6.38000000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,16.09000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,31.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2045,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2045,5.62000000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,13.12830000000
-heatpump,R1,R1,A1,residential,retrofit,2050,31.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2050,23.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2050,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2050,5.62000000000
-windturbine,R1,R1,A1,power,retrofit,2050,14.10000000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,13.12830000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/MCAPrices.csv b/case-studies/hands-on-files/HO4/default/Results/MCAPrices.csv
deleted file mode 100644
index 3f7c97d..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/MCAPrices.csv
+++ /dev/null
@@ -1,169 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,3.81600000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.22070000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,2.54400000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,2.14720000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,2.97130000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.88510000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,1.97120000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,0.50070000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.63520000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.86410000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,3.07850000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,0.50390000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.04230000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.73120000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,3.35160000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,0.36540000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.77910000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.79620000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,3.84390000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,0.39790000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,6.53190000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.76560000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,4.34650000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,0.35560000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2020.csv
deleted file mode 100644
index 62349ec..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,4.00000000000
-0,2030,R1,2020,gasCCGT,3.00000000000
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2025,R1,2020,windturbine,10.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2025.csv
deleted file mode 100644
index 61a7076..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,28 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasCCGT,2020,4.00000000000
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2025,R1,windturbine,2020,10.00000000000
-2,2030,R1,windturbine,2020,10.00000000000
-2,2035,R1,windturbine,2020,10.00000000000
-2,2040,R1,windturbine,2020,10.00000000000
-2,2045,R1,windturbine,2020,10.00000000000
-2,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2030.csv
deleted file mode 100644
index a4665a3..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,38 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2035,R1,gasCCGT,2030,5.33330000000
-2,2040,R1,gasCCGT,2030,5.33330000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-3,2030,R1,windturbine,2020,10.00000000000
-3,2035,R1,windturbine,2020,10.00000000000
-3,2040,R1,windturbine,2020,10.00000000000
-3,2045,R1,windturbine,2020,10.00000000000
-3,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2035.csv
deleted file mode 100644
index f6e6035..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,44 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2035,R1,2025,gasCCGT,4.06670000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2035,R1,2030,gasCCGT,5.33330000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2040.csv
deleted file mode 100644
index 4272c86..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
-9,2045,R1,2040,windturbine,5.62000000000
-9,2049,R1,2040,windturbine,5.62000000000
-9,2050,R1,2040,windturbine,5.62000000000
-9,2054,R1,2040,windturbine,5.62000000000
-9,2055,R1,2040,windturbine,5.62000000000
-9,2059,R1,2040,windturbine,5.62000000000
-9,2060,R1,2040,windturbine,5.62000000000
-9,2064,R1,2040,windturbine,5.62000000000
-9,2065,R1,2040,windturbine,5.62000000000
-9,2069,R1,2040,windturbine,5.62000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2045.csv
deleted file mode 100644
index cb08e79..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,55 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-6,2045,R1,windturbine,2020,10.00000000000
-6,2049,R1,windturbine,2020,10.00000000000
-9,2045,R1,windturbine,2035,6.38000000000
-9,2049,R1,windturbine,2035,6.38000000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2045,R1,windturbine,2040,5.62000000000
-10,2049,R1,windturbine,2040,5.62000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2050.csv
deleted file mode 100644
index 95dcafa..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,43 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index bcdf148..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gasboiler,2020,10.00000000000
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2025,R1,heatpump,2020,19.00000000000
-1,2030,R1,heatpump,2020,19.00000000000
-1,2034,R1,heatpump,2020,19.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 2eb0ec1..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-2,2025,R1,heatpump,2020,19.00000000000
-2,2030,R1,heatpump,2020,19.00000000000
-2,2034,R1,heatpump,2020,19.00000000000
-3,2030,R1,heatpump,2025,6.90000000000
-3,2034,R1,heatpump,2025,6.90000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index ca300fe..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-3,2030,R1,heatpump,2020,19.00000000000
-3,2034,R1,heatpump,2020,19.00000000000
-4,2030,R1,heatpump,2025,6.90000000000
-4,2034,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2025,6.90000000000
-4,2039,R1,heatpump,2025,6.90000000000
-5,2035,R1,heatpump,2030,25.00000000000
-5,2039,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2030,25.00000000000
-5,2044,R1,heatpump,2030,25.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 86d65e7..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasboiler,2025,4.10000000000
-0,2039,R1,gasboiler,2025,4.10000000000
-2,2040,R1,gasboiler,2035,0.91000000000
-2,2044,R1,gasboiler,2035,0.91000000000
-2,2045,R1,gasboiler,2035,0.91000000000
-2,2049,R1,gasboiler,2035,0.91000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2030,25.00000000000
-4,2039,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2030,25.00000000000
-4,2044,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2035,16.09000000000
-5,2044,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2035,16.09000000000
-5,2049,R1,heatpump,2035,16.09000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 268c65a..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2040,R1,gasboiler,2035,0.91000000000
-1,2044,R1,gasboiler,2035,0.91000000000
-1,2045,R1,gasboiler,2035,0.91000000000
-1,2049,R1,gasboiler,2035,0.91000000000
-3,2040,R1,heatpump,2030,25.00000000000
-3,2044,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2035,16.09000000000
-4,2044,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2035,16.09000000000
-4,2049,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2040,31.00000000000
-5,2049,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2040,31.00000000000
-5,2054,R1,heatpump,2040,31.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index 69ea873..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasboiler,2035,0.91000000000
-0,2049,R1,gasboiler,2035,0.91000000000
-3,2045,R1,heatpump,2035,16.09000000000
-3,2049,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2040,31.00000000000
-4,2049,R1,heatpump,2040,31.00000000000
-4,2050,R1,heatpump,2040,31.00000000000
-4,2054,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2045,23.00000000000
-5,2054,R1,heatpump,2045,23.00000000000
-5,2055,R1,heatpump,2045,23.00000000000
-5,2059,R1,heatpump,2045,23.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index 3cc1c8d..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-3,2050,R1,2040,heatpump,31.00000000000
-3,2054,R1,2040,heatpump,31.00000000000
-5,2050,R1,2045,heatpump,23.00000000000
-5,2054,R1,2045,heatpump,23.00000000000
-5,2055,R1,2045,heatpump,23.00000000000
-5,2059,R1,2045,heatpump,23.00000000000
-7,2055,R1,2050,heatpump,31.00000000000
-7,2059,R1,2050,heatpump,31.00000000000
-7,2060,R1,2050,heatpump,31.00000000000
-7,2064,R1,2050,heatpump,31.00000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2020.csv
deleted file mode 100644
index f851258..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,0,R1,gasboiler,2020,10.00000000000
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,1,R1,heatpump,2020,10.55560000000
-CO2f,2020,0,R1,gasboiler,2020,647.10000000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2025.csv
deleted file mode 100644
index 8ce7f70..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,2,R1,heatpump,2020,10.55560000000
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,2,R1,heatpump,2020,10.55560000000
-heat,2030,3,R1,heatpump,2025,3.83330000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2030.csv
deleted file mode 100644
index a6d2019..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,3,R1,heatpump,2020,10.55560000000
-heat,2030,4,R1,heatpump,2025,3.83330000000
-heat,2035,1,R1,gasboiler,2025,2.27780000000
-heat,2035,4,R1,heatpump,2025,3.83330000000
-heat,2035,5,R1,heatpump,2030,13.88890000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
-CO2f,2035,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 35ee123..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,0,R1,gasboiler,2025,2.27780000000
-heat,2035,3,R1,heatpump,2025,3.83330000000
-heat,2035,4,R1,heatpump,2030,13.88890000000
-heat,2040,2,R1,gasboiler,2035,0.50560000000
-heat,2040,4,R1,heatpump,2030,13.88890000000
-heat,2040,5,R1,heatpump,2035,8.93890000000
-CO2f,2035,0,R1,gasboiler,2025,147.39500000000
-CO2f,2040,2,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2040.csv
deleted file mode 100644
index 86233ac..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,1,R1,gasboiler,2035,0.50560000000
-heat,2040,3,R1,heatpump,2030,13.88890000000
-heat,2040,4,R1,heatpump,2035,8.93890000000
-heat,2045,1,R1,gasboiler,2035,0.50560000000
-heat,2045,4,R1,heatpump,2035,8.93890000000
-heat,2045,5,R1,heatpump,2040,17.22220000000
-CO2f,2040,1,R1,gasboiler,2035,32.71450000000
-CO2f,2045,1,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 492d52f..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,0,R1,gasboiler,2035,0.50560000000
-heat,2045,3,R1,heatpump,2035,8.93890000000
-heat,2045,4,R1,heatpump,2040,17.22220000000
-heat,2050,4,R1,heatpump,2040,17.22220000000
-heat,2050,5,R1,heatpump,2045,12.77780000000
-CO2f,2045,0,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2050.csv
deleted file mode 100644
index d89c6fe..0000000
--- a/case-studies/hands-on-files/HO4/default/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-commodity,year,asset,region,installed,technology,supply
-heat,2050,3,R1,2040,heatpump,17.22220000000
-heat,2050,5,R1,2045,heatpump,12.77780000000
-heat,2055,5,R1,2045,heatpump,12.77780000000
-heat,2055,7,R1,2050,heatpump,17.22220000000
diff --git a/case-studies/hands-on-files/HO4/default/input/BaseYearExport.csv b/case-studies/hands-on-files/HO4/default/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO4/default/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/input/BaseYearImport.csv b/case-studies/hands-on-files/HO4/default/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO4/default/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO4/default/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO4/default/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO4/default/input/Projections.csv b/case-studies/hands-on-files/HO4/default/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO4/default/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO4/default/settings.toml b/case-studies/hands-on-files/HO4/default/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO4/default/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO4/default/technodata/Agents.csv b/case-studies/hands-on-files/HO4/default/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO4/default/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO4/default/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO4/default/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO4/default/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO4/default/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO4/default/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO4/default/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO4/default/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO4/default/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO4/default/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO4/default/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO4/default/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO4/default/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO4/default/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO4/default/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO4/default/technodata/residential/CommOut.csv
deleted file mode 100644
index 5e5cd62..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,0,0,64.71,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO4/default/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO4/default/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO4/default/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO4/default_final.zip b/case-studies/hands-on-files/HO4/default_final.zip
deleted file mode 100644
index 272aab3..0000000
Binary files a/case-studies/hands-on-files/HO4/default_final.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index e92c089..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gassupply1,2020,15.00000000000
-0,2025,R1,gassupply1,2020,24.85990000000
-0,2030,R1,gassupply1,2020,17.35990000000
-0,2035,R1,gassupply1,2020,9.85990000000
-0,2040,R1,gassupply1,2020,9.85990000000
-0,2045,R1,gassupply1,2020,9.85990000000
-0,2050,R1,gassupply1,2020,9.85990000000
-0,2059,R1,gassupply1,2020,9.85990000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index e75f645..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gassupply1,2020,24.85990000000
-0,2030,R1,gassupply1,2020,17.35990000000
-0,2035,R1,gassupply1,2020,9.85990000000
-0,2040,R1,gassupply1,2020,9.85990000000
-0,2045,R1,gassupply1,2020,9.85990000000
-0,2050,R1,gassupply1,2020,9.85990000000
-0,2059,R1,gassupply1,2020,9.85990000000
-1,2030,R1,gassupply1,2025,15.25340000000
-1,2035,R1,gassupply1,2025,15.25340000000
-1,2040,R1,gassupply1,2025,15.25340000000
-1,2045,R1,gassupply1,2025,15.25340000000
-1,2050,R1,gassupply1,2025,15.25340000000
-1,2059,R1,gassupply1,2025,15.25340000000
-1,2060,R1,gassupply1,2025,15.25340000000
-1,2064,R1,gassupply1,2025,15.25340000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 8503c30..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,24 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gassupply1,2020,17.35990000000
-0,2035,R1,gassupply1,2020,9.85990000000
-0,2040,R1,gassupply1,2020,9.85990000000
-0,2045,R1,gassupply1,2020,9.85990000000
-0,2050,R1,gassupply1,2020,9.85990000000
-0,2059,R1,gassupply1,2020,9.85990000000
-1,2030,R1,gassupply1,2025,15.25340000000
-1,2035,R1,gassupply1,2025,15.25340000000
-1,2040,R1,gassupply1,2025,15.25340000000
-1,2045,R1,gassupply1,2025,15.25340000000
-1,2050,R1,gassupply1,2025,15.25340000000
-1,2059,R1,gassupply1,2025,15.25340000000
-1,2060,R1,gassupply1,2025,15.25340000000
-1,2064,R1,gassupply1,2025,15.25340000000
-2,2035,R1,gassupply1,2030,15.93050000000
-2,2040,R1,gassupply1,2030,15.93050000000
-2,2045,R1,gassupply1,2030,15.93050000000
-2,2050,R1,gassupply1,2030,15.93050000000
-2,2059,R1,gassupply1,2030,15.93050000000
-2,2060,R1,gassupply1,2030,15.93050000000
-2,2064,R1,gassupply1,2030,15.93050000000
-2,2065,R1,gassupply1,2030,15.93050000000
-2,2069,R1,gassupply1,2030,15.93050000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 13d59a0..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,32 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gassupply1,9.85990000000
-0,2040,R1,2020,gassupply1,9.85990000000
-0,2045,R1,2020,gassupply1,9.85990000000
-0,2050,R1,2020,gassupply1,9.85990000000
-0,2059,R1,2020,gassupply1,9.85990000000
-1,2035,R1,2025,gassupply1,15.25340000000
-1,2040,R1,2025,gassupply1,15.25340000000
-1,2045,R1,2025,gassupply1,15.25340000000
-1,2050,R1,2025,gassupply1,15.25340000000
-1,2059,R1,2025,gassupply1,15.25340000000
-1,2060,R1,2025,gassupply1,15.25340000000
-1,2064,R1,2025,gassupply1,15.25340000000
-2,2035,R1,2030,gassupply1,15.93050000000
-2,2040,R1,2030,gassupply1,15.93050000000
-2,2045,R1,2030,gassupply1,15.93050000000
-2,2050,R1,2030,gassupply1,15.93050000000
-2,2059,R1,2030,gassupply1,15.93050000000
-2,2060,R1,2030,gassupply1,15.93050000000
-2,2064,R1,2030,gassupply1,15.93050000000
-2,2065,R1,2030,gassupply1,15.93050000000
-2,2069,R1,2030,gassupply1,15.93050000000
-3,2040,R1,2035,gassupply1,7.82510000000
-3,2045,R1,2035,gassupply1,7.82510000000
-3,2050,R1,2035,gassupply1,7.82510000000
-3,2059,R1,2035,gassupply1,7.82510000000
-3,2060,R1,2035,gassupply1,7.82510000000
-3,2064,R1,2035,gassupply1,7.82510000000
-3,2065,R1,2035,gassupply1,7.82510000000
-3,2069,R1,2035,gassupply1,7.82510000000
-3,2070,R1,2035,gassupply1,7.82510000000
-3,2074,R1,2035,gassupply1,7.82510000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index b1b3fd3..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,40 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gassupply1,9.85990000000
-0,2045,R1,2020,gassupply1,9.85990000000
-0,2050,R1,2020,gassupply1,9.85990000000
-0,2059,R1,2020,gassupply1,9.85990000000
-1,2040,R1,2025,gassupply1,15.25340000000
-1,2045,R1,2025,gassupply1,15.25340000000
-1,2050,R1,2025,gassupply1,15.25340000000
-1,2059,R1,2025,gassupply1,15.25340000000
-1,2060,R1,2025,gassupply1,15.25340000000
-1,2064,R1,2025,gassupply1,15.25340000000
-2,2040,R1,2030,gassupply1,15.93050000000
-2,2045,R1,2030,gassupply1,15.93050000000
-2,2050,R1,2030,gassupply1,15.93050000000
-2,2059,R1,2030,gassupply1,15.93050000000
-2,2060,R1,2030,gassupply1,15.93050000000
-2,2064,R1,2030,gassupply1,15.93050000000
-2,2065,R1,2030,gassupply1,15.93050000000
-2,2069,R1,2030,gassupply1,15.93050000000
-3,2040,R1,2035,gassupply1,7.82510000000
-3,2045,R1,2035,gassupply1,7.82510000000
-3,2050,R1,2035,gassupply1,7.82510000000
-3,2059,R1,2035,gassupply1,7.82510000000
-3,2060,R1,2035,gassupply1,7.82510000000
-3,2064,R1,2035,gassupply1,7.82510000000
-3,2065,R1,2035,gassupply1,7.82510000000
-3,2069,R1,2035,gassupply1,7.82510000000
-3,2070,R1,2035,gassupply1,7.82510000000
-3,2074,R1,2035,gassupply1,7.82510000000
-4,2045,R1,2040,gassupply1,11.13110000000
-4,2050,R1,2040,gassupply1,11.13110000000
-4,2059,R1,2040,gassupply1,11.13110000000
-4,2060,R1,2040,gassupply1,11.13110000000
-4,2064,R1,2040,gassupply1,11.13110000000
-4,2065,R1,2040,gassupply1,11.13110000000
-4,2069,R1,2040,gassupply1,11.13110000000
-4,2070,R1,2040,gassupply1,11.13110000000
-4,2074,R1,2040,gassupply1,11.13110000000
-4,2075,R1,2040,gassupply1,11.13110000000
-4,2079,R1,2040,gassupply1,11.13110000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index 9424f4e..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,36 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2045,R1,2020,gassupply1,9.85990000000
-0,2050,R1,2020,gassupply1,9.85990000000
-0,2059,R1,2020,gassupply1,9.85990000000
-1,2045,R1,2025,gassupply1,15.25340000000
-1,2050,R1,2025,gassupply1,15.25340000000
-1,2059,R1,2025,gassupply1,15.25340000000
-1,2060,R1,2025,gassupply1,15.25340000000
-1,2064,R1,2025,gassupply1,15.25340000000
-2,2045,R1,2030,gassupply1,15.93050000000
-2,2050,R1,2030,gassupply1,15.93050000000
-2,2059,R1,2030,gassupply1,15.93050000000
-2,2060,R1,2030,gassupply1,15.93050000000
-2,2064,R1,2030,gassupply1,15.93050000000
-2,2065,R1,2030,gassupply1,15.93050000000
-2,2069,R1,2030,gassupply1,15.93050000000
-3,2045,R1,2035,gassupply1,7.82510000000
-3,2050,R1,2035,gassupply1,7.82510000000
-3,2059,R1,2035,gassupply1,7.82510000000
-3,2060,R1,2035,gassupply1,7.82510000000
-3,2064,R1,2035,gassupply1,7.82510000000
-3,2065,R1,2035,gassupply1,7.82510000000
-3,2069,R1,2035,gassupply1,7.82510000000
-3,2070,R1,2035,gassupply1,7.82510000000
-3,2074,R1,2035,gassupply1,7.82510000000
-4,2045,R1,2040,gassupply1,11.13110000000
-4,2050,R1,2040,gassupply1,11.13110000000
-4,2059,R1,2040,gassupply1,11.13110000000
-4,2060,R1,2040,gassupply1,11.13110000000
-4,2064,R1,2040,gassupply1,11.13110000000
-4,2065,R1,2040,gassupply1,11.13110000000
-4,2069,R1,2040,gassupply1,11.13110000000
-4,2070,R1,2040,gassupply1,11.13110000000
-4,2074,R1,2040,gassupply1,11.13110000000
-4,2075,R1,2040,gassupply1,11.13110000000
-4,2079,R1,2040,gassupply1,11.13110000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index bfb3d8e..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,48 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2050,R1,gassupply1,2020,9.85990000000
-0,2055,R1,gassupply1,2020,9.85990000000
-0,2059,R1,gassupply1,2020,9.85990000000
-1,2050,R1,gassupply1,2025,15.25340000000
-1,2055,R1,gassupply1,2025,15.25340000000
-1,2059,R1,gassupply1,2025,15.25340000000
-1,2060,R1,gassupply1,2025,15.25340000000
-1,2064,R1,gassupply1,2025,15.25340000000
-2,2050,R1,gassupply1,2030,15.93050000000
-2,2055,R1,gassupply1,2030,15.93050000000
-2,2059,R1,gassupply1,2030,15.93050000000
-2,2060,R1,gassupply1,2030,15.93050000000
-2,2064,R1,gassupply1,2030,15.93050000000
-2,2065,R1,gassupply1,2030,15.93050000000
-2,2069,R1,gassupply1,2030,15.93050000000
-3,2050,R1,gassupply1,2035,7.82510000000
-3,2055,R1,gassupply1,2035,7.82510000000
-3,2059,R1,gassupply1,2035,7.82510000000
-3,2060,R1,gassupply1,2035,7.82510000000
-3,2064,R1,gassupply1,2035,7.82510000000
-3,2065,R1,gassupply1,2035,7.82510000000
-3,2069,R1,gassupply1,2035,7.82510000000
-3,2070,R1,gassupply1,2035,7.82510000000
-3,2074,R1,gassupply1,2035,7.82510000000
-4,2050,R1,gassupply1,2040,11.13110000000
-4,2055,R1,gassupply1,2040,11.13110000000
-4,2059,R1,gassupply1,2040,11.13110000000
-4,2060,R1,gassupply1,2040,11.13110000000
-4,2064,R1,gassupply1,2040,11.13110000000
-4,2065,R1,gassupply1,2040,11.13110000000
-4,2069,R1,gassupply1,2040,11.13110000000
-4,2070,R1,gassupply1,2040,11.13110000000
-4,2074,R1,gassupply1,2040,11.13110000000
-4,2075,R1,gassupply1,2040,11.13110000000
-4,2079,R1,gassupply1,2040,11.13110000000
-5,2055,R1,gassupply1,2050,0.43140000000
-5,2059,R1,gassupply1,2050,0.43140000000
-5,2060,R1,gassupply1,2050,0.43140000000
-5,2064,R1,gassupply1,2050,0.43140000000
-5,2065,R1,gassupply1,2050,0.43140000000
-5,2069,R1,gassupply1,2050,0.43140000000
-5,2070,R1,gassupply1,2050,0.43140000000
-5,2074,R1,gassupply1,2050,0.43140000000
-5,2075,R1,gassupply1,2050,0.43140000000
-5,2079,R1,gassupply1,2050,0.43140000000
-5,2080,R1,gassupply1,2050,0.43140000000
-5,2089,R1,gassupply1,2050,0.43140000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/MCACapacity.csv b/case-studies/hands-on-files/HO4/default_final/Results/MCACapacity.csv
deleted file mode 100644
index 70caaab..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/MCACapacity.csv
+++ /dev/null
@@ -1,68 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity
-gasboiler,R1,R1,A1,residential,retrofit,2020,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2020,0.50000000000
-solarPV,R1,R1,A1,power,retrofit,2020,0.50000000000
-windturbine,R1,R1,A1,power,retrofit,2020,0.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2020,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2025,17.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2025,7.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2025,1.75000000000
-solarPV,R1,R1,A1,power,retrofit,2025,1.50000000000
-windturbine,R1,R1,A1,power,retrofit,2025,1.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2025,24.85990000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,12.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,11.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,7.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,1.25000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,0.52780000000
-solarPV,R1,R1,A1,power,retrofit,2030,1.50000000000
-windturbine,R1,R1,A1,power,retrofit,2030,1.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,17.35990000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,15.25340000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,11.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,13.15000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,5.01840000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,1.25000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,0.52780000000
-solarPV,R1,R1,A1,power,retrofit,2035,1.50000000000
-windturbine,R1,R1,A1,power,retrofit,2035,1.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,9.85990000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,15.25340000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,15.93050000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,13.15000000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,19.41500000000
-heatpump,R1,R1,A1,residential,retrofit,2040,5.01840000000
-heatpump,R1,R1,A1,residential,retrofit,2040,0.50180000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,1.25000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,0.52780000000
-solarPV,R1,R1,A1,power,retrofit,2040,1.50000000000
-windturbine,R1,R1,A1,power,retrofit,2040,1.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,9.85990000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,15.25340000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,15.93050000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,7.82510000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,19.41500000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,14.20320000000
-heatpump,R1,R1,A1,residential,retrofit,2045,0.50180000000
-heatpump,R1,R1,A1,residential,retrofit,2045,2.78520000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,1.25000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,0.52780000000
-solarPV,R1,R1,A1,power,retrofit,2045,1.50000000000
-windturbine,R1,R1,A1,power,retrofit,2045,1.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,9.85990000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,15.25340000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,15.93050000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,7.82510000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,11.13110000000
-gasboiler,R1,R1,A1,residential,retrofit,2050,14.20320000000
-gasboiler,R1,R1,A1,residential,retrofit,2050,17.79160000000
-heatpump,R1,R1,A1,residential,retrofit,2050,2.78520000000
-heatpump,R1,R1,A1,residential,retrofit,2050,0.41530000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,1.25000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,0.52780000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,0.40470000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,9.85990000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,15.25340000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,15.93050000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,7.82510000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,11.13110000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/MCAPrices.csv b/case-studies/hands-on-files/HO4/default_final/Results/MCAPrices.csv
deleted file mode 100644
index 4984566..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/MCAPrices.csv
+++ /dev/null
@@ -1,169 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,1.46990000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.48670000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,2.20490000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,2.23000000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.46990000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.48670000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.20490000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,2.23000000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.40970000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,4.46000000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,2.93980000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,2.97330000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,0.90350000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.81030000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,1.35860000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,1.22890000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,0.90350000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.81030000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.35860000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.22890000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,2.73750000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,2.53850000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,1.81600000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,1.65650000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.20670000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,1.17470000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,1.81340000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,1.77010000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.20670000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.17470000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.81340000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.77010000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,3.64700000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.58910000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,2.42230000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,2.37100000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,1.50980000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,1.53320000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,2.26820000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,2.30850000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.50980000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.53320000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.26820000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,2.30850000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.55660000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,4.67000000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,3.02870000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,3.08980000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,1.93520000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,2.10830000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,2.90610000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,3.17020000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.93520000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.10830000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.90610000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,3.17020000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.83250000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,6.38740000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,3.87930000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,4.23740000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,4.04250000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,2.58330000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,6.06960000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,3.88400000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,4.04250000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.58330000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,6.06960000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,3.88400000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,12.17470000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,7.82230000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,8.10070000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,5.19070000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2020.csv
deleted file mode 100644
index 3a18354..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,24 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,0.50000000000
-0,2025,R1,2020,gasCCGT,1.75000000000
-0,2030,R1,2020,gasCCGT,1.25000000000
-0,2035,R1,2020,gasCCGT,1.25000000000
-0,2040,R1,2020,gasCCGT,1.25000000000
-0,2045,R1,2020,gasCCGT,1.25000000000
-0,2049,R1,2020,gasCCGT,1.25000000000
-0,2050,R1,2020,gasCCGT,1.25000000000
-0,2059,R1,2020,gasCCGT,1.25000000000
-1,2020,R1,2020,solarPV,0.50000000000
-1,2025,R1,2020,solarPV,1.50000000000
-1,2030,R1,2020,solarPV,1.50000000000
-1,2035,R1,2020,solarPV,1.50000000000
-1,2040,R1,2020,solarPV,1.50000000000
-1,2045,R1,2020,solarPV,1.50000000000
-1,2049,R1,2020,solarPV,1.50000000000
-2,2020,R1,2020,windturbine,0.50000000000
-2,2025,R1,2020,windturbine,1.50000000000
-2,2030,R1,2020,windturbine,1.50000000000
-2,2035,R1,2020,windturbine,1.50000000000
-2,2040,R1,2020,windturbine,1.50000000000
-2,2045,R1,2020,windturbine,1.50000000000
-2,2049,R1,2020,windturbine,1.50000000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2025.csv
deleted file mode 100644
index 1177fef..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,30 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2025,R1,2020,gasCCGT,1.75000000000
-0,2030,R1,2020,gasCCGT,1.25000000000
-0,2035,R1,2020,gasCCGT,1.25000000000
-0,2040,R1,2020,gasCCGT,1.25000000000
-0,2045,R1,2020,gasCCGT,1.25000000000
-0,2049,R1,2020,gasCCGT,1.25000000000
-0,2050,R1,2020,gasCCGT,1.25000000000
-0,2059,R1,2020,gasCCGT,1.25000000000
-1,2025,R1,2020,solarPV,1.50000000000
-1,2030,R1,2020,solarPV,1.50000000000
-1,2035,R1,2020,solarPV,1.50000000000
-1,2040,R1,2020,solarPV,1.50000000000
-1,2045,R1,2020,solarPV,1.50000000000
-1,2049,R1,2020,solarPV,1.50000000000
-2,2025,R1,2020,windturbine,1.50000000000
-2,2030,R1,2020,windturbine,1.50000000000
-2,2035,R1,2020,windturbine,1.50000000000
-2,2040,R1,2020,windturbine,1.50000000000
-2,2045,R1,2020,windturbine,1.50000000000
-2,2049,R1,2020,windturbine,1.50000000000
-3,2030,R1,2025,gasCCGT,0.52780000000
-3,2035,R1,2025,gasCCGT,0.52780000000
-3,2040,R1,2025,gasCCGT,0.52780000000
-3,2045,R1,2025,gasCCGT,0.52780000000
-3,2049,R1,2025,gasCCGT,0.52780000000
-3,2050,R1,2025,gasCCGT,0.52780000000
-3,2059,R1,2025,gasCCGT,0.52780000000
-3,2060,R1,2025,gasCCGT,0.52780000000
-3,2064,R1,2025,gasCCGT,0.52780000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2030.csv
deleted file mode 100644
index 6cd1584..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,27 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gasCCGT,1.25000000000
-0,2035,R1,2020,gasCCGT,1.25000000000
-0,2040,R1,2020,gasCCGT,1.25000000000
-0,2045,R1,2020,gasCCGT,1.25000000000
-0,2049,R1,2020,gasCCGT,1.25000000000
-0,2050,R1,2020,gasCCGT,1.25000000000
-0,2059,R1,2020,gasCCGT,1.25000000000
-1,2030,R1,2020,solarPV,1.50000000000
-1,2035,R1,2020,solarPV,1.50000000000
-1,2040,R1,2020,solarPV,1.50000000000
-1,2045,R1,2020,solarPV,1.50000000000
-1,2049,R1,2020,solarPV,1.50000000000
-2,2030,R1,2020,windturbine,1.50000000000
-2,2035,R1,2020,windturbine,1.50000000000
-2,2040,R1,2020,windturbine,1.50000000000
-2,2045,R1,2020,windturbine,1.50000000000
-2,2049,R1,2020,windturbine,1.50000000000
-3,2030,R1,2025,gasCCGT,0.52780000000
-3,2035,R1,2025,gasCCGT,0.52780000000
-3,2040,R1,2025,gasCCGT,0.52780000000
-3,2045,R1,2025,gasCCGT,0.52780000000
-3,2049,R1,2025,gasCCGT,0.52780000000
-3,2050,R1,2025,gasCCGT,0.52780000000
-3,2059,R1,2025,gasCCGT,0.52780000000
-3,2060,R1,2025,gasCCGT,0.52780000000
-3,2064,R1,2025,gasCCGT,0.52780000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2035.csv
deleted file mode 100644
index 7e5c248..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,23 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gasCCGT,1.25000000000
-0,2040,R1,2020,gasCCGT,1.25000000000
-0,2045,R1,2020,gasCCGT,1.25000000000
-0,2049,R1,2020,gasCCGT,1.25000000000
-0,2050,R1,2020,gasCCGT,1.25000000000
-0,2059,R1,2020,gasCCGT,1.25000000000
-1,2035,R1,2020,solarPV,1.50000000000
-1,2040,R1,2020,solarPV,1.50000000000
-1,2045,R1,2020,solarPV,1.50000000000
-1,2049,R1,2020,solarPV,1.50000000000
-2,2035,R1,2020,windturbine,1.50000000000
-2,2040,R1,2020,windturbine,1.50000000000
-2,2045,R1,2020,windturbine,1.50000000000
-2,2049,R1,2020,windturbine,1.50000000000
-3,2035,R1,2025,gasCCGT,0.52780000000
-3,2040,R1,2025,gasCCGT,0.52780000000
-3,2045,R1,2025,gasCCGT,0.52780000000
-3,2049,R1,2025,gasCCGT,0.52780000000
-3,2050,R1,2025,gasCCGT,0.52780000000
-3,2059,R1,2025,gasCCGT,0.52780000000
-3,2060,R1,2025,gasCCGT,0.52780000000
-3,2064,R1,2025,gasCCGT,0.52780000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2040.csv
deleted file mode 100644
index ef60193..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,19 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,1.25000000000
-0,2045,R1,2020,gasCCGT,1.25000000000
-0,2049,R1,2020,gasCCGT,1.25000000000
-0,2050,R1,2020,gasCCGT,1.25000000000
-0,2059,R1,2020,gasCCGT,1.25000000000
-1,2040,R1,2020,solarPV,1.50000000000
-1,2045,R1,2020,solarPV,1.50000000000
-1,2049,R1,2020,solarPV,1.50000000000
-2,2040,R1,2020,windturbine,1.50000000000
-2,2045,R1,2020,windturbine,1.50000000000
-2,2049,R1,2020,windturbine,1.50000000000
-3,2040,R1,2025,gasCCGT,0.52780000000
-3,2045,R1,2025,gasCCGT,0.52780000000
-3,2049,R1,2025,gasCCGT,0.52780000000
-3,2050,R1,2025,gasCCGT,0.52780000000
-3,2059,R1,2025,gasCCGT,0.52780000000
-3,2060,R1,2025,gasCCGT,0.52780000000
-3,2064,R1,2025,gasCCGT,0.52780000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2045.csv
deleted file mode 100644
index bc00272..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasCCGT,2020,1.25000000000
-0,2049,R1,gasCCGT,2020,1.25000000000
-0,2050,R1,gasCCGT,2020,1.25000000000
-0,2059,R1,gasCCGT,2020,1.25000000000
-1,2045,R1,gasCCGT,2025,0.52780000000
-1,2049,R1,gasCCGT,2025,0.52780000000
-1,2050,R1,gasCCGT,2025,0.52780000000
-1,2059,R1,gasCCGT,2025,0.52780000000
-1,2060,R1,gasCCGT,2025,0.52780000000
-1,2064,R1,gasCCGT,2025,0.52780000000
-2,2050,R1,gasCCGT,2045,0.40470000000
-2,2059,R1,gasCCGT,2045,0.40470000000
-2,2060,R1,gasCCGT,2045,0.40470000000
-2,2064,R1,gasCCGT,2045,0.40470000000
-2,2065,R1,gasCCGT,2045,0.40470000000
-2,2084,R1,gasCCGT,2045,0.40470000000
-3,2045,R1,solarPV,2020,1.50000000000
-3,2049,R1,solarPV,2020,1.50000000000
-4,2045,R1,windturbine,2020,1.50000000000
-4,2049,R1,windturbine,2020,1.50000000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2050.csv
deleted file mode 100644
index 24a105a..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gasCCGT,1.25000000000
-0,2055,R1,2020,gasCCGT,1.25000000000
-0,2059,R1,2020,gasCCGT,1.25000000000
-3,2050,R1,2025,gasCCGT,0.52780000000
-3,2055,R1,2025,gasCCGT,0.52780000000
-3,2059,R1,2025,gasCCGT,0.52780000000
-3,2060,R1,2025,gasCCGT,0.52780000000
-3,2064,R1,2025,gasCCGT,0.52780000000
-4,2050,R1,2045,gasCCGT,0.40470000000
-4,2055,R1,2045,gasCCGT,0.40470000000
-4,2059,R1,2045,gasCCGT,0.40470000000
-4,2060,R1,2045,gasCCGT,0.40470000000
-4,2064,R1,2045,gasCCGT,0.40470000000
-4,2065,R1,2045,gasCCGT,0.40470000000
-4,2084,R1,2045,gasCCGT,0.40470000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index 27f8e13..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gasboiler,2020,10.00000000000
-0,2025,R1,gasboiler,2020,17.00000000000
-0,2030,R1,gasboiler,2020,12.00000000000
-0,2034,R1,gasboiler,2020,12.00000000000
-1,2025,R1,heatpump,2020,7.00000000000
-1,2030,R1,heatpump,2020,7.00000000000
-1,2034,R1,heatpump,2020,7.00000000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index ba7ba27..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasboiler,2020,17.00000000000
-0,2030,R1,gasboiler,2020,12.00000000000
-0,2034,R1,gasboiler,2020,12.00000000000
-1,2030,R1,gasboiler,2025,11.00000000000
-1,2034,R1,gasboiler,2025,11.00000000000
-1,2035,R1,gasboiler,2025,11.00000000000
-1,2039,R1,gasboiler,2025,11.00000000000
-2,2025,R1,heatpump,2020,7.00000000000
-2,2030,R1,heatpump,2020,7.00000000000
-2,2034,R1,heatpump,2020,7.00000000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index fa780d7..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gasboiler,2020,12.00000000000
-0,2034,R1,gasboiler,2020,12.00000000000
-1,2030,R1,gasboiler,2025,11.00000000000
-1,2034,R1,gasboiler,2025,11.00000000000
-1,2035,R1,gasboiler,2025,11.00000000000
-1,2039,R1,gasboiler,2025,11.00000000000
-2,2035,R1,gasboiler,2030,13.15000000000
-2,2039,R1,gasboiler,2030,13.15000000000
-2,2040,R1,gasboiler,2030,13.15000000000
-2,2044,R1,gasboiler,2030,13.15000000000
-3,2030,R1,heatpump,2020,7.00000000000
-3,2034,R1,heatpump,2020,7.00000000000
-4,2035,R1,heatpump,2030,5.01840000000
-4,2039,R1,heatpump,2030,5.01840000000
-4,2040,R1,heatpump,2030,5.01840000000
-4,2044,R1,heatpump,2030,5.01840000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 3d5d990..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,19 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasboiler,2025,11.00000000000
-0,2039,R1,gasboiler,2025,11.00000000000
-1,2035,R1,gasboiler,2030,13.15000000000
-1,2039,R1,gasboiler,2030,13.15000000000
-1,2040,R1,gasboiler,2030,13.15000000000
-1,2044,R1,gasboiler,2030,13.15000000000
-2,2040,R1,gasboiler,2035,19.41500000000
-2,2044,R1,gasboiler,2035,19.41500000000
-2,2045,R1,gasboiler,2035,19.41500000000
-2,2049,R1,gasboiler,2035,19.41500000000
-3,2035,R1,heatpump,2030,5.01840000000
-3,2039,R1,heatpump,2030,5.01840000000
-3,2040,R1,heatpump,2030,5.01840000000
-3,2044,R1,heatpump,2030,5.01840000000
-4,2040,R1,heatpump,2035,0.50180000000
-4,2044,R1,heatpump,2035,0.50180000000
-4,2045,R1,heatpump,2035,0.50180000000
-4,2049,R1,heatpump,2035,0.50180000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 22514f5..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2040,R1,gasboiler,2030,13.15000000000
-0,2044,R1,gasboiler,2030,13.15000000000
-1,2040,R1,gasboiler,2035,19.41500000000
-1,2044,R1,gasboiler,2035,19.41500000000
-1,2045,R1,gasboiler,2035,19.41500000000
-1,2049,R1,gasboiler,2035,19.41500000000
-2,2045,R1,gasboiler,2040,14.20320000000
-2,2049,R1,gasboiler,2040,14.20320000000
-2,2050,R1,gasboiler,2040,14.20320000000
-2,2054,R1,gasboiler,2040,14.20320000000
-3,2040,R1,heatpump,2030,5.01840000000
-3,2044,R1,heatpump,2030,5.01840000000
-4,2040,R1,heatpump,2035,0.50180000000
-4,2044,R1,heatpump,2035,0.50180000000
-4,2045,R1,heatpump,2035,0.50180000000
-4,2049,R1,heatpump,2035,0.50180000000
-5,2045,R1,heatpump,2040,2.78520000000
-5,2049,R1,heatpump,2040,2.78520000000
-5,2050,R1,heatpump,2040,2.78520000000
-5,2054,R1,heatpump,2040,2.78520000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index 1c58c5a..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasboiler,2035,19.41500000000
-0,2049,R1,gasboiler,2035,19.41500000000
-1,2045,R1,gasboiler,2040,14.20320000000
-1,2049,R1,gasboiler,2040,14.20320000000
-1,2050,R1,gasboiler,2040,14.20320000000
-1,2054,R1,gasboiler,2040,14.20320000000
-2,2050,R1,gasboiler,2045,17.79160000000
-2,2054,R1,gasboiler,2045,17.79160000000
-2,2055,R1,gasboiler,2045,17.79160000000
-2,2059,R1,gasboiler,2045,17.79160000000
-3,2045,R1,heatpump,2035,0.50180000000
-3,2049,R1,heatpump,2035,0.50180000000
-4,2045,R1,heatpump,2040,2.78520000000
-4,2049,R1,heatpump,2040,2.78520000000
-4,2050,R1,heatpump,2040,2.78520000000
-4,2054,R1,heatpump,2040,2.78520000000
-5,2050,R1,heatpump,2045,0.41530000000
-5,2054,R1,heatpump,2045,0.41530000000
-5,2055,R1,heatpump,2045,0.41530000000
-5,2059,R1,heatpump,2045,0.41530000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index db3b254..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,installed,technology,capacity
-2,2050,R1,2040,gasboiler,14.20320000000
-2,2054,R1,2040,gasboiler,14.20320000000
-3,2050,R1,2040,heatpump,2.78520000000
-3,2054,R1,2040,heatpump,2.78520000000
-4,2050,R1,2045,gasboiler,17.79160000000
-4,2054,R1,2045,gasboiler,17.79160000000
-4,2055,R1,2045,gasboiler,17.79160000000
-4,2059,R1,2045,gasboiler,17.79160000000
-5,2050,R1,2045,heatpump,0.41530000000
-5,2054,R1,2045,heatpump,0.41530000000
-5,2055,R1,2045,heatpump,0.41530000000
-5,2059,R1,2045,heatpump,0.41530000000
-6,2055,R1,2050,gasboiler,10.05120000000
-6,2059,R1,2050,gasboiler,10.05120000000
-6,2060,R1,2050,gasboiler,10.05120000000
-6,2064,R1,2050,gasboiler,10.05120000000
-7,2055,R1,2050,heatpump,1.64440000000
-7,2059,R1,2050,heatpump,1.64440000000
-7,2060,R1,2050,heatpump,1.64440000000
-7,2064,R1,2050,heatpump,1.64440000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2020.csv
deleted file mode 100644
index d475bd3..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,0,R1,gasboiler,2020,10.00000000000
-heat,2025,0,R1,gasboiler,2020,9.44440000000
-heat,2025,1,R1,heatpump,2020,3.88890000000
-CO2f,2020,0,R1,gasboiler,2020,647.10000000000
-CO2f,2025,0,R1,gasboiler,2020,611.15000000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2025.csv
deleted file mode 100644
index 4da4605..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,0,R1,gasboiler,2020,9.44440000000
-heat,2025,2,R1,heatpump,2020,3.88890000000
-heat,2030,0,R1,gasboiler,2020,6.66670000000
-heat,2030,1,R1,gasboiler,2025,6.11110000000
-heat,2030,2,R1,heatpump,2020,3.88890000000
-CO2f,2025,0,R1,gasboiler,2020,611.15000000000
-CO2f,2030,0,R1,gasboiler,2020,431.40000000000
-CO2f,2030,1,R1,gasboiler,2025,395.45000000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2030.csv
deleted file mode 100644
index d13425e..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,0,R1,gasboiler,2020,6.66670000000
-heat,2030,1,R1,gasboiler,2025,6.11110000000
-heat,2030,3,R1,heatpump,2020,3.88890000000
-heat,2035,1,R1,gasboiler,2025,7.54240000000
-heat,2035,2,R1,gasboiler,2030,9.01660000000
-heat,2035,4,R1,heatpump,2030,3.44100000000
-CO2f,2030,0,R1,gasboiler,2020,431.40000000000
-CO2f,2030,1,R1,gasboiler,2025,395.45000000000
-CO2f,2035,1,R1,gasboiler,2025,488.06890000000
-CO2f,2035,2,R1,gasboiler,2030,583.46420000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 8bf75df..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,12 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,0,R1,gasboiler,2025,7.54240000000
-heat,2035,1,R1,gasboiler,2030,9.01660000000
-heat,2035,3,R1,heatpump,2030,3.44100000000
-heat,2040,1,R1,gasboiler,2030,8.05650000000
-heat,2040,2,R1,gasboiler,2035,11.89480000000
-heat,2040,3,R1,heatpump,2030,3.07460000000
-heat,2040,4,R1,heatpump,2035,0.30750000000
-CO2f,2035,0,R1,gasboiler,2025,488.06890000000
-CO2f,2035,1,R1,gasboiler,2030,583.46420000000
-CO2f,2040,1,R1,gasboiler,2030,521.33510000000
-CO2f,2040,2,R1,gasboiler,2035,769.71260000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2040.csv
deleted file mode 100644
index 05a0c20..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,0,R1,gasboiler,2030,8.05650000000
-heat,2040,1,R1,gasboiler,2035,11.89480000000
-heat,2040,3,R1,heatpump,2030,3.07460000000
-heat,2040,4,R1,heatpump,2035,0.30750000000
-heat,2045,1,R1,gasboiler,2035,14.02870000000
-heat,2045,2,R1,gasboiler,2040,10.26280000000
-heat,2045,4,R1,heatpump,2035,0.36260000000
-heat,2045,5,R1,heatpump,2040,2.01250000000
-CO2f,2040,0,R1,gasboiler,2030,521.33510000000
-CO2f,2040,1,R1,gasboiler,2035,769.71260000000
-CO2f,2045,1,R1,gasboiler,2035,907.79670000000
-CO2f,2045,2,R1,gasboiler,2040,664.10830000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 27053b5..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,0,R1,gasboiler,2035,14.02870000000
-heat,2045,1,R1,gasboiler,2040,10.26280000000
-heat,2045,3,R1,heatpump,2035,0.36260000000
-heat,2045,4,R1,heatpump,2040,2.01250000000
-heat,2050,1,R1,gasboiler,2040,12.10670000000
-heat,2050,2,R1,gasboiler,2045,15.16530000000
-heat,2050,4,R1,heatpump,2040,2.37410000000
-heat,2050,5,R1,heatpump,2045,0.35400000000
-CO2f,2045,0,R1,gasboiler,2035,907.79670000000
-CO2f,2045,1,R1,gasboiler,2040,664.10830000000
-CO2f,2050,1,R1,gasboiler,2040,783.42170000000
-CO2f,2050,2,R1,gasboiler,2045,981.34550000000
diff --git a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2050.csv
deleted file mode 100644
index e73605e..0000000
--- a/case-studies/hands-on-files/HO4/default_final/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,installed,technology,supply
-heat,2050,2,R1,2040,gasboiler,12.10670000000
-heat,2050,3,R1,2040,heatpump,2.37410000000
-heat,2050,4,R1,2045,gasboiler,15.16530000000
-heat,2050,5,R1,2045,heatpump,0.35400000000
-heat,2055,4,R1,2045,gasboiler,17.79160000000
-heat,2055,5,R1,2045,heatpump,0.41530000000
-heat,2055,6,R1,2050,gasboiler,10.05120000000
-heat,2055,7,R1,2050,heatpump,1.64440000000
-CO2f,2050,2,R1,2040,gasboiler,783.42170000000
-CO2f,2050,4,R1,2045,gasboiler,981.34550000000
-CO2f,2055,4,R1,2045,gasboiler,1151.29200000000
-CO2f,2055,6,R1,2050,gasboiler,650.41240000000
diff --git a/case-studies/hands-on-files/HO4/default_final/input/BaseYearExport.csv b/case-studies/hands-on-files/HO4/default_final/input/BaseYearExport.csv
deleted file mode 100644
index 06cef50..0000000
--- a/case-studies/hands-on-files/HO4/default_final/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind,solar
-Unit,-,Year,PJ,PJ,PJ,kt,PJ,PJ
-R1,Exports,2010,0,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/input/BaseYearImport.csv b/case-studies/hands-on-files/HO4/default_final/input/BaseYearImport.csv
deleted file mode 100644
index 1f38edb..0000000
--- a/case-studies/hands-on-files/HO4/default_final/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind,solar
-Unit,-,Year,PJ,PJ,PJ,kt,PJ,PJ
-R1,Imports,2010,0,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO4/default_final/input/GlobalCommodities.csv
deleted file mode 100644
index 386bb1b..0000000
--- a/case-studies/hands-on-files/HO4/default_final/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
-Solar,Energy,solar,0,1,PJ
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/input/Projections.csv b/case-studies/hands-on-files/HO4/default_final/input/Projections.csv
deleted file mode 100644
index 91c6a1d..0000000
--- a/case-studies/hands-on-files/HO4/default_final/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind,solar
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/settings.toml b/case-studies/hands-on-files/HO4/default_final/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO4/default_final/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/Agents.csv b/case-studies/hands-on-files/HO4/default_final/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO4/default_final/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO4/default_final/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO4/default_final/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO4/default_final/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO4/default_final/technodata/power/CommIn.csv
deleted file mode 100644
index dc456c2..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,solar
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1,0
-solarPV,R1,2020,fixed,0,0,0,0,0,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO4/default_final/technodata/power/CommOut.csv
deleted file mode 100644
index e52c441..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind,solar
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0,0
-windturbine,R1,2020,fixed,1,0,0,0,0,0
-solarPV,R1,2020,fixed,1,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO4/default_final/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 0614492..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,0.5,0,0,0,0,0,0
-windturbine,R1,PJ/y,0.5,0,0,0,0,0,0
-solarPV,R1,PJ/y,0.5,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO4/default_final/technodata/power/Technodata.csv
deleted file mode 100644
index 32326cd..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
-solarPV,R1,2020,fixed,15,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,solar,electricity,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO4/default_final/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO4/default_final/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO4/default_final/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO4/default_final/technodata/residential/CommOut.csv
deleted file mode 100644
index 5e5cd62..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,0,0,64.71,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO4/default_final/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO4/default_final/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO4/default_final/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO4/default_final/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO5/Macrodrivers.csv b/case-studies/hands-on-files/HO5/Macrodrivers.csv
deleted file mode 100644
index 196b1f2..0000000
--- a/case-studies/hands-on-files/HO5/Macrodrivers.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Variable,RegionName,Unit,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060,2061,2062,2063,2064,2065,2066,2067,2068,2069,2070,2071,2072,2073,2074,2075,2076,2077,2078,2079,2080,2081,2082,2083,2084,2085,2086,2087,2088,2089,2090,2091,2092,2093,2094,2095,2096,2097,2098,2099,2100,2101,2102,2103,2104,2105,2106,2107,2108,2109,2110
-GDP|PPP,R1,millionUS$2005,1206919,1220599,1234278,1247958,1261637,1275317,1288997,1302676,1316356,1330035,1343715,1384465,1425215,1465965,1506715,1547465,1588215,1628965,1669715,1710465,1751215,1796887,1842558,1888230,1933901,1979573,2025244,2070916,2116587,2162259,2207930,2257386,2306842,2356297,2405753,2455209,2504665,2554120,2603576,2653032,2702488,2758342,2814196,2870050,2925904,2981758,3037612,3093466,3149320,3205175,3261029,3330014,3398999,3467984,3536969,3605954,3674939,3743925,3812910,3881895,3950880,4017445,4084009,4150574,4217139,4283704,4350268,4416833,4483398,4549962,4616527,4681288,4746048,4810808,4875569,4940329,5005090,5069850,5134610,5199371,5264131,5326189,5388246,5450304,5512362,5574419,5636477,5698534,5760592,5822650,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707
-Population,R1,million,80.0042,80.9151,81.82599,82.73689,83.64779,84.55868,85.46958,86.38048,87.29137,88.20227,89.11317,89.83065,90.54813,91.26561,91.98309,92.70057,93.41805,94.13553,94.85301,95.57049,96.28797,96.82205,97.35612,97.89019,98.42427,98.95834,99.49242,100.0265,100.5606,101.0946,101.6287,101.971,102.3133,102.6556,102.9979,103.3402,103.6825,104.0247,104.367,104.7093,105.0516,105.1876,105.3236,105.4596,105.5956,105.7316,105.8676,106.0036,106.1396,106.2756,106.4116,106.3854,106.3591,106.3328,106.3065,106.2802,106.2539,106.2276,106.2014,106.1751,106.1488,105.9819,105.815,105.6482,105.4813,105.3144,105.1476,104.9807,104.8138,104.6469,104.4801,104.2169,103.9538,103.6906,103.4275,103.1643,102.9012,102.638,102.3749,102.1117,101.8486,101.5358,101.223,100.9102,100.5974,100.2846,99.97179,99.65899,99.34619,99.03339,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059
diff --git a/case-studies/hands-on-files/HO5/TimesliceSharepreset.csv b/case-studies/hands-on-files/HO5/TimesliceSharepreset.csv
deleted file mode 100644
index 47bf41e..0000000
--- a/case-studies/hands-on-files/HO5/TimesliceSharepreset.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-SN,RegionName,electricity,gas,heat,CO2f,wind
-1,R1,0,0,0.034835,0,0
-2,R1,0,0,0.064546,0,0
-3,R1,0,0,0.044569,0,0
-4,R1,0,0,0.011161,0,0
-5,R1,0,0,0.014145,0,0
-6,R1,0,0,0.085783,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO5/capacity_results.xlsx b/case-studies/hands-on-files/HO5/capacity_results.xlsx
deleted file mode 100644
index 5179aa6..0000000
Binary files a/case-studies/hands-on-files/HO5/capacity_results.xlsx and /dev/null differ
diff --git a/case-studies/hands-on-files/HO5/default.zip b/case-studies/hands-on-files/HO5/default.zip
deleted file mode 100644
index c34a1d9..0000000
Binary files a/case-studies/hands-on-files/HO5/default.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index fdbb2d2..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gassupply1,15.00000000000
-0,2025,R1,2020,gassupply1,15.00000000000
-0,2030,R1,2020,gassupply1,7.50000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index b130ea3..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gassupply1,2020,15.00000000000
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 9fd495c..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gassupply1,2020,7.50000000000
-1,2030,R1,gassupply1,2025,9.58580000000
-1,2035,R1,gassupply1,2025,9.58580000000
-1,2040,R1,gassupply1,2025,9.58580000000
-1,2045,R1,gassupply1,2025,9.58580000000
-1,2050,R1,gassupply1,2025,9.58580000000
-1,2064,R1,gassupply1,2025,9.58580000000
-2,2035,R1,gassupply1,2030,13.12830000000
-2,2040,R1,gassupply1,2030,13.12830000000
-2,2045,R1,gassupply1,2030,13.12830000000
-2,2050,R1,gassupply1,2030,13.12830000000
-2,2064,R1,gassupply1,2030,13.12830000000
-2,2065,R1,gassupply1,2030,13.12830000000
-2,2069,R1,gassupply1,2030,13.12830000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 6aeac29..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2035,R1,2025,gassupply1,9.58580000000
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2035,R1,2030,gassupply1,13.12830000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index dbad909..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2040,R1,2025,gassupply1,9.58580000000
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2040,R1,2030,gassupply1,13.12830000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index 9012924..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2045,R1,2025,gassupply1,9.58580000000
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2045,R1,2030,gassupply1,13.12830000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index 98a9ae8..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,installed,technology,capacity
-1,2050,R1,2025,gassupply1,9.58580000000
-1,2055,R1,2025,gassupply1,9.58580000000
-1,2064,R1,2025,gassupply1,9.58580000000
-2,2050,R1,2030,gassupply1,13.12830000000
-2,2055,R1,2030,gassupply1,13.12830000000
-2,2064,R1,2030,gassupply1,13.12830000000
-2,2065,R1,2030,gassupply1,13.12830000000
-2,2069,R1,2030,gassupply1,13.12830000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/MCACapacity.csv b/case-studies/hands-on-files/HO5/default/Results/MCACapacity.csv
deleted file mode 100644
index 21b682f..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/MCACapacity.csv
+++ /dev/null
@@ -1,57 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity
-gasboiler,R1,R1,A1,residential,retrofit,2020,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2020,1.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2020,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2025,5.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2025,19.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2025,4.00000000000
-windturbine,R1,R1,A1,power,retrofit,2025,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2025,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,19.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,6.90000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,4.06670000000
-windturbine,R1,R1,A1,power,retrofit,2030,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,7.50000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,9.58580000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,4.10000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,6.90000000000
-heatpump,R1,R1,A1,residential,retrofit,2035,25.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2035,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,25.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2040,16.09000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2040,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,6.38000000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,13.12830000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,0.91000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,16.09000000000
-heatpump,R1,R1,A1,residential,retrofit,2045,31.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2045,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2045,5.62000000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,13.12830000000
-heatpump,R1,R1,A1,residential,retrofit,2050,31.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2050,23.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,3.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,4.06670000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,5.33330000000
-windturbine,R1,R1,A1,power,retrofit,2050,6.38000000000
-windturbine,R1,R1,A1,power,retrofit,2050,5.62000000000
-windturbine,R1,R1,A1,power,retrofit,2050,14.10000000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,9.58580000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,13.12830000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/MCAPrices.csv b/case-studies/hands-on-files/HO5/default/Results/MCAPrices.csv
deleted file mode 100644
index 3f7c97d..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/MCAPrices.csv
+++ /dev/null
@@ -1,169 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.27200000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.07360000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.90800000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.61040000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,3.81600000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.22070000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,2.54400000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,2.14720000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,0.98080000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.20570000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.47480000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.34200000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,2.97130000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.88510000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,1.97120000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,0.50070000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.53340000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.21620000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.30450000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.35110000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.63520000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.86410000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,3.07850000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,0.50390000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.67080000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.12210000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.51000000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.22850000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.04230000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.73120000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,3.35160000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,0.36540000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.91760000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.13290000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.87960000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.24880000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.77910000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.79620000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,3.84390000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,0.39790000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.16920000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.10080000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,3.25680000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.20890000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,6.53190000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.76560000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,4.34650000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,0.35560000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2020.csv
deleted file mode 100644
index 62349ec..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,4.00000000000
-0,2030,R1,2020,gasCCGT,3.00000000000
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2025,R1,2020,windturbine,10.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2025.csv
deleted file mode 100644
index 61a7076..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,28 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasCCGT,2020,4.00000000000
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2025,R1,windturbine,2020,10.00000000000
-2,2030,R1,windturbine,2020,10.00000000000
-2,2035,R1,windturbine,2020,10.00000000000
-2,2040,R1,windturbine,2020,10.00000000000
-2,2045,R1,windturbine,2020,10.00000000000
-2,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2030.csv
deleted file mode 100644
index a4665a3..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,38 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gasCCGT,2020,3.00000000000
-0,2035,R1,gasCCGT,2020,3.00000000000
-0,2040,R1,gasCCGT,2020,3.00000000000
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2030,R1,gasCCGT,2025,4.06670000000
-1,2035,R1,gasCCGT,2025,4.06670000000
-1,2040,R1,gasCCGT,2025,4.06670000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2035,R1,gasCCGT,2030,5.33330000000
-2,2040,R1,gasCCGT,2030,5.33330000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-3,2030,R1,windturbine,2020,10.00000000000
-3,2035,R1,windturbine,2020,10.00000000000
-3,2040,R1,windturbine,2020,10.00000000000
-3,2045,R1,windturbine,2020,10.00000000000
-3,2049,R1,windturbine,2020,10.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2035.csv
deleted file mode 100644
index f6e6035..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,44 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gasCCGT,3.00000000000
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2035,R1,2025,gasCCGT,4.06670000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2035,R1,2030,gasCCGT,5.33330000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2040.csv
deleted file mode 100644
index 4272c86..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,50 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,3.00000000000
-0,2045,R1,2020,gasCCGT,3.00000000000
-0,2049,R1,2020,gasCCGT,3.00000000000
-0,2050,R1,2020,gasCCGT,3.00000000000
-0,2054,R1,2020,gasCCGT,3.00000000000
-0,2055,R1,2020,gasCCGT,3.00000000000
-0,2059,R1,2020,gasCCGT,3.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2040,R1,2025,gasCCGT,4.06670000000
-2,2045,R1,2025,gasCCGT,4.06670000000
-2,2049,R1,2025,gasCCGT,4.06670000000
-2,2050,R1,2025,gasCCGT,4.06670000000
-2,2054,R1,2025,gasCCGT,4.06670000000
-2,2055,R1,2025,gasCCGT,4.06670000000
-2,2059,R1,2025,gasCCGT,4.06670000000
-2,2060,R1,2025,gasCCGT,4.06670000000
-2,2064,R1,2025,gasCCGT,4.06670000000
-4,2040,R1,2030,gasCCGT,5.33330000000
-4,2045,R1,2030,gasCCGT,5.33330000000
-4,2049,R1,2030,gasCCGT,5.33330000000
-4,2050,R1,2030,gasCCGT,5.33330000000
-4,2054,R1,2030,gasCCGT,5.33330000000
-4,2055,R1,2030,gasCCGT,5.33330000000
-4,2059,R1,2030,gasCCGT,5.33330000000
-4,2060,R1,2030,gasCCGT,5.33330000000
-4,2064,R1,2030,gasCCGT,5.33330000000
-4,2065,R1,2030,gasCCGT,5.33330000000
-4,2069,R1,2030,gasCCGT,5.33330000000
-7,2040,R1,2035,windturbine,6.38000000000
-7,2045,R1,2035,windturbine,6.38000000000
-7,2049,R1,2035,windturbine,6.38000000000
-7,2050,R1,2035,windturbine,6.38000000000
-7,2054,R1,2035,windturbine,6.38000000000
-7,2055,R1,2035,windturbine,6.38000000000
-7,2059,R1,2035,windturbine,6.38000000000
-7,2060,R1,2035,windturbine,6.38000000000
-7,2064,R1,2035,windturbine,6.38000000000
-9,2045,R1,2040,windturbine,5.62000000000
-9,2049,R1,2040,windturbine,5.62000000000
-9,2050,R1,2040,windturbine,5.62000000000
-9,2054,R1,2040,windturbine,5.62000000000
-9,2055,R1,2040,windturbine,5.62000000000
-9,2059,R1,2040,windturbine,5.62000000000
-9,2060,R1,2040,windturbine,5.62000000000
-9,2064,R1,2040,windturbine,5.62000000000
-9,2065,R1,2040,windturbine,5.62000000000
-9,2069,R1,2040,windturbine,5.62000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2045.csv
deleted file mode 100644
index cb08e79..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,55 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasCCGT,2020,3.00000000000
-0,2049,R1,gasCCGT,2020,3.00000000000
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2045,R1,gasCCGT,2025,4.06670000000
-1,2049,R1,gasCCGT,2025,4.06670000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2045,R1,gasCCGT,2030,5.33330000000
-2,2049,R1,gasCCGT,2030,5.33330000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-6,2045,R1,windturbine,2020,10.00000000000
-6,2049,R1,windturbine,2020,10.00000000000
-9,2045,R1,windturbine,2035,6.38000000000
-9,2049,R1,windturbine,2035,6.38000000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2045,R1,windturbine,2040,5.62000000000
-10,2049,R1,windturbine,2040,5.62000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2050.csv
deleted file mode 100644
index 95dcafa..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,43 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2050,R1,gasCCGT,2020,3.00000000000
-0,2054,R1,gasCCGT,2020,3.00000000000
-0,2055,R1,gasCCGT,2020,3.00000000000
-0,2059,R1,gasCCGT,2020,3.00000000000
-1,2050,R1,gasCCGT,2025,4.06670000000
-1,2054,R1,gasCCGT,2025,4.06670000000
-1,2055,R1,gasCCGT,2025,4.06670000000
-1,2059,R1,gasCCGT,2025,4.06670000000
-1,2060,R1,gasCCGT,2025,4.06670000000
-1,2064,R1,gasCCGT,2025,4.06670000000
-2,2050,R1,gasCCGT,2030,5.33330000000
-2,2054,R1,gasCCGT,2030,5.33330000000
-2,2055,R1,gasCCGT,2030,5.33330000000
-2,2059,R1,gasCCGT,2030,5.33330000000
-2,2060,R1,gasCCGT,2030,5.33330000000
-2,2064,R1,gasCCGT,2030,5.33330000000
-2,2065,R1,gasCCGT,2030,5.33330000000
-2,2069,R1,gasCCGT,2030,5.33330000000
-9,2050,R1,windturbine,2035,6.38000000000
-9,2054,R1,windturbine,2035,6.38000000000
-9,2055,R1,windturbine,2035,6.38000000000
-9,2059,R1,windturbine,2035,6.38000000000
-9,2060,R1,windturbine,2035,6.38000000000
-9,2064,R1,windturbine,2035,6.38000000000
-10,2050,R1,windturbine,2040,5.62000000000
-10,2054,R1,windturbine,2040,5.62000000000
-10,2055,R1,windturbine,2040,5.62000000000
-10,2059,R1,windturbine,2040,5.62000000000
-10,2060,R1,windturbine,2040,5.62000000000
-10,2064,R1,windturbine,2040,5.62000000000
-10,2065,R1,windturbine,2040,5.62000000000
-10,2069,R1,windturbine,2040,5.62000000000
-11,2050,R1,windturbine,2045,14.10000000000
-11,2054,R1,windturbine,2045,14.10000000000
-11,2055,R1,windturbine,2045,14.10000000000
-11,2059,R1,windturbine,2045,14.10000000000
-11,2060,R1,windturbine,2045,14.10000000000
-11,2064,R1,windturbine,2045,14.10000000000
-11,2065,R1,windturbine,2045,14.10000000000
-11,2069,R1,windturbine,2045,14.10000000000
-11,2070,R1,windturbine,2045,14.10000000000
-11,2074,R1,windturbine,2045,14.10000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index bcdf148..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gasboiler,2020,10.00000000000
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2025,R1,heatpump,2020,19.00000000000
-1,2030,R1,heatpump,2020,19.00000000000
-1,2034,R1,heatpump,2020,19.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 2eb0ec1..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasboiler,2020,5.00000000000
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-2,2025,R1,heatpump,2020,19.00000000000
-2,2030,R1,heatpump,2020,19.00000000000
-2,2034,R1,heatpump,2020,19.00000000000
-3,2030,R1,heatpump,2025,6.90000000000
-3,2034,R1,heatpump,2025,6.90000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index ca300fe..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2030,R1,gasboiler,2025,4.10000000000
-1,2034,R1,gasboiler,2025,4.10000000000
-1,2035,R1,gasboiler,2025,4.10000000000
-1,2039,R1,gasboiler,2025,4.10000000000
-3,2030,R1,heatpump,2020,19.00000000000
-3,2034,R1,heatpump,2020,19.00000000000
-4,2030,R1,heatpump,2025,6.90000000000
-4,2034,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2025,6.90000000000
-4,2039,R1,heatpump,2025,6.90000000000
-5,2035,R1,heatpump,2030,25.00000000000
-5,2039,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2030,25.00000000000
-5,2044,R1,heatpump,2030,25.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 86d65e7..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasboiler,2025,4.10000000000
-0,2039,R1,gasboiler,2025,4.10000000000
-2,2040,R1,gasboiler,2035,0.91000000000
-2,2044,R1,gasboiler,2035,0.91000000000
-2,2045,R1,gasboiler,2035,0.91000000000
-2,2049,R1,gasboiler,2035,0.91000000000
-3,2035,R1,heatpump,2025,6.90000000000
-3,2039,R1,heatpump,2025,6.90000000000
-4,2035,R1,heatpump,2030,25.00000000000
-4,2039,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2030,25.00000000000
-4,2044,R1,heatpump,2030,25.00000000000
-5,2040,R1,heatpump,2035,16.09000000000
-5,2044,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2035,16.09000000000
-5,2049,R1,heatpump,2035,16.09000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 268c65a..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-1,2040,R1,gasboiler,2035,0.91000000000
-1,2044,R1,gasboiler,2035,0.91000000000
-1,2045,R1,gasboiler,2035,0.91000000000
-1,2049,R1,gasboiler,2035,0.91000000000
-3,2040,R1,heatpump,2030,25.00000000000
-3,2044,R1,heatpump,2030,25.00000000000
-4,2040,R1,heatpump,2035,16.09000000000
-4,2044,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2035,16.09000000000
-4,2049,R1,heatpump,2035,16.09000000000
-5,2045,R1,heatpump,2040,31.00000000000
-5,2049,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2040,31.00000000000
-5,2054,R1,heatpump,2040,31.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index 69ea873..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasboiler,2035,0.91000000000
-0,2049,R1,gasboiler,2035,0.91000000000
-3,2045,R1,heatpump,2035,16.09000000000
-3,2049,R1,heatpump,2035,16.09000000000
-4,2045,R1,heatpump,2040,31.00000000000
-4,2049,R1,heatpump,2040,31.00000000000
-4,2050,R1,heatpump,2040,31.00000000000
-4,2054,R1,heatpump,2040,31.00000000000
-5,2050,R1,heatpump,2045,23.00000000000
-5,2054,R1,heatpump,2045,23.00000000000
-5,2055,R1,heatpump,2045,23.00000000000
-5,2059,R1,heatpump,2045,23.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index 3cc1c8d..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-asset,year,region,installed,technology,capacity
-3,2050,R1,2040,heatpump,31.00000000000
-3,2054,R1,2040,heatpump,31.00000000000
-5,2050,R1,2045,heatpump,23.00000000000
-5,2054,R1,2045,heatpump,23.00000000000
-5,2055,R1,2045,heatpump,23.00000000000
-5,2059,R1,2045,heatpump,23.00000000000
-7,2055,R1,2050,heatpump,31.00000000000
-7,2059,R1,2050,heatpump,31.00000000000
-7,2060,R1,2050,heatpump,31.00000000000
-7,2064,R1,2050,heatpump,31.00000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2020.csv
deleted file mode 100644
index f851258..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,0,R1,gasboiler,2020,10.00000000000
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,1,R1,heatpump,2020,10.55560000000
-CO2f,2020,0,R1,gasboiler,2020,647.10000000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2025.csv
deleted file mode 100644
index 8ce7f70..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,0,R1,gasboiler,2020,2.77780000000
-heat,2025,2,R1,heatpump,2020,10.55560000000
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,2,R1,heatpump,2020,10.55560000000
-heat,2030,3,R1,heatpump,2025,3.83330000000
-CO2f,2025,0,R1,gasboiler,2020,179.75000000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2030.csv
deleted file mode 100644
index a6d2019..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,1,R1,gasboiler,2025,2.27780000000
-heat,2030,3,R1,heatpump,2020,10.55560000000
-heat,2030,4,R1,heatpump,2025,3.83330000000
-heat,2035,1,R1,gasboiler,2025,2.27780000000
-heat,2035,4,R1,heatpump,2025,3.83330000000
-heat,2035,5,R1,heatpump,2030,13.88890000000
-CO2f,2030,1,R1,gasboiler,2025,147.39500000000
-CO2f,2035,1,R1,gasboiler,2025,147.39500000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 35ee123..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,0,R1,gasboiler,2025,2.27780000000
-heat,2035,3,R1,heatpump,2025,3.83330000000
-heat,2035,4,R1,heatpump,2030,13.88890000000
-heat,2040,2,R1,gasboiler,2035,0.50560000000
-heat,2040,4,R1,heatpump,2030,13.88890000000
-heat,2040,5,R1,heatpump,2035,8.93890000000
-CO2f,2035,0,R1,gasboiler,2025,147.39500000000
-CO2f,2040,2,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2040.csv
deleted file mode 100644
index 86233ac..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,1,R1,gasboiler,2035,0.50560000000
-heat,2040,3,R1,heatpump,2030,13.88890000000
-heat,2040,4,R1,heatpump,2035,8.93890000000
-heat,2045,1,R1,gasboiler,2035,0.50560000000
-heat,2045,4,R1,heatpump,2035,8.93890000000
-heat,2045,5,R1,heatpump,2040,17.22220000000
-CO2f,2040,1,R1,gasboiler,2035,32.71450000000
-CO2f,2045,1,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 492d52f..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,0,R1,gasboiler,2035,0.50560000000
-heat,2045,3,R1,heatpump,2035,8.93890000000
-heat,2045,4,R1,heatpump,2040,17.22220000000
-heat,2050,4,R1,heatpump,2040,17.22220000000
-heat,2050,5,R1,heatpump,2045,12.77780000000
-CO2f,2045,0,R1,gasboiler,2035,32.71450000000
diff --git a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2050.csv
deleted file mode 100644
index d89c6fe..0000000
--- a/case-studies/hands-on-files/HO5/default/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-commodity,year,asset,region,installed,technology,supply
-heat,2050,3,R1,2040,heatpump,17.22220000000
-heat,2050,5,R1,2045,heatpump,12.77780000000
-heat,2055,5,R1,2045,heatpump,12.77780000000
-heat,2055,7,R1,2050,heatpump,17.22220000000
diff --git a/case-studies/hands-on-files/HO5/default/input/BaseYearExport.csv b/case-studies/hands-on-files/HO5/default/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO5/default/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/input/BaseYearImport.csv b/case-studies/hands-on-files/HO5/default/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO5/default/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO5/default/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO5/default/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO5/default/input/Projections.csv b/case-studies/hands-on-files/HO5/default/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO5/default/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO5/default/settings.toml b/case-studies/hands-on-files/HO5/default/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO5/default/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO5/default/technodata/Agents.csv b/case-studies/hands-on-files/HO5/default/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO5/default/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO5/default/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO5/default/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO5/default/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO5/default/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO5/default/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO5/default/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO5/default/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO5/default/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO5/default/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO5/default/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO5/default/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO5/default/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO5/default/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO5/default/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO5/default/technodata/residential/CommOut.csv
deleted file mode 100644
index 5e5cd62..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,0,0,64.71,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO5/default/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO5/default/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO5/default/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO5/default/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO5/default_final.zip b/case-studies/hands-on-files/HO5/default_final.zip
deleted file mode 100644
index 50af58a..0000000
Binary files a/case-studies/hands-on-files/HO5/default_final.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index 7c5469b..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gassupply1,2020,15.00000000000
-0,2025,R1,gassupply1,2020,51.01130000000
-0,2030,R1,gassupply1,2020,43.51130000000
-0,2035,R1,gassupply1,2020,36.01130000000
-0,2040,R1,gassupply1,2020,36.01130000000
-0,2045,R1,gassupply1,2020,36.01130000000
-0,2050,R1,gassupply1,2020,36.01130000000
-0,2059,R1,gassupply1,2020,36.01130000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index 975377a..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gassupply1,2020,51.01130000000
-0,2030,R1,gassupply1,2020,43.51130000000
-0,2035,R1,gassupply1,2020,36.01130000000
-0,2040,R1,gassupply1,2020,36.01130000000
-0,2045,R1,gassupply1,2020,36.01130000000
-0,2050,R1,gassupply1,2020,36.01130000000
-0,2059,R1,gassupply1,2020,36.01130000000
-1,2030,R1,gassupply1,2025,41.48870000000
-1,2035,R1,gassupply1,2025,41.48870000000
-1,2040,R1,gassupply1,2025,41.48870000000
-1,2045,R1,gassupply1,2025,41.48870000000
-1,2050,R1,gassupply1,2025,41.48870000000
-1,2059,R1,gassupply1,2025,41.48870000000
-1,2060,R1,gassupply1,2025,41.48870000000
-1,2064,R1,gassupply1,2025,41.48870000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 58516e4..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gassupply1,43.51130000000
-0,2035,R1,2020,gassupply1,36.01130000000
-0,2040,R1,2020,gassupply1,36.01130000000
-0,2045,R1,2020,gassupply1,36.01130000000
-0,2050,R1,2020,gassupply1,36.01130000000
-0,2059,R1,2020,gassupply1,36.01130000000
-1,2030,R1,2025,gassupply1,41.48870000000
-1,2035,R1,2025,gassupply1,41.48870000000
-1,2040,R1,2025,gassupply1,41.48870000000
-1,2045,R1,2025,gassupply1,41.48870000000
-1,2050,R1,2025,gassupply1,41.48870000000
-1,2059,R1,2025,gassupply1,41.48870000000
-1,2060,R1,2025,gassupply1,41.48870000000
-1,2064,R1,2025,gassupply1,41.48870000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 793a0ad..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gassupply1,2020,36.01130000000
-0,2040,R1,gassupply1,2020,36.01130000000
-0,2045,R1,gassupply1,2020,36.01130000000
-0,2050,R1,gassupply1,2020,36.01130000000
-0,2059,R1,gassupply1,2020,36.01130000000
-1,2035,R1,gassupply1,2025,41.48870000000
-1,2040,R1,gassupply1,2025,41.48870000000
-1,2045,R1,gassupply1,2025,41.48870000000
-1,2050,R1,gassupply1,2025,41.48870000000
-1,2059,R1,gassupply1,2025,41.48870000000
-1,2060,R1,gassupply1,2025,41.48870000000
-1,2064,R1,gassupply1,2025,41.48870000000
-2,2040,R1,gassupply1,2035,25.00000000000
-2,2045,R1,gassupply1,2035,25.00000000000
-2,2050,R1,gassupply1,2035,25.00000000000
-2,2059,R1,gassupply1,2035,25.00000000000
-2,2060,R1,gassupply1,2035,25.00000000000
-2,2064,R1,gassupply1,2035,25.00000000000
-2,2065,R1,gassupply1,2035,25.00000000000
-2,2074,R1,gassupply1,2035,25.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index 4bbb889..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,19 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gassupply1,36.01130000000
-0,2045,R1,2020,gassupply1,36.01130000000
-0,2050,R1,2020,gassupply1,36.01130000000
-0,2059,R1,2020,gassupply1,36.01130000000
-1,2040,R1,2025,gassupply1,41.48870000000
-1,2045,R1,2025,gassupply1,41.48870000000
-1,2050,R1,2025,gassupply1,41.48870000000
-1,2059,R1,2025,gassupply1,41.48870000000
-1,2060,R1,2025,gassupply1,41.48870000000
-1,2064,R1,2025,gassupply1,41.48870000000
-2,2040,R1,2035,gassupply1,25.00000000000
-2,2045,R1,2035,gassupply1,25.00000000000
-2,2050,R1,2035,gassupply1,25.00000000000
-2,2059,R1,2035,gassupply1,25.00000000000
-2,2060,R1,2035,gassupply1,25.00000000000
-2,2064,R1,2035,gassupply1,25.00000000000
-2,2065,R1,2035,gassupply1,25.00000000000
-2,2074,R1,2035,gassupply1,25.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index e7a7f18..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,24 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gassupply1,2020,36.01130000000
-0,2050,R1,gassupply1,2020,36.01130000000
-0,2059,R1,gassupply1,2020,36.01130000000
-1,2045,R1,gassupply1,2025,41.48870000000
-1,2050,R1,gassupply1,2025,41.48870000000
-1,2059,R1,gassupply1,2025,41.48870000000
-1,2060,R1,gassupply1,2025,41.48870000000
-1,2064,R1,gassupply1,2025,41.48870000000
-2,2045,R1,gassupply1,2035,25.00000000000
-2,2050,R1,gassupply1,2035,25.00000000000
-2,2059,R1,gassupply1,2035,25.00000000000
-2,2060,R1,gassupply1,2035,25.00000000000
-2,2064,R1,gassupply1,2035,25.00000000000
-2,2065,R1,gassupply1,2035,25.00000000000
-2,2074,R1,gassupply1,2035,25.00000000000
-3,2050,R1,gassupply1,2045,25.00000000000
-3,2059,R1,gassupply1,2045,25.00000000000
-3,2060,R1,gassupply1,2045,25.00000000000
-3,2064,R1,gassupply1,2045,25.00000000000
-3,2065,R1,gassupply1,2045,25.00000000000
-3,2074,R1,gassupply1,2045,25.00000000000
-3,2075,R1,gassupply1,2045,25.00000000000
-3,2084,R1,gassupply1,2045,25.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index 2cc9710..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gassupply1,36.01130000000
-0,2055,R1,2020,gassupply1,36.01130000000
-0,2059,R1,2020,gassupply1,36.01130000000
-1,2050,R1,2025,gassupply1,41.48870000000
-1,2055,R1,2025,gassupply1,41.48870000000
-1,2059,R1,2025,gassupply1,41.48870000000
-1,2060,R1,2025,gassupply1,41.48870000000
-1,2064,R1,2025,gassupply1,41.48870000000
-2,2050,R1,2035,gassupply1,25.00000000000
-2,2055,R1,2035,gassupply1,25.00000000000
-2,2059,R1,2035,gassupply1,25.00000000000
-2,2060,R1,2035,gassupply1,25.00000000000
-2,2064,R1,2035,gassupply1,25.00000000000
-2,2065,R1,2035,gassupply1,25.00000000000
-2,2074,R1,2035,gassupply1,25.00000000000
-3,2050,R1,2045,gassupply1,25.00000000000
-3,2055,R1,2045,gassupply1,25.00000000000
-3,2059,R1,2045,gassupply1,25.00000000000
-3,2060,R1,2045,gassupply1,25.00000000000
-3,2064,R1,2045,gassupply1,25.00000000000
-3,2065,R1,2045,gassupply1,25.00000000000
-3,2074,R1,2045,gassupply1,25.00000000000
-3,2075,R1,2045,gassupply1,25.00000000000
-3,2084,R1,2045,gassupply1,25.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/MCACapacity.csv b/case-studies/hands-on-files/HO5/default_final/Results/MCACapacity.csv
deleted file mode 100644
index ef7b8b4..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/MCACapacity.csv
+++ /dev/null
@@ -1,82 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity
-gasboiler,R1,R1,A1,residential,retrofit,2020,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2020,1.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2020,15.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2025,61.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2025,100.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2025,6.00000000000
-windturbine,R1,R1,A1,power,retrofit,2025,10.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2025,51.01130000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,56.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2030,29.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,100.00000000000
-heatpump,R1,R1,A1,residential,retrofit,2030,32.54610000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2030,15.00000000000
-windturbine,R1,R1,A1,power,retrofit,2030,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2030,15.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,43.51130000000
-gassupply1,R1,R1,A1,gas,retrofit,2030,41.48870000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,29.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2035,53.24570000000
-heatpump,R1,R1,A1,residential,retrofit,2035,32.54610000000
-heatpump,R1,R1,A1,residential,retrofit,2035,42.30240000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,15.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2035,5.00000000000
-windturbine,R1,R1,A1,power,retrofit,2035,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2035,15.00000000000
-windturbine,R1,R1,A1,power,retrofit,2035,5.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,36.01130000000
-gassupply1,R1,R1,A1,gas,retrofit,2035,41.48870000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,53.24570000000
-gasboiler,R1,R1,A1,residential,retrofit,2040,38.43400000000
-heatpump,R1,R1,A1,residential,retrofit,2040,42.30240000000
-heatpump,R1,R1,A1,residential,retrofit,2040,48.61030000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,15.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2040,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,15.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,5.00000000000
-windturbine,R1,R1,A1,power,retrofit,2040,10.56440000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,36.01130000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,41.48870000000
-gassupply1,R1,R1,A1,gas,retrofit,2040,25.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,38.43400000000
-gasboiler,R1,R1,A1,residential,retrofit,2045,48.09860000000
-heatpump,R1,R1,A1,residential,retrofit,2045,48.61030000000
-heatpump,R1,R1,A1,residential,retrofit,2045,36.58680000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,15.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2045,5.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,10.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,15.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,5.00000000000
-windturbine,R1,R1,A1,power,retrofit,2045,10.56440000000
-windturbine,R1,R1,A1,power,retrofit,2045,5.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,36.01130000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,41.48870000000
-gassupply1,R1,R1,A1,gas,retrofit,2045,25.00000000000
-gasboiler,R1,R1,A1,residential,retrofit,2050,48.09860000000
-gasboiler,R1,R1,A1,residential,retrofit,2050,43.11450000000
-heatpump,R1,R1,A1,residential,retrofit,2050,36.58680000000
-heatpump,R1,R1,A1,residential,retrofit,2050,55.12520000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,15.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,10.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,5.00000000000
-gasCCGT,R1,R1,A1,power,retrofit,2050,15.00000000000
-windturbine,R1,R1,A1,power,retrofit,2050,15.00000000000
-windturbine,R1,R1,A1,power,retrofit,2050,5.00000000000
-windturbine,R1,R1,A1,power,retrofit,2050,10.56440000000
-windturbine,R1,R1,A1,power,retrofit,2050,5.00000000000
-windturbine,R1,R1,A1,power,retrofit,2050,11.89780000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,36.01130000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,41.48870000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,25.00000000000
-gassupply1,R1,R1,A1,gas,retrofit,2050,25.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/MCAPrices.csv b/case-studies/hands-on-files/HO5/default_final/Results/MCAPrices.csv
deleted file mode 100644
index be05ade..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/MCAPrices.csv
+++ /dev/null
@@ -1,169 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,2.05860000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.06450000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.65880000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,3.81440000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.11950000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,3.07360000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.63380000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.08250000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.12230000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,0.65960000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.02070000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.53150000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,0.83590000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.02620000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.67360000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,5.06940000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.15880000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,4.08490000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,1.39460000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.06450000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.64020000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,2.59890000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.11950000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,1.30090000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.78760000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.08250000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.84500000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,0.44480000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.02070000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.18930000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,0.56400000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.02620000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.24250000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,3.46820000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.15880000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,1.83770000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.88410000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.06450000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,1.05900000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,3.50620000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.11950000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,2.02880000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.41400000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.08250000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.36990000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,0.60160000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.02070000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.33020000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,0.76270000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.02620000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.41990000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,4.67430000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.15880000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,2.76000000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,2.38240000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.06450000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,1.29030000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,4.42960000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.11950000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,2.48060000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,3.05150000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.08250000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.67110000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,0.76120000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.02070000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.40110000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,0.96500000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.02620000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.51030000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,5.90160000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.15880000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,3.38270000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,3.06990000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.06450000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,1.66950000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,5.70370000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.11950000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,3.20430000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,3.93120000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.08250000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.16100000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,0.98150000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.02070000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.51970000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,1.24420000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.02620000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.66110000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,7.59500000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.15880000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,4.36460000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,4.05680000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.06450000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,2.03040000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,7.53370000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.11950000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,3.90390000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,5.19420000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.08250000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.62980000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.29750000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.02070000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,0.63110000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,1.64470000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.02620000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,0.80290000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,10.02840000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.15880000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,5.32360000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2020.csv
deleted file mode 100644
index 54b34ee..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,6.00000000000
-0,2030,R1,2020,gasCCGT,5.00000000000
-0,2035,R1,2020,gasCCGT,5.00000000000
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2025,R1,2020,windturbine,10.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2025.csv
deleted file mode 100644
index be91851..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,35 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasCCGT,2020,6.00000000000
-0,2030,R1,gasCCGT,2020,5.00000000000
-0,2035,R1,gasCCGT,2020,5.00000000000
-0,2040,R1,gasCCGT,2020,5.00000000000
-0,2045,R1,gasCCGT,2020,5.00000000000
-0,2049,R1,gasCCGT,2020,5.00000000000
-0,2050,R1,gasCCGT,2020,5.00000000000
-0,2054,R1,gasCCGT,2020,5.00000000000
-0,2055,R1,gasCCGT,2020,5.00000000000
-0,2059,R1,gasCCGT,2020,5.00000000000
-1,2030,R1,gasCCGT,2025,15.00000000000
-1,2035,R1,gasCCGT,2025,15.00000000000
-1,2040,R1,gasCCGT,2025,15.00000000000
-1,2045,R1,gasCCGT,2025,15.00000000000
-1,2049,R1,gasCCGT,2025,15.00000000000
-1,2050,R1,gasCCGT,2025,15.00000000000
-1,2054,R1,gasCCGT,2025,15.00000000000
-1,2055,R1,gasCCGT,2025,15.00000000000
-1,2059,R1,gasCCGT,2025,15.00000000000
-1,2060,R1,gasCCGT,2025,15.00000000000
-1,2064,R1,gasCCGT,2025,15.00000000000
-2,2025,R1,windturbine,2020,10.00000000000
-2,2030,R1,windturbine,2020,10.00000000000
-2,2035,R1,windturbine,2020,10.00000000000
-2,2040,R1,windturbine,2020,10.00000000000
-2,2045,R1,windturbine,2020,10.00000000000
-2,2049,R1,windturbine,2020,10.00000000000
-3,2030,R1,windturbine,2025,15.00000000000
-3,2035,R1,windturbine,2025,15.00000000000
-3,2040,R1,windturbine,2025,15.00000000000
-3,2045,R1,windturbine,2025,15.00000000000
-3,2049,R1,windturbine,2025,15.00000000000
-3,2050,R1,windturbine,2025,15.00000000000
-3,2054,R1,windturbine,2025,15.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2030.csv
deleted file mode 100644
index 44fc4ae..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,53 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gasCCGT,5.00000000000
-0,2035,R1,2020,gasCCGT,5.00000000000
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2054,R1,2020,gasCCGT,5.00000000000
-0,2055,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2030,R1,2025,gasCCGT,15.00000000000
-2,2035,R1,2025,gasCCGT,15.00000000000
-2,2040,R1,2025,gasCCGT,15.00000000000
-2,2045,R1,2025,gasCCGT,15.00000000000
-2,2049,R1,2025,gasCCGT,15.00000000000
-2,2050,R1,2025,gasCCGT,15.00000000000
-2,2054,R1,2025,gasCCGT,15.00000000000
-2,2055,R1,2025,gasCCGT,15.00000000000
-2,2059,R1,2025,gasCCGT,15.00000000000
-2,2060,R1,2025,gasCCGT,15.00000000000
-2,2064,R1,2025,gasCCGT,15.00000000000
-3,2030,R1,2025,windturbine,15.00000000000
-3,2035,R1,2025,windturbine,15.00000000000
-3,2040,R1,2025,windturbine,15.00000000000
-3,2045,R1,2025,windturbine,15.00000000000
-3,2049,R1,2025,windturbine,15.00000000000
-3,2050,R1,2025,windturbine,15.00000000000
-3,2054,R1,2025,windturbine,15.00000000000
-4,2035,R1,2030,gasCCGT,5.00000000000
-4,2040,R1,2030,gasCCGT,5.00000000000
-4,2045,R1,2030,gasCCGT,5.00000000000
-4,2049,R1,2030,gasCCGT,5.00000000000
-4,2050,R1,2030,gasCCGT,5.00000000000
-4,2054,R1,2030,gasCCGT,5.00000000000
-4,2055,R1,2030,gasCCGT,5.00000000000
-4,2059,R1,2030,gasCCGT,5.00000000000
-4,2060,R1,2030,gasCCGT,5.00000000000
-4,2064,R1,2030,gasCCGT,5.00000000000
-4,2065,R1,2030,gasCCGT,5.00000000000
-4,2069,R1,2030,gasCCGT,5.00000000000
-5,2035,R1,2030,windturbine,5.00000000000
-5,2040,R1,2030,windturbine,5.00000000000
-5,2045,R1,2030,windturbine,5.00000000000
-5,2049,R1,2030,windturbine,5.00000000000
-5,2050,R1,2030,windturbine,5.00000000000
-5,2054,R1,2030,windturbine,5.00000000000
-5,2055,R1,2030,windturbine,5.00000000000
-5,2059,R1,2030,windturbine,5.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2035.csv
deleted file mode 100644
index 889e0ec..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,71 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasCCGT,2020,5.00000000000
-0,2040,R1,gasCCGT,2020,5.00000000000
-0,2045,R1,gasCCGT,2020,5.00000000000
-0,2049,R1,gasCCGT,2020,5.00000000000
-0,2050,R1,gasCCGT,2020,5.00000000000
-0,2054,R1,gasCCGT,2020,5.00000000000
-0,2055,R1,gasCCGT,2020,5.00000000000
-0,2059,R1,gasCCGT,2020,5.00000000000
-1,2035,R1,gasCCGT,2025,15.00000000000
-1,2040,R1,gasCCGT,2025,15.00000000000
-1,2045,R1,gasCCGT,2025,15.00000000000
-1,2049,R1,gasCCGT,2025,15.00000000000
-1,2050,R1,gasCCGT,2025,15.00000000000
-1,2054,R1,gasCCGT,2025,15.00000000000
-1,2055,R1,gasCCGT,2025,15.00000000000
-1,2059,R1,gasCCGT,2025,15.00000000000
-1,2060,R1,gasCCGT,2025,15.00000000000
-1,2064,R1,gasCCGT,2025,15.00000000000
-2,2035,R1,gasCCGT,2030,5.00000000000
-2,2040,R1,gasCCGT,2030,5.00000000000
-2,2045,R1,gasCCGT,2030,5.00000000000
-2,2049,R1,gasCCGT,2030,5.00000000000
-2,2050,R1,gasCCGT,2030,5.00000000000
-2,2054,R1,gasCCGT,2030,5.00000000000
-2,2055,R1,gasCCGT,2030,5.00000000000
-2,2059,R1,gasCCGT,2030,5.00000000000
-2,2060,R1,gasCCGT,2030,5.00000000000
-2,2064,R1,gasCCGT,2030,5.00000000000
-2,2065,R1,gasCCGT,2030,5.00000000000
-2,2069,R1,gasCCGT,2030,5.00000000000
-3,2040,R1,gasCCGT,2035,10.00000000000
-3,2045,R1,gasCCGT,2035,10.00000000000
-3,2049,R1,gasCCGT,2035,10.00000000000
-3,2050,R1,gasCCGT,2035,10.00000000000
-3,2054,R1,gasCCGT,2035,10.00000000000
-3,2055,R1,gasCCGT,2035,10.00000000000
-3,2059,R1,gasCCGT,2035,10.00000000000
-3,2060,R1,gasCCGT,2035,10.00000000000
-3,2064,R1,gasCCGT,2035,10.00000000000
-3,2065,R1,gasCCGT,2035,10.00000000000
-3,2069,R1,gasCCGT,2035,10.00000000000
-3,2070,R1,gasCCGT,2035,10.00000000000
-3,2074,R1,gasCCGT,2035,10.00000000000
-4,2035,R1,windturbine,2020,10.00000000000
-4,2040,R1,windturbine,2020,10.00000000000
-4,2045,R1,windturbine,2020,10.00000000000
-4,2049,R1,windturbine,2020,10.00000000000
-5,2035,R1,windturbine,2025,15.00000000000
-5,2040,R1,windturbine,2025,15.00000000000
-5,2045,R1,windturbine,2025,15.00000000000
-5,2049,R1,windturbine,2025,15.00000000000
-5,2050,R1,windturbine,2025,15.00000000000
-5,2054,R1,windturbine,2025,15.00000000000
-6,2035,R1,windturbine,2030,5.00000000000
-6,2040,R1,windturbine,2030,5.00000000000
-6,2045,R1,windturbine,2030,5.00000000000
-6,2049,R1,windturbine,2030,5.00000000000
-6,2050,R1,windturbine,2030,5.00000000000
-6,2054,R1,windturbine,2030,5.00000000000
-6,2055,R1,windturbine,2030,5.00000000000
-6,2059,R1,windturbine,2030,5.00000000000
-7,2040,R1,windturbine,2035,10.56440000000
-7,2045,R1,windturbine,2035,10.56440000000
-7,2049,R1,windturbine,2035,10.56440000000
-7,2050,R1,windturbine,2035,10.56440000000
-7,2054,R1,windturbine,2035,10.56440000000
-7,2055,R1,windturbine,2035,10.56440000000
-7,2059,R1,windturbine,2035,10.56440000000
-7,2060,R1,windturbine,2035,10.56440000000
-7,2064,R1,windturbine,2035,10.56440000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2040.csv
deleted file mode 100644
index c371fd7..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,89 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2054,R1,2020,gasCCGT,5.00000000000
-0,2055,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2040,R1,2025,gasCCGT,15.00000000000
-2,2045,R1,2025,gasCCGT,15.00000000000
-2,2049,R1,2025,gasCCGT,15.00000000000
-2,2050,R1,2025,gasCCGT,15.00000000000
-2,2054,R1,2025,gasCCGT,15.00000000000
-2,2055,R1,2025,gasCCGT,15.00000000000
-2,2059,R1,2025,gasCCGT,15.00000000000
-2,2060,R1,2025,gasCCGT,15.00000000000
-2,2064,R1,2025,gasCCGT,15.00000000000
-3,2040,R1,2025,windturbine,15.00000000000
-3,2045,R1,2025,windturbine,15.00000000000
-3,2049,R1,2025,windturbine,15.00000000000
-3,2050,R1,2025,windturbine,15.00000000000
-3,2054,R1,2025,windturbine,15.00000000000
-4,2040,R1,2030,gasCCGT,5.00000000000
-4,2045,R1,2030,gasCCGT,5.00000000000
-4,2049,R1,2030,gasCCGT,5.00000000000
-4,2050,R1,2030,gasCCGT,5.00000000000
-4,2054,R1,2030,gasCCGT,5.00000000000
-4,2055,R1,2030,gasCCGT,5.00000000000
-4,2059,R1,2030,gasCCGT,5.00000000000
-4,2060,R1,2030,gasCCGT,5.00000000000
-4,2064,R1,2030,gasCCGT,5.00000000000
-4,2065,R1,2030,gasCCGT,5.00000000000
-4,2069,R1,2030,gasCCGT,5.00000000000
-5,2040,R1,2030,windturbine,5.00000000000
-5,2045,R1,2030,windturbine,5.00000000000
-5,2049,R1,2030,windturbine,5.00000000000
-5,2050,R1,2030,windturbine,5.00000000000
-5,2054,R1,2030,windturbine,5.00000000000
-5,2055,R1,2030,windturbine,5.00000000000
-5,2059,R1,2030,windturbine,5.00000000000
-6,2040,R1,2035,gasCCGT,10.00000000000
-6,2045,R1,2035,gasCCGT,10.00000000000
-6,2049,R1,2035,gasCCGT,10.00000000000
-6,2050,R1,2035,gasCCGT,10.00000000000
-6,2054,R1,2035,gasCCGT,10.00000000000
-6,2055,R1,2035,gasCCGT,10.00000000000
-6,2059,R1,2035,gasCCGT,10.00000000000
-6,2060,R1,2035,gasCCGT,10.00000000000
-6,2064,R1,2035,gasCCGT,10.00000000000
-6,2065,R1,2035,gasCCGT,10.00000000000
-6,2069,R1,2035,gasCCGT,10.00000000000
-6,2070,R1,2035,gasCCGT,10.00000000000
-6,2074,R1,2035,gasCCGT,10.00000000000
-7,2040,R1,2035,windturbine,10.56440000000
-7,2045,R1,2035,windturbine,10.56440000000
-7,2049,R1,2035,windturbine,10.56440000000
-7,2050,R1,2035,windturbine,10.56440000000
-7,2054,R1,2035,windturbine,10.56440000000
-7,2055,R1,2035,windturbine,10.56440000000
-7,2059,R1,2035,windturbine,10.56440000000
-7,2060,R1,2035,windturbine,10.56440000000
-7,2064,R1,2035,windturbine,10.56440000000
-8,2045,R1,2040,gasCCGT,5.00000000000
-8,2049,R1,2040,gasCCGT,5.00000000000
-8,2050,R1,2040,gasCCGT,5.00000000000
-8,2054,R1,2040,gasCCGT,5.00000000000
-8,2055,R1,2040,gasCCGT,5.00000000000
-8,2059,R1,2040,gasCCGT,5.00000000000
-8,2060,R1,2040,gasCCGT,5.00000000000
-8,2064,R1,2040,gasCCGT,5.00000000000
-8,2065,R1,2040,gasCCGT,5.00000000000
-8,2069,R1,2040,gasCCGT,5.00000000000
-8,2070,R1,2040,gasCCGT,5.00000000000
-8,2074,R1,2040,gasCCGT,5.00000000000
-8,2075,R1,2040,gasCCGT,5.00000000000
-8,2079,R1,2040,gasCCGT,5.00000000000
-9,2045,R1,2040,windturbine,5.00000000000
-9,2049,R1,2040,windturbine,5.00000000000
-9,2050,R1,2040,windturbine,5.00000000000
-9,2054,R1,2040,windturbine,5.00000000000
-9,2055,R1,2040,windturbine,5.00000000000
-9,2059,R1,2040,windturbine,5.00000000000
-9,2060,R1,2040,windturbine,5.00000000000
-9,2064,R1,2040,windturbine,5.00000000000
-9,2065,R1,2040,windturbine,5.00000000000
-9,2069,R1,2040,windturbine,5.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2045.csv
deleted file mode 100644
index 314b04f..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,105 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasCCGT,2020,5.00000000000
-0,2049,R1,gasCCGT,2020,5.00000000000
-0,2050,R1,gasCCGT,2020,5.00000000000
-0,2054,R1,gasCCGT,2020,5.00000000000
-0,2055,R1,gasCCGT,2020,5.00000000000
-0,2059,R1,gasCCGT,2020,5.00000000000
-1,2045,R1,gasCCGT,2025,15.00000000000
-1,2049,R1,gasCCGT,2025,15.00000000000
-1,2050,R1,gasCCGT,2025,15.00000000000
-1,2054,R1,gasCCGT,2025,15.00000000000
-1,2055,R1,gasCCGT,2025,15.00000000000
-1,2059,R1,gasCCGT,2025,15.00000000000
-1,2060,R1,gasCCGT,2025,15.00000000000
-1,2064,R1,gasCCGT,2025,15.00000000000
-2,2045,R1,gasCCGT,2030,5.00000000000
-2,2049,R1,gasCCGT,2030,5.00000000000
-2,2050,R1,gasCCGT,2030,5.00000000000
-2,2054,R1,gasCCGT,2030,5.00000000000
-2,2055,R1,gasCCGT,2030,5.00000000000
-2,2059,R1,gasCCGT,2030,5.00000000000
-2,2060,R1,gasCCGT,2030,5.00000000000
-2,2064,R1,gasCCGT,2030,5.00000000000
-2,2065,R1,gasCCGT,2030,5.00000000000
-2,2069,R1,gasCCGT,2030,5.00000000000
-3,2045,R1,gasCCGT,2035,10.00000000000
-3,2049,R1,gasCCGT,2035,10.00000000000
-3,2050,R1,gasCCGT,2035,10.00000000000
-3,2054,R1,gasCCGT,2035,10.00000000000
-3,2055,R1,gasCCGT,2035,10.00000000000
-3,2059,R1,gasCCGT,2035,10.00000000000
-3,2060,R1,gasCCGT,2035,10.00000000000
-3,2064,R1,gasCCGT,2035,10.00000000000
-3,2065,R1,gasCCGT,2035,10.00000000000
-3,2069,R1,gasCCGT,2035,10.00000000000
-3,2070,R1,gasCCGT,2035,10.00000000000
-3,2074,R1,gasCCGT,2035,10.00000000000
-4,2045,R1,gasCCGT,2040,5.00000000000
-4,2049,R1,gasCCGT,2040,5.00000000000
-4,2050,R1,gasCCGT,2040,5.00000000000
-4,2054,R1,gasCCGT,2040,5.00000000000
-4,2055,R1,gasCCGT,2040,5.00000000000
-4,2059,R1,gasCCGT,2040,5.00000000000
-4,2060,R1,gasCCGT,2040,5.00000000000
-4,2064,R1,gasCCGT,2040,5.00000000000
-4,2065,R1,gasCCGT,2040,5.00000000000
-4,2069,R1,gasCCGT,2040,5.00000000000
-4,2070,R1,gasCCGT,2040,5.00000000000
-4,2074,R1,gasCCGT,2040,5.00000000000
-4,2075,R1,gasCCGT,2040,5.00000000000
-4,2079,R1,gasCCGT,2040,5.00000000000
-5,2050,R1,gasCCGT,2045,15.00000000000
-5,2054,R1,gasCCGT,2045,15.00000000000
-5,2055,R1,gasCCGT,2045,15.00000000000
-5,2059,R1,gasCCGT,2045,15.00000000000
-5,2060,R1,gasCCGT,2045,15.00000000000
-5,2064,R1,gasCCGT,2045,15.00000000000
-5,2065,R1,gasCCGT,2045,15.00000000000
-5,2069,R1,gasCCGT,2045,15.00000000000
-5,2070,R1,gasCCGT,2045,15.00000000000
-5,2074,R1,gasCCGT,2045,15.00000000000
-5,2075,R1,gasCCGT,2045,15.00000000000
-5,2079,R1,gasCCGT,2045,15.00000000000
-5,2080,R1,gasCCGT,2045,15.00000000000
-5,2084,R1,gasCCGT,2045,15.00000000000
-6,2045,R1,windturbine,2020,10.00000000000
-6,2049,R1,windturbine,2020,10.00000000000
-7,2045,R1,windturbine,2025,15.00000000000
-7,2049,R1,windturbine,2025,15.00000000000
-7,2050,R1,windturbine,2025,15.00000000000
-7,2054,R1,windturbine,2025,15.00000000000
-8,2045,R1,windturbine,2030,5.00000000000
-8,2049,R1,windturbine,2030,5.00000000000
-8,2050,R1,windturbine,2030,5.00000000000
-8,2054,R1,windturbine,2030,5.00000000000
-8,2055,R1,windturbine,2030,5.00000000000
-8,2059,R1,windturbine,2030,5.00000000000
-9,2045,R1,windturbine,2035,10.56440000000
-9,2049,R1,windturbine,2035,10.56440000000
-9,2050,R1,windturbine,2035,10.56440000000
-9,2054,R1,windturbine,2035,10.56440000000
-9,2055,R1,windturbine,2035,10.56440000000
-9,2059,R1,windturbine,2035,10.56440000000
-9,2060,R1,windturbine,2035,10.56440000000
-9,2064,R1,windturbine,2035,10.56440000000
-10,2045,R1,windturbine,2040,5.00000000000
-10,2049,R1,windturbine,2040,5.00000000000
-10,2050,R1,windturbine,2040,5.00000000000
-10,2054,R1,windturbine,2040,5.00000000000
-10,2055,R1,windturbine,2040,5.00000000000
-10,2059,R1,windturbine,2040,5.00000000000
-10,2060,R1,windturbine,2040,5.00000000000
-10,2064,R1,windturbine,2040,5.00000000000
-10,2065,R1,windturbine,2040,5.00000000000
-10,2069,R1,windturbine,2040,5.00000000000
-11,2050,R1,windturbine,2045,11.89780000000
-11,2054,R1,windturbine,2045,11.89780000000
-11,2055,R1,windturbine,2045,11.89780000000
-11,2059,R1,windturbine,2045,11.89780000000
-11,2060,R1,windturbine,2045,11.89780000000
-11,2064,R1,windturbine,2045,11.89780000000
-11,2065,R1,windturbine,2045,11.89780000000
-11,2069,R1,windturbine,2045,11.89780000000
-11,2070,R1,windturbine,2045,11.89780000000
-11,2074,R1,windturbine,2045,11.89780000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2050.csv
deleted file mode 100644
index e0535b5..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,109 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2054,R1,2020,gasCCGT,5.00000000000
-0,2055,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-2,2050,R1,2025,gasCCGT,15.00000000000
-2,2054,R1,2025,gasCCGT,15.00000000000
-2,2055,R1,2025,gasCCGT,15.00000000000
-2,2059,R1,2025,gasCCGT,15.00000000000
-2,2060,R1,2025,gasCCGT,15.00000000000
-2,2064,R1,2025,gasCCGT,15.00000000000
-3,2050,R1,2025,windturbine,15.00000000000
-3,2054,R1,2025,windturbine,15.00000000000
-4,2050,R1,2030,gasCCGT,5.00000000000
-4,2054,R1,2030,gasCCGT,5.00000000000
-4,2055,R1,2030,gasCCGT,5.00000000000
-4,2059,R1,2030,gasCCGT,5.00000000000
-4,2060,R1,2030,gasCCGT,5.00000000000
-4,2064,R1,2030,gasCCGT,5.00000000000
-4,2065,R1,2030,gasCCGT,5.00000000000
-4,2069,R1,2030,gasCCGT,5.00000000000
-5,2050,R1,2030,windturbine,5.00000000000
-5,2054,R1,2030,windturbine,5.00000000000
-5,2055,R1,2030,windturbine,5.00000000000
-5,2059,R1,2030,windturbine,5.00000000000
-6,2050,R1,2035,gasCCGT,10.00000000000
-6,2054,R1,2035,gasCCGT,10.00000000000
-6,2055,R1,2035,gasCCGT,10.00000000000
-6,2059,R1,2035,gasCCGT,10.00000000000
-6,2060,R1,2035,gasCCGT,10.00000000000
-6,2064,R1,2035,gasCCGT,10.00000000000
-6,2065,R1,2035,gasCCGT,10.00000000000
-6,2069,R1,2035,gasCCGT,10.00000000000
-6,2070,R1,2035,gasCCGT,10.00000000000
-6,2074,R1,2035,gasCCGT,10.00000000000
-7,2050,R1,2035,windturbine,10.56440000000
-7,2054,R1,2035,windturbine,10.56440000000
-7,2055,R1,2035,windturbine,10.56440000000
-7,2059,R1,2035,windturbine,10.56440000000
-7,2060,R1,2035,windturbine,10.56440000000
-7,2064,R1,2035,windturbine,10.56440000000
-8,2050,R1,2040,gasCCGT,5.00000000000
-8,2054,R1,2040,gasCCGT,5.00000000000
-8,2055,R1,2040,gasCCGT,5.00000000000
-8,2059,R1,2040,gasCCGT,5.00000000000
-8,2060,R1,2040,gasCCGT,5.00000000000
-8,2064,R1,2040,gasCCGT,5.00000000000
-8,2065,R1,2040,gasCCGT,5.00000000000
-8,2069,R1,2040,gasCCGT,5.00000000000
-8,2070,R1,2040,gasCCGT,5.00000000000
-8,2074,R1,2040,gasCCGT,5.00000000000
-8,2075,R1,2040,gasCCGT,5.00000000000
-8,2079,R1,2040,gasCCGT,5.00000000000
-9,2050,R1,2040,windturbine,5.00000000000
-9,2054,R1,2040,windturbine,5.00000000000
-9,2055,R1,2040,windturbine,5.00000000000
-9,2059,R1,2040,windturbine,5.00000000000
-9,2060,R1,2040,windturbine,5.00000000000
-9,2064,R1,2040,windturbine,5.00000000000
-9,2065,R1,2040,windturbine,5.00000000000
-9,2069,R1,2040,windturbine,5.00000000000
-10,2050,R1,2045,gasCCGT,15.00000000000
-10,2054,R1,2045,gasCCGT,15.00000000000
-10,2055,R1,2045,gasCCGT,15.00000000000
-10,2059,R1,2045,gasCCGT,15.00000000000
-10,2060,R1,2045,gasCCGT,15.00000000000
-10,2064,R1,2045,gasCCGT,15.00000000000
-10,2065,R1,2045,gasCCGT,15.00000000000
-10,2069,R1,2045,gasCCGT,15.00000000000
-10,2070,R1,2045,gasCCGT,15.00000000000
-10,2074,R1,2045,gasCCGT,15.00000000000
-10,2075,R1,2045,gasCCGT,15.00000000000
-10,2079,R1,2045,gasCCGT,15.00000000000
-10,2080,R1,2045,gasCCGT,15.00000000000
-10,2084,R1,2045,gasCCGT,15.00000000000
-11,2050,R1,2045,windturbine,11.89780000000
-11,2054,R1,2045,windturbine,11.89780000000
-11,2055,R1,2045,windturbine,11.89780000000
-11,2059,R1,2045,windturbine,11.89780000000
-11,2060,R1,2045,windturbine,11.89780000000
-11,2064,R1,2045,windturbine,11.89780000000
-11,2065,R1,2045,windturbine,11.89780000000
-11,2069,R1,2045,windturbine,11.89780000000
-11,2070,R1,2045,windturbine,11.89780000000
-11,2074,R1,2045,windturbine,11.89780000000
-12,2055,R1,2050,gasCCGT,5.00000000000
-12,2059,R1,2050,gasCCGT,5.00000000000
-12,2060,R1,2050,gasCCGT,5.00000000000
-12,2064,R1,2050,gasCCGT,5.00000000000
-12,2065,R1,2050,gasCCGT,5.00000000000
-12,2069,R1,2050,gasCCGT,5.00000000000
-12,2070,R1,2050,gasCCGT,5.00000000000
-12,2074,R1,2050,gasCCGT,5.00000000000
-12,2075,R1,2050,gasCCGT,5.00000000000
-12,2079,R1,2050,gasCCGT,5.00000000000
-12,2080,R1,2050,gasCCGT,5.00000000000
-12,2084,R1,2050,gasCCGT,5.00000000000
-12,2085,R1,2050,gasCCGT,5.00000000000
-12,2089,R1,2050,gasCCGT,5.00000000000
-13,2055,R1,2050,windturbine,6.77140000000
-13,2059,R1,2050,windturbine,6.77140000000
-13,2060,R1,2050,windturbine,6.77140000000
-13,2064,R1,2050,windturbine,6.77140000000
-13,2065,R1,2050,windturbine,6.77140000000
-13,2069,R1,2050,windturbine,6.77140000000
-13,2070,R1,2050,windturbine,6.77140000000
-13,2074,R1,2050,windturbine,6.77140000000
-13,2075,R1,2050,windturbine,6.77140000000
-13,2079,R1,2050,windturbine,6.77140000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index 77d60cf..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gasboiler,2020,10.00000000000
-0,2025,R1,gasboiler,2020,61.00000000000
-0,2030,R1,gasboiler,2020,56.00000000000
-0,2034,R1,gasboiler,2020,56.00000000000
-1,2025,R1,heatpump,2020,100.00000000000
-1,2030,R1,heatpump,2020,100.00000000000
-1,2034,R1,heatpump,2020,100.00000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 9bfa6dd..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasboiler,2020,61.00000000000
-0,2030,R1,gasboiler,2020,56.00000000000
-0,2034,R1,gasboiler,2020,56.00000000000
-1,2030,R1,gasboiler,2025,29.00000000000
-1,2034,R1,gasboiler,2025,29.00000000000
-1,2035,R1,gasboiler,2025,29.00000000000
-1,2039,R1,gasboiler,2025,29.00000000000
-2,2025,R1,heatpump,2020,100.00000000000
-2,2030,R1,heatpump,2020,100.00000000000
-2,2034,R1,heatpump,2020,100.00000000000
-3,2030,R1,heatpump,2025,32.54610000000
-3,2034,R1,heatpump,2025,32.54610000000
-3,2035,R1,heatpump,2025,32.54610000000
-3,2039,R1,heatpump,2025,32.54610000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index 95a40dd..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gasboiler,2020,56.00000000000
-0,2034,R1,gasboiler,2020,56.00000000000
-1,2030,R1,gasboiler,2025,29.00000000000
-1,2034,R1,gasboiler,2025,29.00000000000
-1,2035,R1,gasboiler,2025,29.00000000000
-1,2039,R1,gasboiler,2025,29.00000000000
-2,2035,R1,gasboiler,2030,53.24570000000
-2,2039,R1,gasboiler,2030,53.24570000000
-2,2040,R1,gasboiler,2030,53.24570000000
-2,2044,R1,gasboiler,2030,53.24570000000
-3,2030,R1,heatpump,2020,100.00000000000
-3,2034,R1,heatpump,2020,100.00000000000
-4,2030,R1,heatpump,2025,32.54610000000
-4,2034,R1,heatpump,2025,32.54610000000
-4,2035,R1,heatpump,2025,32.54610000000
-4,2039,R1,heatpump,2025,32.54610000000
-5,2035,R1,heatpump,2030,42.30240000000
-5,2039,R1,heatpump,2030,42.30240000000
-5,2040,R1,heatpump,2030,42.30240000000
-5,2044,R1,heatpump,2030,42.30240000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 4c064e7..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasboiler,2025,29.00000000000
-0,2039,R1,gasboiler,2025,29.00000000000
-1,2035,R1,gasboiler,2030,53.24570000000
-1,2039,R1,gasboiler,2030,53.24570000000
-1,2040,R1,gasboiler,2030,53.24570000000
-1,2044,R1,gasboiler,2030,53.24570000000
-2,2040,R1,gasboiler,2035,38.43400000000
-2,2044,R1,gasboiler,2035,38.43400000000
-2,2045,R1,gasboiler,2035,38.43400000000
-2,2049,R1,gasboiler,2035,38.43400000000
-3,2035,R1,heatpump,2025,32.54610000000
-3,2039,R1,heatpump,2025,32.54610000000
-4,2035,R1,heatpump,2030,42.30240000000
-4,2039,R1,heatpump,2030,42.30240000000
-4,2040,R1,heatpump,2030,42.30240000000
-4,2044,R1,heatpump,2030,42.30240000000
-5,2040,R1,heatpump,2035,48.61030000000
-5,2044,R1,heatpump,2035,48.61030000000
-5,2045,R1,heatpump,2035,48.61030000000
-5,2049,R1,heatpump,2035,48.61030000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index ac6be6a..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2040,R1,gasboiler,2030,53.24570000000
-0,2044,R1,gasboiler,2030,53.24570000000
-1,2040,R1,gasboiler,2035,38.43400000000
-1,2044,R1,gasboiler,2035,38.43400000000
-1,2045,R1,gasboiler,2035,38.43400000000
-1,2049,R1,gasboiler,2035,38.43400000000
-2,2045,R1,gasboiler,2040,48.09860000000
-2,2049,R1,gasboiler,2040,48.09860000000
-2,2050,R1,gasboiler,2040,48.09860000000
-2,2054,R1,gasboiler,2040,48.09860000000
-3,2040,R1,heatpump,2030,42.30240000000
-3,2044,R1,heatpump,2030,42.30240000000
-4,2040,R1,heatpump,2035,48.61030000000
-4,2044,R1,heatpump,2035,48.61030000000
-4,2045,R1,heatpump,2035,48.61030000000
-4,2049,R1,heatpump,2035,48.61030000000
-5,2045,R1,heatpump,2040,36.58680000000
-5,2049,R1,heatpump,2040,36.58680000000
-5,2050,R1,heatpump,2040,36.58680000000
-5,2054,R1,heatpump,2040,36.58680000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index a37d716..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasboiler,2035,38.43400000000
-0,2049,R1,gasboiler,2035,38.43400000000
-1,2045,R1,gasboiler,2040,48.09860000000
-1,2049,R1,gasboiler,2040,48.09860000000
-1,2050,R1,gasboiler,2040,48.09860000000
-1,2054,R1,gasboiler,2040,48.09860000000
-2,2050,R1,gasboiler,2045,43.11450000000
-2,2054,R1,gasboiler,2045,43.11450000000
-2,2055,R1,gasboiler,2045,43.11450000000
-2,2059,R1,gasboiler,2045,43.11450000000
-3,2045,R1,heatpump,2035,48.61030000000
-3,2049,R1,heatpump,2035,48.61030000000
-4,2045,R1,heatpump,2040,36.58680000000
-4,2049,R1,heatpump,2040,36.58680000000
-4,2050,R1,heatpump,2040,36.58680000000
-4,2054,R1,heatpump,2040,36.58680000000
-5,2050,R1,heatpump,2045,55.12520000000
-5,2054,R1,heatpump,2045,55.12520000000
-5,2055,R1,heatpump,2045,55.12520000000
-5,2059,R1,heatpump,2045,55.12520000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index 7195712..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2050,R1,gasboiler,2040,48.09860000000
-0,2054,R1,gasboiler,2040,48.09860000000
-1,2050,R1,gasboiler,2045,43.11450000000
-1,2054,R1,gasboiler,2045,43.11450000000
-1,2055,R1,gasboiler,2045,43.11450000000
-1,2059,R1,gasboiler,2045,43.11450000000
-2,2055,R1,gasboiler,2050,33.44280000000
-2,2059,R1,gasboiler,2050,33.44280000000
-2,2060,R1,gasboiler,2050,33.44280000000
-2,2064,R1,gasboiler,2050,33.44280000000
-3,2050,R1,heatpump,2040,36.58680000000
-3,2054,R1,heatpump,2040,36.58680000000
-4,2050,R1,heatpump,2045,55.12520000000
-4,2054,R1,heatpump,2045,55.12520000000
-4,2055,R1,heatpump,2045,55.12520000000
-4,2059,R1,heatpump,2045,55.12520000000
-5,2055,R1,heatpump,2050,31.20960000000
-5,2059,R1,heatpump,2050,31.20960000000
-5,2060,R1,heatpump,2050,31.20960000000
-5,2064,R1,heatpump,2050,31.20960000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2020.csv
deleted file mode 100644
index 008803c..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,0,R1,gasboiler,2020,10.00000000000
-heat,2025,0,R1,gasboiler,2020,61.00000000000
-heat,2025,1,R1,heatpump,2020,100.00000000000
-CO2f,2020,0,R1,gasboiler,2020,647.10000000000
-CO2f,2025,0,R1,gasboiler,2020,3947.31000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2025.csv
deleted file mode 100644
index b8252bf..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,0,R1,gasboiler,2020,61.00000000000
-heat,2025,2,R1,heatpump,2020,100.00000000000
-heat,2030,0,R1,gasboiler,2020,56.00000000000
-heat,2030,1,R1,gasboiler,2025,29.00000000000
-heat,2030,2,R1,heatpump,2020,100.00000000000
-heat,2030,3,R1,heatpump,2025,32.54610000000
-CO2f,2025,0,R1,gasboiler,2020,3947.31000000000
-CO2f,2030,0,R1,gasboiler,2020,3623.76000000000
-CO2f,2030,1,R1,gasboiler,2025,1876.59000000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2030.csv
deleted file mode 100644
index 102c981..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,0,R1,gasboiler,2020,56.00000000000
-heat,2030,1,R1,gasboiler,2025,29.00000000000
-heat,2030,3,R1,heatpump,2020,100.00000000000
-heat,2030,4,R1,heatpump,2025,32.54610000000
-heat,2035,1,R1,gasboiler,2025,29.00000000000
-heat,2035,2,R1,gasboiler,2030,53.24570000000
-heat,2035,4,R1,heatpump,2025,32.54610000000
-heat,2035,5,R1,heatpump,2030,42.30240000000
-CO2f,2030,0,R1,gasboiler,2020,3623.76000000000
-CO2f,2030,1,R1,gasboiler,2025,1876.59000000000
-CO2f,2035,1,R1,gasboiler,2025,1876.59000000000
-CO2f,2035,2,R1,gasboiler,2030,3445.52680000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 385992d..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,0,R1,gasboiler,2025,29.00000000000
-heat,2035,1,R1,gasboiler,2030,53.24570000000
-heat,2035,3,R1,heatpump,2025,32.54610000000
-heat,2035,4,R1,heatpump,2030,42.30240000000
-heat,2040,1,R1,gasboiler,2030,53.24570000000
-heat,2040,2,R1,gasboiler,2035,38.43400000000
-heat,2040,4,R1,heatpump,2030,42.30240000000
-heat,2040,5,R1,heatpump,2035,48.61030000000
-CO2f,2035,0,R1,gasboiler,2025,1876.59000000000
-CO2f,2035,1,R1,gasboiler,2030,3445.52680000000
-CO2f,2040,1,R1,gasboiler,2030,3445.52680000000
-CO2f,2040,2,R1,gasboiler,2035,2487.06230000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2040.csv
deleted file mode 100644
index 22c4ba8..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,0,R1,gasboiler,2030,53.24570000000
-heat,2040,1,R1,gasboiler,2035,38.43400000000
-heat,2040,3,R1,heatpump,2030,42.30240000000
-heat,2040,4,R1,heatpump,2035,48.61030000000
-heat,2045,1,R1,gasboiler,2035,38.43400000000
-heat,2045,2,R1,gasboiler,2040,48.09860000000
-heat,2045,4,R1,heatpump,2035,48.61030000000
-heat,2045,5,R1,heatpump,2040,36.58680000000
-CO2f,2040,0,R1,gasboiler,2030,3445.52680000000
-CO2f,2040,1,R1,gasboiler,2035,2487.06230000000
-CO2f,2045,1,R1,gasboiler,2035,2487.06230000000
-CO2f,2045,2,R1,gasboiler,2040,3112.46190000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 55fe2cc..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,0,R1,gasboiler,2035,38.43400000000
-heat,2045,1,R1,gasboiler,2040,48.09860000000
-heat,2045,3,R1,heatpump,2035,48.61030000000
-heat,2045,4,R1,heatpump,2040,36.58680000000
-heat,2050,1,R1,gasboiler,2040,48.09860000000
-heat,2050,2,R1,gasboiler,2045,43.11450000000
-heat,2050,4,R1,heatpump,2040,36.58680000000
-heat,2050,5,R1,heatpump,2045,55.12520000000
-CO2f,2045,0,R1,gasboiler,2035,2487.06230000000
-CO2f,2045,1,R1,gasboiler,2040,3112.46190000000
-CO2f,2050,1,R1,gasboiler,2040,3112.46190000000
-CO2f,2050,2,R1,gasboiler,2045,2789.93880000000
diff --git a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2050.csv
deleted file mode 100644
index eb0846b..0000000
--- a/case-studies/hands-on-files/HO5/default_final/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2050,0,R1,gasboiler,2040,48.09860000000
-heat,2050,1,R1,gasboiler,2045,43.11450000000
-heat,2050,3,R1,heatpump,2040,36.58680000000
-heat,2050,4,R1,heatpump,2045,55.12520000000
-heat,2055,1,R1,gasboiler,2045,43.11450000000
-heat,2055,2,R1,gasboiler,2050,33.44280000000
-heat,2055,4,R1,heatpump,2045,55.12520000000
-heat,2055,5,R1,heatpump,2050,31.20960000000
-CO2f,2050,0,R1,gasboiler,2040,3112.46190000000
-CO2f,2050,1,R1,gasboiler,2045,2789.93880000000
-CO2f,2055,1,R1,gasboiler,2045,2789.93880000000
-CO2f,2055,2,R1,gasboiler,2050,2164.08060000000
diff --git a/case-studies/hands-on-files/HO5/default_final/input/BaseYearExport.csv b/case-studies/hands-on-files/HO5/default_final/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO5/default_final/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/input/BaseYearImport.csv b/case-studies/hands-on-files/HO5/default_final/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO5/default_final/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO5/default_final/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO5/default_final/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO5/default_final/input/Projections.csv b/case-studies/hands-on-files/HO5/default_final/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO5/default_final/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO5/default_final/settings.toml b/case-studies/hands-on-files/HO5/default_final/settings.toml
deleted file mode 100644
index 094b7c0..0000000
--- a/case-studies/hands-on-files/HO5/default_final/settings.toml
+++ /dev/null
@@ -1,149 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-
-timeslice_shares_path = '{path}/technodata/preset/TimesliceSharepreset.csv'
-macrodrivers_path = '{path}/technodata/preset/Macrodrivers.csv'
-regression_path = '{path}/technodata/preset/regressionparameters.csv'
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/Agents.csv b/case-studies/hands-on-files/HO5/default_final/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO5/default_final/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO5/default_final/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO5/default_final/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO5/default_final/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO5/default_final/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO5/default_final/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO5/default_final/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO5/default_final/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/preset/Macrodrivers.csv b/case-studies/hands-on-files/HO5/default_final/technodata/preset/Macrodrivers.csv
deleted file mode 100644
index 196b1f2..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/preset/Macrodrivers.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-Variable,RegionName,Unit,2010,2011,2012,2013,2014,2015,2016,2017,2018,2019,2020,2021,2022,2023,2024,2025,2026,2027,2028,2029,2030,2031,2032,2033,2034,2035,2036,2037,2038,2039,2040,2041,2042,2043,2044,2045,2046,2047,2048,2049,2050,2051,2052,2053,2054,2055,2056,2057,2058,2059,2060,2061,2062,2063,2064,2065,2066,2067,2068,2069,2070,2071,2072,2073,2074,2075,2076,2077,2078,2079,2080,2081,2082,2083,2084,2085,2086,2087,2088,2089,2090,2091,2092,2093,2094,2095,2096,2097,2098,2099,2100,2101,2102,2103,2104,2105,2106,2107,2108,2109,2110
-GDP|PPP,R1,millionUS$2005,1206919,1220599,1234278,1247958,1261637,1275317,1288997,1302676,1316356,1330035,1343715,1384465,1425215,1465965,1506715,1547465,1588215,1628965,1669715,1710465,1751215,1796887,1842558,1888230,1933901,1979573,2025244,2070916,2116587,2162259,2207930,2257386,2306842,2356297,2405753,2455209,2504665,2554120,2603576,2653032,2702488,2758342,2814196,2870050,2925904,2981758,3037612,3093466,3149320,3205175,3261029,3330014,3398999,3467984,3536969,3605954,3674939,3743925,3812910,3881895,3950880,4017445,4084009,4150574,4217139,4283704,4350268,4416833,4483398,4549962,4616527,4681288,4746048,4810808,4875569,4940329,5005090,5069850,5134610,5199371,5264131,5326189,5388246,5450304,5512362,5574419,5636477,5698534,5760592,5822650,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707,5884707
-Population,R1,million,80.0042,80.9151,81.82599,82.73689,83.64779,84.55868,85.46958,86.38048,87.29137,88.20227,89.11317,89.83065,90.54813,91.26561,91.98309,92.70057,93.41805,94.13553,94.85301,95.57049,96.28797,96.82205,97.35612,97.89019,98.42427,98.95834,99.49242,100.0265,100.5606,101.0946,101.6287,101.971,102.3133,102.6556,102.9979,103.3402,103.6825,104.0247,104.367,104.7093,105.0516,105.1876,105.3236,105.4596,105.5956,105.7316,105.8676,106.0036,106.1396,106.2756,106.4116,106.3854,106.3591,106.3328,106.3065,106.2802,106.2539,106.2276,106.2014,106.1751,106.1488,105.9819,105.815,105.6482,105.4813,105.3144,105.1476,104.9807,104.8138,104.6469,104.4801,104.2169,103.9538,103.6906,103.4275,103.1643,102.9012,102.638,102.3749,102.1117,101.8486,101.5358,101.223,100.9102,100.5974,100.2846,99.97179,99.65899,99.34619,99.03339,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059,98.72059
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/preset/TimesliceSharepreset.csv b/case-studies/hands-on-files/HO5/default_final/technodata/preset/TimesliceSharepreset.csv
deleted file mode 100644
index 47bf41e..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/preset/TimesliceSharepreset.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-SN,RegionName,electricity,gas,heat,CO2f,wind
-1,R1,0,0,0.034835,0,0
-2,R1,0,0,0.064546,0,0
-3,R1,0,0,0.044569,0,0
-4,R1,0,0,0.011161,0,0
-5,R1,0,0,0.014145,0,0
-6,R1,0,0,0.085783,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/preset/regressionparameters.csv b/case-studies/hands-on-files/HO5/default_final/technodata/preset/regressionparameters.csv
deleted file mode 100644
index 4742f3b..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/preset/regressionparameters.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-SectorName,FunctionType,Coeff,RegionName,electricity,gas,heat,CO2f
-Residential,logistic-sigmoid,GDPexp,R1,0,0,9.94E-02,0
-Residential,logistic-sigmoid,constant,R1,0,0,0.0000434,0
-Residential,logistic-sigmoid,GDPscaleLess,R1,0,0,753.1068725,0
-Residential,logistic-sigmoid,GDPscaleGreater,R1,0,0,672.9316672,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO5/default_final/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO5/default_final/technodata/residential/CommOut.csv
deleted file mode 100644
index 5e5cd62..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,0,0,64.71,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO5/default_final/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO5/default_final/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO5/default_final/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO5/default_final/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO5/regressionparameters.csv b/case-studies/hands-on-files/HO5/regressionparameters.csv
deleted file mode 100644
index 4742f3b..0000000
--- a/case-studies/hands-on-files/HO5/regressionparameters.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-SectorName,FunctionType,Coeff,RegionName,electricity,gas,heat,CO2f
-Residential,logistic-sigmoid,GDPexp,R1,0,0,9.94E-02,0
-Residential,logistic-sigmoid,constant,R1,0,0,0.0000434,0
-Residential,logistic-sigmoid,GDPscaleLess,R1,0,0,753.1068725,0
-Residential,logistic-sigmoid,GDPscaleGreater,R1,0,0,672.9316672,0
diff --git a/case-studies/hands-on-files/HO6/MCACapacity.xls b/case-studies/hands-on-files/HO6/MCACapacity.xls
deleted file mode 100644
index c6734e5..0000000
Binary files a/case-studies/hands-on-files/HO6/MCACapacity.xls and /dev/null differ
diff --git a/case-studies/hands-on-files/HO6/default.zip b/case-studies/hands-on-files/HO6/default.zip
deleted file mode 100644
index 82a1074..0000000
Binary files a/case-studies/hands-on-files/HO6/default.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO6/default/input/BaseYearExport.csv b/case-studies/hands-on-files/HO6/default/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO6/default/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/input/BaseYearImport.csv b/case-studies/hands-on-files/HO6/default/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO6/default/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO6/default/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO6/default/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO6/default/input/Projections.csv b/case-studies/hands-on-files/HO6/default/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO6/default/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO6/default/settings.toml b/case-studies/hands-on-files/HO6/default/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO6/default/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO6/default/technodata/Agents.csv b/case-studies/hands-on-files/HO6/default/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO6/default/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO6/default/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO6/default/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO6/default/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO6/default/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO6/default/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO6/default/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO6/default/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO6/default/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO6/default/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO6/default/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO6/default/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO6/default/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO6/default/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO6/default/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO6/default/technodata/residential/CommOut.csv
deleted file mode 100644
index 5e5cd62..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,0,0,64.71,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO6/default/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO6/default/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO6/default/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO6/default/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO6/default_final.zip b/case-studies/hands-on-files/HO6/default_final.zip
deleted file mode 100644
index e1e3360..0000000
Binary files a/case-studies/hands-on-files/HO6/default_final.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index b2baff7..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,9 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gassupply1,2020,15.00000000000
-0,2025,R1,gassupply1,2020,21.38650000000
-0,2030,R1,gassupply1,2020,13.88650000000
-0,2035,R1,gassupply1,2020,6.38650000000
-0,2040,R1,gassupply1,2020,6.38650000000
-0,2045,R1,gassupply1,2020,6.38650000000
-0,2050,R1,gassupply1,2020,6.38650000000
-0,2059,R1,gassupply1,2020,6.38650000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index eaf9429..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gassupply1,2020,21.38650000000
-0,2030,R1,gassupply1,2020,13.88650000000
-0,2035,R1,gassupply1,2020,6.38650000000
-0,2040,R1,gassupply1,2020,6.38650000000
-0,2045,R1,gassupply1,2020,6.38650000000
-0,2050,R1,gassupply1,2020,6.38650000000
-0,2059,R1,gassupply1,2020,6.38650000000
-1,2030,R1,gassupply1,2025,17.17050000000
-1,2035,R1,gassupply1,2025,17.17050000000
-1,2040,R1,gassupply1,2025,17.17050000000
-1,2045,R1,gassupply1,2025,17.17050000000
-1,2050,R1,gassupply1,2025,17.17050000000
-1,2059,R1,gassupply1,2025,17.17050000000
-1,2060,R1,gassupply1,2025,17.17050000000
-1,2064,R1,gassupply1,2025,17.17050000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 318652a..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,24 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gassupply1,2020,13.88650000000
-0,2035,R1,gassupply1,2020,6.38650000000
-0,2040,R1,gassupply1,2020,6.38650000000
-0,2045,R1,gassupply1,2020,6.38650000000
-0,2050,R1,gassupply1,2020,6.38650000000
-0,2059,R1,gassupply1,2020,6.38650000000
-1,2030,R1,gassupply1,2025,17.17050000000
-1,2035,R1,gassupply1,2025,17.17050000000
-1,2040,R1,gassupply1,2025,17.17050000000
-1,2045,R1,gassupply1,2025,17.17050000000
-1,2050,R1,gassupply1,2025,17.17050000000
-1,2059,R1,gassupply1,2025,17.17050000000
-1,2060,R1,gassupply1,2025,17.17050000000
-1,2064,R1,gassupply1,2025,17.17050000000
-2,2035,R1,gassupply1,2030,14.53590000000
-2,2040,R1,gassupply1,2030,14.53590000000
-2,2045,R1,gassupply1,2030,14.53590000000
-2,2050,R1,gassupply1,2030,14.53590000000
-2,2059,R1,gassupply1,2030,14.53590000000
-2,2060,R1,gassupply1,2030,14.53590000000
-2,2064,R1,gassupply1,2030,14.53590000000
-2,2065,R1,gassupply1,2030,14.53590000000
-2,2069,R1,gassupply1,2030,14.53590000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index 01142c0..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,32 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gassupply1,6.38650000000
-0,2040,R1,2020,gassupply1,6.38650000000
-0,2045,R1,2020,gassupply1,6.38650000000
-0,2050,R1,2020,gassupply1,6.38650000000
-0,2059,R1,2020,gassupply1,6.38650000000
-1,2035,R1,2025,gassupply1,17.17050000000
-1,2040,R1,2025,gassupply1,17.17050000000
-1,2045,R1,2025,gassupply1,17.17050000000
-1,2050,R1,2025,gassupply1,17.17050000000
-1,2059,R1,2025,gassupply1,17.17050000000
-1,2060,R1,2025,gassupply1,17.17050000000
-1,2064,R1,2025,gassupply1,17.17050000000
-2,2035,R1,2030,gassupply1,14.53590000000
-2,2040,R1,2030,gassupply1,14.53590000000
-2,2045,R1,2030,gassupply1,14.53590000000
-2,2050,R1,2030,gassupply1,14.53590000000
-2,2059,R1,2030,gassupply1,14.53590000000
-2,2060,R1,2030,gassupply1,14.53590000000
-2,2064,R1,2030,gassupply1,14.53590000000
-2,2065,R1,2030,gassupply1,14.53590000000
-2,2069,R1,2030,gassupply1,14.53590000000
-3,2040,R1,2035,gassupply1,3.44930000000
-3,2045,R1,2035,gassupply1,3.44930000000
-3,2050,R1,2035,gassupply1,3.44930000000
-3,2059,R1,2035,gassupply1,3.44930000000
-3,2060,R1,2035,gassupply1,3.44930000000
-3,2064,R1,2035,gassupply1,3.44930000000
-3,2065,R1,2035,gassupply1,3.44930000000
-3,2069,R1,2035,gassupply1,3.44930000000
-3,2070,R1,2035,gassupply1,3.44930000000
-3,2074,R1,2035,gassupply1,3.44930000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index 5e207d8..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,40 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2040,R1,gassupply1,2020,6.38650000000
-0,2045,R1,gassupply1,2020,6.38650000000
-0,2050,R1,gassupply1,2020,6.38650000000
-0,2059,R1,gassupply1,2020,6.38650000000
-1,2040,R1,gassupply1,2025,17.17050000000
-1,2045,R1,gassupply1,2025,17.17050000000
-1,2050,R1,gassupply1,2025,17.17050000000
-1,2059,R1,gassupply1,2025,17.17050000000
-1,2060,R1,gassupply1,2025,17.17050000000
-1,2064,R1,gassupply1,2025,17.17050000000
-2,2040,R1,gassupply1,2030,14.53590000000
-2,2045,R1,gassupply1,2030,14.53590000000
-2,2050,R1,gassupply1,2030,14.53590000000
-2,2059,R1,gassupply1,2030,14.53590000000
-2,2060,R1,gassupply1,2030,14.53590000000
-2,2064,R1,gassupply1,2030,14.53590000000
-2,2065,R1,gassupply1,2030,14.53590000000
-2,2069,R1,gassupply1,2030,14.53590000000
-3,2040,R1,gassupply1,2035,3.44930000000
-3,2045,R1,gassupply1,2035,3.44930000000
-3,2050,R1,gassupply1,2035,3.44930000000
-3,2059,R1,gassupply1,2035,3.44930000000
-3,2060,R1,gassupply1,2035,3.44930000000
-3,2064,R1,gassupply1,2035,3.44930000000
-3,2065,R1,gassupply1,2035,3.44930000000
-3,2069,R1,gassupply1,2035,3.44930000000
-3,2070,R1,gassupply1,2035,3.44930000000
-3,2074,R1,gassupply1,2035,3.44930000000
-4,2045,R1,gassupply1,2040,7.56090000000
-4,2050,R1,gassupply1,2040,7.56090000000
-4,2059,R1,gassupply1,2040,7.56090000000
-4,2060,R1,gassupply1,2040,7.56090000000
-4,2064,R1,gassupply1,2040,7.56090000000
-4,2065,R1,gassupply1,2040,7.56090000000
-4,2069,R1,gassupply1,2040,7.56090000000
-4,2070,R1,gassupply1,2040,7.56090000000
-4,2074,R1,gassupply1,2040,7.56090000000
-4,2075,R1,gassupply1,2040,7.56090000000
-4,2079,R1,gassupply1,2040,7.56090000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index 89589cc..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,48 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gassupply1,2020,6.38650000000
-0,2050,R1,gassupply1,2020,6.38650000000
-0,2059,R1,gassupply1,2020,6.38650000000
-1,2045,R1,gassupply1,2025,17.17050000000
-1,2050,R1,gassupply1,2025,17.17050000000
-1,2059,R1,gassupply1,2025,17.17050000000
-1,2060,R1,gassupply1,2025,17.17050000000
-1,2064,R1,gassupply1,2025,17.17050000000
-2,2045,R1,gassupply1,2030,14.53590000000
-2,2050,R1,gassupply1,2030,14.53590000000
-2,2059,R1,gassupply1,2030,14.53590000000
-2,2060,R1,gassupply1,2030,14.53590000000
-2,2064,R1,gassupply1,2030,14.53590000000
-2,2065,R1,gassupply1,2030,14.53590000000
-2,2069,R1,gassupply1,2030,14.53590000000
-3,2045,R1,gassupply1,2035,3.44930000000
-3,2050,R1,gassupply1,2035,3.44930000000
-3,2059,R1,gassupply1,2035,3.44930000000
-3,2060,R1,gassupply1,2035,3.44930000000
-3,2064,R1,gassupply1,2035,3.44930000000
-3,2065,R1,gassupply1,2035,3.44930000000
-3,2069,R1,gassupply1,2035,3.44930000000
-3,2070,R1,gassupply1,2035,3.44930000000
-3,2074,R1,gassupply1,2035,3.44930000000
-4,2045,R1,gassupply1,2040,7.56090000000
-4,2050,R1,gassupply1,2040,7.56090000000
-4,2059,R1,gassupply1,2040,7.56090000000
-4,2060,R1,gassupply1,2040,7.56090000000
-4,2064,R1,gassupply1,2040,7.56090000000
-4,2065,R1,gassupply1,2040,7.56090000000
-4,2069,R1,gassupply1,2040,7.56090000000
-4,2070,R1,gassupply1,2040,7.56090000000
-4,2074,R1,gassupply1,2040,7.56090000000
-4,2075,R1,gassupply1,2040,7.56090000000
-4,2079,R1,gassupply1,2040,7.56090000000
-5,2050,R1,gassupply1,2045,7.20090000000
-5,2059,R1,gassupply1,2045,7.20090000000
-5,2060,R1,gassupply1,2045,7.20090000000
-5,2064,R1,gassupply1,2045,7.20090000000
-5,2065,R1,gassupply1,2045,7.20090000000
-5,2069,R1,gassupply1,2045,7.20090000000
-5,2070,R1,gassupply1,2045,7.20090000000
-5,2074,R1,gassupply1,2045,7.20090000000
-5,2075,R1,gassupply1,2045,7.20090000000
-5,2079,R1,gassupply1,2045,7.20090000000
-5,2080,R1,gassupply1,2045,7.20090000000
-5,2084,R1,gassupply1,2045,7.20090000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index 8188aa3..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,49 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gassupply1,6.38650000000
-0,2055,R1,2020,gassupply1,6.38650000000
-0,2059,R1,2020,gassupply1,6.38650000000
-1,2050,R1,2025,gassupply1,17.17050000000
-1,2055,R1,2025,gassupply1,17.17050000000
-1,2059,R1,2025,gassupply1,17.17050000000
-1,2060,R1,2025,gassupply1,17.17050000000
-1,2064,R1,2025,gassupply1,17.17050000000
-2,2050,R1,2030,gassupply1,14.53590000000
-2,2055,R1,2030,gassupply1,14.53590000000
-2,2059,R1,2030,gassupply1,14.53590000000
-2,2060,R1,2030,gassupply1,14.53590000000
-2,2064,R1,2030,gassupply1,14.53590000000
-2,2065,R1,2030,gassupply1,14.53590000000
-2,2069,R1,2030,gassupply1,14.53590000000
-3,2050,R1,2035,gassupply1,3.44930000000
-3,2055,R1,2035,gassupply1,3.44930000000
-3,2059,R1,2035,gassupply1,3.44930000000
-3,2060,R1,2035,gassupply1,3.44930000000
-3,2064,R1,2035,gassupply1,3.44930000000
-3,2065,R1,2035,gassupply1,3.44930000000
-3,2069,R1,2035,gassupply1,3.44930000000
-3,2070,R1,2035,gassupply1,3.44930000000
-3,2074,R1,2035,gassupply1,3.44930000000
-4,2050,R1,2040,gassupply1,7.56090000000
-4,2055,R1,2040,gassupply1,7.56090000000
-4,2059,R1,2040,gassupply1,7.56090000000
-4,2060,R1,2040,gassupply1,7.56090000000
-4,2064,R1,2040,gassupply1,7.56090000000
-4,2065,R1,2040,gassupply1,7.56090000000
-4,2069,R1,2040,gassupply1,7.56090000000
-4,2070,R1,2040,gassupply1,7.56090000000
-4,2074,R1,2040,gassupply1,7.56090000000
-4,2075,R1,2040,gassupply1,7.56090000000
-4,2079,R1,2040,gassupply1,7.56090000000
-5,2050,R1,2045,gassupply1,7.20090000000
-5,2055,R1,2045,gassupply1,7.20090000000
-5,2059,R1,2045,gassupply1,7.20090000000
-5,2060,R1,2045,gassupply1,7.20090000000
-5,2064,R1,2045,gassupply1,7.20090000000
-5,2065,R1,2045,gassupply1,7.20090000000
-5,2069,R1,2045,gassupply1,7.20090000000
-5,2070,R1,2045,gassupply1,7.20090000000
-5,2074,R1,2045,gassupply1,7.20090000000
-5,2075,R1,2045,gassupply1,7.20090000000
-5,2079,R1,2045,gassupply1,7.20090000000
-5,2080,R1,2045,gassupply1,7.20090000000
-5,2084,R1,2045,gassupply1,7.20090000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/MCACapacity.csv b/case-studies/hands-on-files/HO6/default_final/Results/MCACapacity.csv
deleted file mode 100644
index 128847e..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/MCACapacity.csv
+++ /dev/null
@@ -1,147 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2020,5,,,,
-gasboiler,R1,R1,A2,residential,retrofit,2020,5,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2020,0.5,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2020,0.5,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2020,7.5,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2020,7.5,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2025,8.5,agent,(Multiple Items),,
-heatpump,R1,R1,A1,residential,retrofit,2025,3.5,sector,(Multiple Items),,
-gasboiler,R1,R1,A2,residential,retrofit,2025,7,,,,
-heatpump,R1,R1,A2,residential,retrofit,2025,4.75,Sum of capacity,Column Labels,,
-gasCCGT,R1,R1,A1,power,retrofit,2025,1.1763,Row Labels,gasCCGT,Grand Total,
-gasCCGT,R1,R1,A2,power,retrofit,2025,0.8382,2020,0.5,0.5,
-windturbine,R1,R1,A2,power,retrofit,2025,0.7609,2025,1.1763,1.1763,
-gassupply1,R1,R1,A1,gas,retrofit,2025,10.6932,2030,2.3094,2.3094,
-gassupply1,R1,R1,A2,gas,retrofit,2025,10.6932,2035,2.4088,2.4088,
-gasboiler,R1,R1,A1,residential,retrofit,2030,6,2040,2.5774,2.5774,
-gasboiler,R1,R1,A1,residential,retrofit,2030,5.625,2045,2.5774,2.5774,
-heatpump,R1,R1,A1,residential,retrofit,2030,3.5,2050,2.9106,2.9106,
-gasboiler,R1,R1,A2,residential,retrofit,2030,4.5,Grand Total,14.4599,14.4599,
-gasboiler,R1,R1,A2,residential,retrofit,2030,2.8125,,,,
-heatpump,R1,R1,A2,residential,retrofit,2030,4.75,,,,
-heatpump,R1,R1,A2,residential,retrofit,2030,1.7875,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2030,0.6763,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2030,1.6331,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2030,0.3382,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2030,0.8166,agent,A2,,
-windturbine,R1,R1,A2,power,retrofit,2030,0.7609,sector,power,,
-windturbine,R1,R1,A2,power,retrofit,2030,1.8373,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2030,6.9432,Sum of capacity,Column Labels,,
-gassupply1,R1,R1,A1,gas,retrofit,2030,8.5853,Row Labels,gasCCGT,windturbine,Grand Total
-gassupply1,R1,R1,A2,gas,retrofit,2030,6.9432,2020,0.5,,0.5
-gassupply1,R1,R1,A2,gas,retrofit,2030,8.5853,2025,0.8382,0.7609,1.5991
-gasboiler,R1,R1,A1,residential,retrofit,2035,5.625,2030,1.1548,2.5982,3.753
-gasboiler,R1,R1,A1,residential,retrofit,2035,6.5812,2035,1.2542,2.8219,4.0761
-heatpump,R1,R1,A1,residential,retrofit,2035,2.75,2040,1.398,3.1454,4.5434
-gasboiler,R1,R1,A2,residential,retrofit,2035,2.8125,2045,1.398,3.1454,4.5434
-gasboiler,R1,R1,A2,residential,retrofit,2035,4.1156,2050,1.9423,3.6091,5.5514
-heatpump,R1,R1,A2,residential,retrofit,2035,1.7875,Grand Total,8.4855,16.0809,24.5664
-heatpump,R1,R1,A2,residential,retrofit,2035,4.2019,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2035,0.6763,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2035,1.6331,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2035,0.0994,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2035,0.3382,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2035,0.8166,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2035,0.0994,,,,
-windturbine,R1,R1,A2,power,retrofit,2035,0.7609,,,,
-windturbine,R1,R1,A2,power,retrofit,2035,1.8373,,,,
-windturbine,R1,R1,A2,power,retrofit,2035,0.2237,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2035,3.1932,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2035,8.5853,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2035,7.268,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2035,3.1932,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2035,8.5853,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2035,7.268,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2040,6.5812,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2040,9.8456,,,,
-heatpump,R1,R1,A1,residential,retrofit,2040,2.75,,,,
-heatpump,R1,R1,A1,residential,retrofit,2040,0.275,,,,
-gasboiler,R1,R1,A2,residential,retrofit,2040,4.1156,,,,
-gasboiler,R1,R1,A2,residential,retrofit,2040,3.2527,,,,
-heatpump,R1,R1,A2,residential,retrofit,2040,4.2019,,,,
-heatpump,R1,R1,A2,residential,retrofit,2040,2.6932,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2040,0.6763,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2040,1.6331,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2040,0.0994,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2040,0.1686,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2040,0.3382,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2040,0.8166,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2040,0.0994,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2040,0.1438,,,,
-windturbine,R1,R1,A2,power,retrofit,2040,0.7609,,,,
-windturbine,R1,R1,A2,power,retrofit,2040,1.8373,,,,
-windturbine,R1,R1,A2,power,retrofit,2040,0.2237,,,,
-windturbine,R1,R1,A2,power,retrofit,2040,0.3235,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2040,3.1932,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2040,8.5853,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2040,7.268,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2040,1.7247,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2040,3.1932,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2040,8.5853,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2040,7.268,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2040,1.7247,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2045,9.8456,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2045,9.1749,,,,
-heatpump,R1,R1,A1,residential,retrofit,2045,0.275,,,,
-heatpump,R1,R1,A1,residential,retrofit,2045,1.5263,,,,
-gasboiler,R1,R1,A2,residential,retrofit,2045,3.2527,,,,
-gasboiler,R1,R1,A2,residential,retrofit,2045,3.9262,,,,
-heatpump,R1,R1,A2,residential,retrofit,2045,2.6932,,,,
-heatpump,R1,R1,A2,residential,retrofit,2045,3.9457,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2045,0.6763,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2045,1.6331,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2045,0.0994,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2045,0.1686,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2045,0.3382,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2045,0.8166,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2045,0.0994,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2045,0.1438,,,,
-windturbine,R1,R1,A2,power,retrofit,2045,0.7609,,,,
-windturbine,R1,R1,A2,power,retrofit,2045,1.8373,,,,
-windturbine,R1,R1,A2,power,retrofit,2045,0.2237,,,,
-windturbine,R1,R1,A2,power,retrofit,2045,0.3235,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,3.1932,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,8.5853,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,7.268,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,1.7247,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,3.7805,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2045,3.1932,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2045,8.5853,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2045,7.268,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2045,1.7247,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2045,3.7805,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2050,9.1749,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2050,8.8738,,,,
-heatpump,R1,R1,A1,residential,retrofit,2050,1.5263,,,,
-heatpump,R1,R1,A1,residential,retrofit,2050,0.2276,,,,
-gasboiler,R1,R1,A2,residential,retrofit,2050,3.9262,,,,
-gasboiler,R1,R1,A2,residential,retrofit,2050,3.4853,,,,
-heatpump,R1,R1,A2,residential,retrofit,2050,3.9457,,,,
-heatpump,R1,R1,A2,residential,retrofit,2050,3.1786,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2050,0.6763,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2050,1.6331,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2050,0.0994,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2050,0.1686,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2050,0.3332,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2050,0.3382,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2050,0.8166,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2050,0.0994,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2050,0.1438,,,,
-gasCCGT,R1,R1,A2,power,retrofit,2050,0.5443,,,,
-windturbine,R1,R1,A2,power,retrofit,2050,1.8373,,,,
-windturbine,R1,R1,A2,power,retrofit,2050,0.2237,,,,
-windturbine,R1,R1,A2,power,retrofit,2050,0.3235,,,,
-windturbine,R1,R1,A2,power,retrofit,2050,1.2246,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,3.1932,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,8.5853,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,7.268,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,1.7247,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,3.7805,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,3.6005,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2050,3.1932,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2050,8.5853,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2050,7.268,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2050,1.7247,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2050,3.7805,,,,
-gassupply1,R1,R1,A2,gas,retrofit,2050,3.6005,,,,
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/MCAPrices.csv b/case-studies/hands-on-files/HO6/default_final/Results/MCAPrices.csv
deleted file mode 100644
index f41cc4d..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/MCAPrices.csv
+++ /dev/null
@@ -1,169 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,2.16470000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.44060000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,3.24710000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,2.16100000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.16470000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.44060000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,3.24710000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,2.16100000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,6.49420000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,4.32190000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,4.32940000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,2.88130000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,1.16120000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.71260000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,1.74630000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,1.09410000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.16120000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.71260000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.74630000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.09410000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,3.51920000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,2.33940000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,2.33430000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,1.49240000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.54980000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,0.98750000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,2.32910000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,1.49500000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.54980000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.98750000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.32910000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.49500000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.68460000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.07360000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,3.11130000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,2.01180000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,1.93280000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,1.28210000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,2.90370000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,1.93990000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.93280000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.28210000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.90370000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.93990000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,5.83360000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.98030000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,3.87740000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,2.60880000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,2.48350000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,1.76900000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,3.72960000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,2.67070000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.48350000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.76900000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,3.72960000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,2.67070000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,7.48550000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,5.44580000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,4.97870000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,3.58410000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,3.07960000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,2.13140000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,4.62390000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,3.21940000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,3.07960000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.13140000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,4.62390000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,3.21940000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,9.27450000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,6.57370000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,6.17110000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,4.32250000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2020.csv
deleted file mode 100644
index 9d9f90c..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,16 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,2.01450000000
-0,2030,R1,2020,gasCCGT,1.01450000000
-0,2035,R1,2020,gasCCGT,1.01450000000
-0,2040,R1,2020,gasCCGT,1.01450000000
-0,2045,R1,2020,gasCCGT,1.01450000000
-0,2049,R1,2020,gasCCGT,1.01450000000
-0,2050,R1,2020,gasCCGT,1.01450000000
-0,2059,R1,2020,gasCCGT,1.01450000000
-1,2025,R1,2020,windturbine,0.76090000000
-1,2030,R1,2020,windturbine,0.76090000000
-1,2035,R1,2020,windturbine,0.76090000000
-1,2040,R1,2020,windturbine,0.76090000000
-1,2045,R1,2020,windturbine,0.76090000000
-1,2049,R1,2020,windturbine,0.76090000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2025.csv
deleted file mode 100644
index f3f0b07..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,35 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasCCGT,2020,2.01450000000
-0,2030,R1,gasCCGT,2020,1.01450000000
-0,2035,R1,gasCCGT,2020,1.01450000000
-0,2040,R1,gasCCGT,2020,1.01450000000
-0,2045,R1,gasCCGT,2020,1.01450000000
-0,2049,R1,gasCCGT,2020,1.01450000000
-0,2050,R1,gasCCGT,2020,1.01450000000
-0,2054,R1,gasCCGT,2020,1.01450000000
-0,2055,R1,gasCCGT,2020,1.01450000000
-0,2059,R1,gasCCGT,2020,1.01450000000
-1,2030,R1,gasCCGT,2025,2.44970000000
-1,2035,R1,gasCCGT,2025,2.44970000000
-1,2040,R1,gasCCGT,2025,2.44970000000
-1,2045,R1,gasCCGT,2025,2.44970000000
-1,2049,R1,gasCCGT,2025,2.44970000000
-1,2050,R1,gasCCGT,2025,2.44970000000
-1,2054,R1,gasCCGT,2025,2.44970000000
-1,2055,R1,gasCCGT,2025,2.44970000000
-1,2059,R1,gasCCGT,2025,2.44970000000
-1,2060,R1,gasCCGT,2025,2.44970000000
-1,2064,R1,gasCCGT,2025,2.44970000000
-2,2025,R1,windturbine,2020,0.76090000000
-2,2030,R1,windturbine,2020,0.76090000000
-2,2035,R1,windturbine,2020,0.76090000000
-2,2040,R1,windturbine,2020,0.76090000000
-2,2045,R1,windturbine,2020,0.76090000000
-2,2049,R1,windturbine,2020,0.76090000000
-3,2030,R1,windturbine,2025,1.83730000000
-3,2035,R1,windturbine,2025,1.83730000000
-3,2040,R1,windturbine,2025,1.83730000000
-3,2045,R1,windturbine,2025,1.83730000000
-3,2049,R1,windturbine,2025,1.83730000000
-3,2050,R1,windturbine,2025,1.83730000000
-3,2054,R1,windturbine,2025,1.83730000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2030.csv
deleted file mode 100644
index 127ee60..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,53 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gasCCGT,1.01450000000
-0,2035,R1,2020,gasCCGT,1.01450000000
-0,2040,R1,2020,gasCCGT,1.01450000000
-0,2045,R1,2020,gasCCGT,1.01450000000
-0,2049,R1,2020,gasCCGT,1.01450000000
-0,2050,R1,2020,gasCCGT,1.01450000000
-0,2054,R1,2020,gasCCGT,1.01450000000
-0,2055,R1,2020,gasCCGT,1.01450000000
-0,2059,R1,2020,gasCCGT,1.01450000000
-1,2030,R1,2020,windturbine,0.76090000000
-1,2035,R1,2020,windturbine,0.76090000000
-1,2040,R1,2020,windturbine,0.76090000000
-1,2045,R1,2020,windturbine,0.76090000000
-1,2049,R1,2020,windturbine,0.76090000000
-2,2030,R1,2025,gasCCGT,2.44970000000
-2,2035,R1,2025,gasCCGT,2.44970000000
-2,2040,R1,2025,gasCCGT,2.44970000000
-2,2045,R1,2025,gasCCGT,2.44970000000
-2,2049,R1,2025,gasCCGT,2.44970000000
-2,2050,R1,2025,gasCCGT,2.44970000000
-2,2054,R1,2025,gasCCGT,2.44970000000
-2,2055,R1,2025,gasCCGT,2.44970000000
-2,2059,R1,2025,gasCCGT,2.44970000000
-2,2060,R1,2025,gasCCGT,2.44970000000
-2,2064,R1,2025,gasCCGT,2.44970000000
-3,2030,R1,2025,windturbine,1.83730000000
-3,2035,R1,2025,windturbine,1.83730000000
-3,2040,R1,2025,windturbine,1.83730000000
-3,2045,R1,2025,windturbine,1.83730000000
-3,2049,R1,2025,windturbine,1.83730000000
-3,2050,R1,2025,windturbine,1.83730000000
-3,2054,R1,2025,windturbine,1.83730000000
-4,2035,R1,2030,gasCCGT,0.19880000000
-4,2040,R1,2030,gasCCGT,0.19880000000
-4,2045,R1,2030,gasCCGT,0.19880000000
-4,2049,R1,2030,gasCCGT,0.19880000000
-4,2050,R1,2030,gasCCGT,0.19880000000
-4,2054,R1,2030,gasCCGT,0.19880000000
-4,2055,R1,2030,gasCCGT,0.19880000000
-4,2059,R1,2030,gasCCGT,0.19880000000
-4,2060,R1,2030,gasCCGT,0.19880000000
-4,2064,R1,2030,gasCCGT,0.19880000000
-4,2065,R1,2030,gasCCGT,0.19880000000
-4,2069,R1,2030,gasCCGT,0.19880000000
-5,2035,R1,2030,windturbine,0.22370000000
-5,2040,R1,2030,windturbine,0.22370000000
-5,2045,R1,2030,windturbine,0.22370000000
-5,2049,R1,2030,windturbine,0.22370000000
-5,2050,R1,2030,windturbine,0.22370000000
-5,2054,R1,2030,windturbine,0.22370000000
-5,2055,R1,2030,windturbine,0.22370000000
-5,2059,R1,2030,windturbine,0.22370000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2035.csv
deleted file mode 100644
index 1470f3d..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,71 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasCCGT,2020,1.01450000000
-0,2040,R1,gasCCGT,2020,1.01450000000
-0,2045,R1,gasCCGT,2020,1.01450000000
-0,2049,R1,gasCCGT,2020,1.01450000000
-0,2050,R1,gasCCGT,2020,1.01450000000
-0,2054,R1,gasCCGT,2020,1.01450000000
-0,2055,R1,gasCCGT,2020,1.01450000000
-0,2059,R1,gasCCGT,2020,1.01450000000
-1,2035,R1,gasCCGT,2025,2.44970000000
-1,2040,R1,gasCCGT,2025,2.44970000000
-1,2045,R1,gasCCGT,2025,2.44970000000
-1,2049,R1,gasCCGT,2025,2.44970000000
-1,2050,R1,gasCCGT,2025,2.44970000000
-1,2054,R1,gasCCGT,2025,2.44970000000
-1,2055,R1,gasCCGT,2025,2.44970000000
-1,2059,R1,gasCCGT,2025,2.44970000000
-1,2060,R1,gasCCGT,2025,2.44970000000
-1,2064,R1,gasCCGT,2025,2.44970000000
-2,2035,R1,gasCCGT,2030,0.19880000000
-2,2040,R1,gasCCGT,2030,0.19880000000
-2,2045,R1,gasCCGT,2030,0.19880000000
-2,2049,R1,gasCCGT,2030,0.19880000000
-2,2050,R1,gasCCGT,2030,0.19880000000
-2,2054,R1,gasCCGT,2030,0.19880000000
-2,2055,R1,gasCCGT,2030,0.19880000000
-2,2059,R1,gasCCGT,2030,0.19880000000
-2,2060,R1,gasCCGT,2030,0.19880000000
-2,2064,R1,gasCCGT,2030,0.19880000000
-2,2065,R1,gasCCGT,2030,0.19880000000
-2,2069,R1,gasCCGT,2030,0.19880000000
-3,2040,R1,gasCCGT,2035,0.31240000000
-3,2045,R1,gasCCGT,2035,0.31240000000
-3,2049,R1,gasCCGT,2035,0.31240000000
-3,2050,R1,gasCCGT,2035,0.31240000000
-3,2054,R1,gasCCGT,2035,0.31240000000
-3,2055,R1,gasCCGT,2035,0.31240000000
-3,2059,R1,gasCCGT,2035,0.31240000000
-3,2060,R1,gasCCGT,2035,0.31240000000
-3,2064,R1,gasCCGT,2035,0.31240000000
-3,2065,R1,gasCCGT,2035,0.31240000000
-3,2069,R1,gasCCGT,2035,0.31240000000
-3,2070,R1,gasCCGT,2035,0.31240000000
-3,2074,R1,gasCCGT,2035,0.31240000000
-4,2035,R1,windturbine,2020,0.76090000000
-4,2040,R1,windturbine,2020,0.76090000000
-4,2045,R1,windturbine,2020,0.76090000000
-4,2049,R1,windturbine,2020,0.76090000000
-5,2035,R1,windturbine,2025,1.83730000000
-5,2040,R1,windturbine,2025,1.83730000000
-5,2045,R1,windturbine,2025,1.83730000000
-5,2049,R1,windturbine,2025,1.83730000000
-5,2050,R1,windturbine,2025,1.83730000000
-5,2054,R1,windturbine,2025,1.83730000000
-6,2035,R1,windturbine,2030,0.22370000000
-6,2040,R1,windturbine,2030,0.22370000000
-6,2045,R1,windturbine,2030,0.22370000000
-6,2049,R1,windturbine,2030,0.22370000000
-6,2050,R1,windturbine,2030,0.22370000000
-6,2054,R1,windturbine,2030,0.22370000000
-6,2055,R1,windturbine,2030,0.22370000000
-6,2059,R1,windturbine,2030,0.22370000000
-7,2040,R1,windturbine,2035,0.32350000000
-7,2045,R1,windturbine,2035,0.32350000000
-7,2049,R1,windturbine,2035,0.32350000000
-7,2050,R1,windturbine,2035,0.32350000000
-7,2054,R1,windturbine,2035,0.32350000000
-7,2055,R1,windturbine,2035,0.32350000000
-7,2059,R1,windturbine,2035,0.32350000000
-7,2060,R1,windturbine,2035,0.32350000000
-7,2064,R1,windturbine,2035,0.32350000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2040.csv
deleted file mode 100644
index d24df1f..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,65 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,1.01450000000
-0,2045,R1,2020,gasCCGT,1.01450000000
-0,2049,R1,2020,gasCCGT,1.01450000000
-0,2050,R1,2020,gasCCGT,1.01450000000
-0,2054,R1,2020,gasCCGT,1.01450000000
-0,2055,R1,2020,gasCCGT,1.01450000000
-0,2059,R1,2020,gasCCGT,1.01450000000
-1,2040,R1,2020,windturbine,0.76090000000
-1,2045,R1,2020,windturbine,0.76090000000
-1,2049,R1,2020,windturbine,0.76090000000
-2,2040,R1,2025,gasCCGT,2.44970000000
-2,2045,R1,2025,gasCCGT,2.44970000000
-2,2049,R1,2025,gasCCGT,2.44970000000
-2,2050,R1,2025,gasCCGT,2.44970000000
-2,2054,R1,2025,gasCCGT,2.44970000000
-2,2055,R1,2025,gasCCGT,2.44970000000
-2,2059,R1,2025,gasCCGT,2.44970000000
-2,2060,R1,2025,gasCCGT,2.44970000000
-2,2064,R1,2025,gasCCGT,2.44970000000
-3,2040,R1,2025,windturbine,1.83730000000
-3,2045,R1,2025,windturbine,1.83730000000
-3,2049,R1,2025,windturbine,1.83730000000
-3,2050,R1,2025,windturbine,1.83730000000
-3,2054,R1,2025,windturbine,1.83730000000
-4,2040,R1,2030,gasCCGT,0.19880000000
-4,2045,R1,2030,gasCCGT,0.19880000000
-4,2049,R1,2030,gasCCGT,0.19880000000
-4,2050,R1,2030,gasCCGT,0.19880000000
-4,2054,R1,2030,gasCCGT,0.19880000000
-4,2055,R1,2030,gasCCGT,0.19880000000
-4,2059,R1,2030,gasCCGT,0.19880000000
-4,2060,R1,2030,gasCCGT,0.19880000000
-4,2064,R1,2030,gasCCGT,0.19880000000
-4,2065,R1,2030,gasCCGT,0.19880000000
-4,2069,R1,2030,gasCCGT,0.19880000000
-5,2040,R1,2030,windturbine,0.22370000000
-5,2045,R1,2030,windturbine,0.22370000000
-5,2049,R1,2030,windturbine,0.22370000000
-5,2050,R1,2030,windturbine,0.22370000000
-5,2054,R1,2030,windturbine,0.22370000000
-5,2055,R1,2030,windturbine,0.22370000000
-5,2059,R1,2030,windturbine,0.22370000000
-6,2040,R1,2035,gasCCGT,0.31240000000
-6,2045,R1,2035,gasCCGT,0.31240000000
-6,2049,R1,2035,gasCCGT,0.31240000000
-6,2050,R1,2035,gasCCGT,0.31240000000
-6,2054,R1,2035,gasCCGT,0.31240000000
-6,2055,R1,2035,gasCCGT,0.31240000000
-6,2059,R1,2035,gasCCGT,0.31240000000
-6,2060,R1,2035,gasCCGT,0.31240000000
-6,2064,R1,2035,gasCCGT,0.31240000000
-6,2065,R1,2035,gasCCGT,0.31240000000
-6,2069,R1,2035,gasCCGT,0.31240000000
-6,2070,R1,2035,gasCCGT,0.31240000000
-6,2074,R1,2035,gasCCGT,0.31240000000
-7,2040,R1,2035,windturbine,0.32350000000
-7,2045,R1,2035,windturbine,0.32350000000
-7,2049,R1,2035,windturbine,0.32350000000
-7,2050,R1,2035,windturbine,0.32350000000
-7,2054,R1,2035,windturbine,0.32350000000
-7,2055,R1,2035,windturbine,0.32350000000
-7,2059,R1,2035,windturbine,0.32350000000
-7,2060,R1,2035,windturbine,0.32350000000
-7,2064,R1,2035,windturbine,0.32350000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2045.csv
deleted file mode 100644
index 59c0af3..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,80 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasCCGT,2020,1.01450000000
-0,2049,R1,gasCCGT,2020,1.01450000000
-0,2050,R1,gasCCGT,2020,1.01450000000
-0,2054,R1,gasCCGT,2020,1.01450000000
-0,2055,R1,gasCCGT,2020,1.01450000000
-0,2059,R1,gasCCGT,2020,1.01450000000
-1,2045,R1,gasCCGT,2025,2.44970000000
-1,2049,R1,gasCCGT,2025,2.44970000000
-1,2050,R1,gasCCGT,2025,2.44970000000
-1,2054,R1,gasCCGT,2025,2.44970000000
-1,2055,R1,gasCCGT,2025,2.44970000000
-1,2059,R1,gasCCGT,2025,2.44970000000
-1,2060,R1,gasCCGT,2025,2.44970000000
-1,2064,R1,gasCCGT,2025,2.44970000000
-2,2045,R1,gasCCGT,2030,0.19880000000
-2,2049,R1,gasCCGT,2030,0.19880000000
-2,2050,R1,gasCCGT,2030,0.19880000000
-2,2054,R1,gasCCGT,2030,0.19880000000
-2,2055,R1,gasCCGT,2030,0.19880000000
-2,2059,R1,gasCCGT,2030,0.19880000000
-2,2060,R1,gasCCGT,2030,0.19880000000
-2,2064,R1,gasCCGT,2030,0.19880000000
-2,2065,R1,gasCCGT,2030,0.19880000000
-2,2069,R1,gasCCGT,2030,0.19880000000
-3,2045,R1,gasCCGT,2035,0.31240000000
-3,2049,R1,gasCCGT,2035,0.31240000000
-3,2050,R1,gasCCGT,2035,0.31240000000
-3,2054,R1,gasCCGT,2035,0.31240000000
-3,2055,R1,gasCCGT,2035,0.31240000000
-3,2059,R1,gasCCGT,2035,0.31240000000
-3,2060,R1,gasCCGT,2035,0.31240000000
-3,2064,R1,gasCCGT,2035,0.31240000000
-3,2065,R1,gasCCGT,2035,0.31240000000
-3,2069,R1,gasCCGT,2035,0.31240000000
-3,2070,R1,gasCCGT,2035,0.31240000000
-3,2074,R1,gasCCGT,2035,0.31240000000
-4,2049,R1,gasCCGT,2045,0.20140000000
-4,2050,R1,gasCCGT,2045,0.87740000000
-4,2054,R1,gasCCGT,2045,0.87740000000
-4,2055,R1,gasCCGT,2045,0.87740000000
-4,2059,R1,gasCCGT,2045,0.87740000000
-4,2060,R1,gasCCGT,2045,0.87740000000
-4,2064,R1,gasCCGT,2045,0.87740000000
-4,2065,R1,gasCCGT,2045,0.87740000000
-4,2069,R1,gasCCGT,2045,0.87740000000
-4,2070,R1,gasCCGT,2045,0.87740000000
-4,2074,R1,gasCCGT,2045,0.87740000000
-4,2075,R1,gasCCGT,2045,0.87740000000
-4,2084,R1,gasCCGT,2045,0.87740000000
-5,2045,R1,windturbine,2020,0.76090000000
-5,2049,R1,windturbine,2020,0.76090000000
-6,2045,R1,windturbine,2025,1.83730000000
-6,2049,R1,windturbine,2025,1.83730000000
-6,2050,R1,windturbine,2025,1.83730000000
-6,2054,R1,windturbine,2025,1.83730000000
-7,2045,R1,windturbine,2030,0.22370000000
-7,2049,R1,windturbine,2030,0.22370000000
-7,2050,R1,windturbine,2030,0.22370000000
-7,2054,R1,windturbine,2030,0.22370000000
-7,2055,R1,windturbine,2030,0.22370000000
-7,2059,R1,windturbine,2030,0.22370000000
-8,2045,R1,windturbine,2035,0.32350000000
-8,2049,R1,windturbine,2035,0.32350000000
-8,2050,R1,windturbine,2035,0.32350000000
-8,2054,R1,windturbine,2035,0.32350000000
-8,2055,R1,windturbine,2035,0.32350000000
-8,2059,R1,windturbine,2035,0.32350000000
-8,2060,R1,windturbine,2035,0.32350000000
-8,2064,R1,windturbine,2035,0.32350000000
-9,2050,R1,windturbine,2045,1.22460000000
-9,2054,R1,windturbine,2045,1.22460000000
-9,2055,R1,windturbine,2045,1.22460000000
-9,2059,R1,windturbine,2045,1.22460000000
-9,2060,R1,windturbine,2045,1.22460000000
-9,2064,R1,windturbine,2045,1.22460000000
-9,2065,R1,windturbine,2045,1.22460000000
-9,2069,R1,windturbine,2045,1.22460000000
-9,2070,R1,windturbine,2045,1.22460000000
-9,2074,R1,windturbine,2045,1.22460000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2050.csv
deleted file mode 100644
index a66cafa..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,63 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gasCCGT,1.01450000000
-0,2054,R1,2020,gasCCGT,1.01450000000
-0,2055,R1,2020,gasCCGT,1.01450000000
-0,2059,R1,2020,gasCCGT,1.01450000000
-2,2050,R1,2025,gasCCGT,2.44970000000
-2,2054,R1,2025,gasCCGT,2.44970000000
-2,2055,R1,2025,gasCCGT,2.44970000000
-2,2059,R1,2025,gasCCGT,2.44970000000
-2,2060,R1,2025,gasCCGT,2.44970000000
-2,2064,R1,2025,gasCCGT,2.44970000000
-3,2050,R1,2025,windturbine,1.83730000000
-3,2054,R1,2025,windturbine,1.83730000000
-4,2050,R1,2030,gasCCGT,0.19880000000
-4,2054,R1,2030,gasCCGT,0.19880000000
-4,2055,R1,2030,gasCCGT,0.19880000000
-4,2059,R1,2030,gasCCGT,0.19880000000
-4,2060,R1,2030,gasCCGT,0.19880000000
-4,2064,R1,2030,gasCCGT,0.19880000000
-4,2065,R1,2030,gasCCGT,0.19880000000
-4,2069,R1,2030,gasCCGT,0.19880000000
-5,2050,R1,2030,windturbine,0.22370000000
-5,2054,R1,2030,windturbine,0.22370000000
-5,2055,R1,2030,windturbine,0.22370000000
-5,2059,R1,2030,windturbine,0.22370000000
-6,2050,R1,2035,gasCCGT,0.31240000000
-6,2054,R1,2035,gasCCGT,0.31240000000
-6,2055,R1,2035,gasCCGT,0.31240000000
-6,2059,R1,2035,gasCCGT,0.31240000000
-6,2060,R1,2035,gasCCGT,0.31240000000
-6,2064,R1,2035,gasCCGT,0.31240000000
-6,2065,R1,2035,gasCCGT,0.31240000000
-6,2069,R1,2035,gasCCGT,0.31240000000
-6,2070,R1,2035,gasCCGT,0.31240000000
-6,2074,R1,2035,gasCCGT,0.31240000000
-7,2050,R1,2035,windturbine,0.32350000000
-7,2054,R1,2035,windturbine,0.32350000000
-7,2055,R1,2035,windturbine,0.32350000000
-7,2059,R1,2035,windturbine,0.32350000000
-7,2060,R1,2035,windturbine,0.32350000000
-7,2064,R1,2035,windturbine,0.32350000000
-8,2050,R1,2045,gasCCGT,0.87740000000
-8,2054,R1,2045,gasCCGT,0.87740000000
-8,2055,R1,2045,gasCCGT,0.87740000000
-8,2059,R1,2045,gasCCGT,0.87740000000
-8,2060,R1,2045,gasCCGT,0.87740000000
-8,2064,R1,2045,gasCCGT,0.87740000000
-8,2065,R1,2045,gasCCGT,0.87740000000
-8,2069,R1,2045,gasCCGT,0.87740000000
-8,2070,R1,2045,gasCCGT,0.87740000000
-8,2074,R1,2045,gasCCGT,0.87740000000
-8,2075,R1,2045,gasCCGT,0.87740000000
-8,2084,R1,2045,gasCCGT,0.87740000000
-9,2050,R1,2045,windturbine,1.22460000000
-9,2054,R1,2045,windturbine,1.22460000000
-9,2055,R1,2045,windturbine,1.22460000000
-9,2059,R1,2045,windturbine,1.22460000000
-9,2060,R1,2045,windturbine,1.22460000000
-9,2064,R1,2045,windturbine,1.22460000000
-9,2065,R1,2045,windturbine,1.22460000000
-9,2069,R1,2045,windturbine,1.22460000000
-9,2070,R1,2045,windturbine,1.22460000000
-9,2074,R1,2045,windturbine,1.22460000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index dec351f..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,8 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2020,R1,gasboiler,2020,10.00000000000
-0,2025,R1,gasboiler,2020,15.50000000000
-0,2030,R1,gasboiler,2020,10.50000000000
-0,2034,R1,gasboiler,2020,10.50000000000
-1,2025,R1,heatpump,2020,8.25000000000
-1,2030,R1,heatpump,2020,8.25000000000
-1,2034,R1,heatpump,2020,8.25000000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 8d60039..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2025,R1,gasboiler,2020,15.50000000000
-0,2030,R1,gasboiler,2020,10.50000000000
-0,2034,R1,gasboiler,2020,10.50000000000
-1,2030,R1,gasboiler,2025,8.43750000000
-1,2034,R1,gasboiler,2025,8.43750000000
-1,2035,R1,gasboiler,2025,8.43750000000
-1,2039,R1,gasboiler,2025,8.43750000000
-2,2025,R1,heatpump,2020,8.25000000000
-2,2030,R1,heatpump,2020,8.25000000000
-2,2034,R1,heatpump,2020,8.25000000000
-3,2030,R1,heatpump,2025,1.78750000000
-3,2034,R1,heatpump,2025,1.78750000000
-3,2035,R1,heatpump,2025,1.78750000000
-3,2039,R1,heatpump,2025,1.78750000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index 175e690..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2030,R1,gasboiler,2020,10.50000000000
-0,2034,R1,gasboiler,2020,10.50000000000
-1,2030,R1,gasboiler,2025,8.43750000000
-1,2034,R1,gasboiler,2025,8.43750000000
-1,2035,R1,gasboiler,2025,8.43750000000
-1,2039,R1,gasboiler,2025,8.43750000000
-2,2035,R1,gasboiler,2030,10.69690000000
-2,2039,R1,gasboiler,2030,10.69690000000
-2,2040,R1,gasboiler,2030,10.69690000000
-2,2044,R1,gasboiler,2030,10.69690000000
-3,2030,R1,heatpump,2020,8.25000000000
-3,2034,R1,heatpump,2020,8.25000000000
-4,2030,R1,heatpump,2025,1.78750000000
-4,2034,R1,heatpump,2025,1.78750000000
-4,2035,R1,heatpump,2025,1.78750000000
-4,2039,R1,heatpump,2025,1.78750000000
-5,2035,R1,heatpump,2030,6.95190000000
-5,2039,R1,heatpump,2030,6.95190000000
-5,2040,R1,heatpump,2030,6.95190000000
-5,2044,R1,heatpump,2030,6.95190000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 2db9d43..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2035,R1,gasboiler,2025,8.43750000000
-0,2039,R1,gasboiler,2025,8.43750000000
-1,2035,R1,gasboiler,2030,10.69690000000
-1,2039,R1,gasboiler,2030,10.69690000000
-1,2040,R1,gasboiler,2030,10.69690000000
-1,2044,R1,gasboiler,2030,10.69690000000
-2,2040,R1,gasboiler,2035,13.09830000000
-2,2044,R1,gasboiler,2035,13.09830000000
-2,2045,R1,gasboiler,2035,13.09830000000
-2,2049,R1,gasboiler,2035,13.09830000000
-3,2035,R1,heatpump,2025,1.78750000000
-3,2039,R1,heatpump,2025,1.78750000000
-4,2035,R1,heatpump,2030,6.95190000000
-4,2039,R1,heatpump,2030,6.95190000000
-4,2040,R1,heatpump,2030,6.95190000000
-4,2044,R1,heatpump,2030,6.95190000000
-5,2040,R1,heatpump,2035,2.96820000000
-5,2044,R1,heatpump,2035,2.96820000000
-5,2045,R1,heatpump,2035,2.96820000000
-5,2049,R1,heatpump,2035,2.96820000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 2df821a..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2040,R1,gasboiler,2030,10.69690000000
-0,2044,R1,gasboiler,2030,10.69690000000
-1,2040,R1,gasboiler,2035,13.09830000000
-1,2044,R1,gasboiler,2035,13.09830000000
-1,2045,R1,gasboiler,2035,13.09830000000
-1,2049,R1,gasboiler,2035,13.09830000000
-2,2045,R1,gasboiler,2040,13.10120000000
-2,2049,R1,gasboiler,2040,13.10120000000
-2,2050,R1,gasboiler,2040,13.10120000000
-2,2054,R1,gasboiler,2040,13.10120000000
-3,2040,R1,heatpump,2030,6.95190000000
-3,2044,R1,heatpump,2030,6.95190000000
-4,2040,R1,heatpump,2035,2.96820000000
-4,2044,R1,heatpump,2035,2.96820000000
-4,2045,R1,heatpump,2035,2.96820000000
-4,2049,R1,heatpump,2035,2.96820000000
-5,2045,R1,heatpump,2040,5.47190000000
-5,2049,R1,heatpump,2040,5.47190000000
-5,2050,R1,heatpump,2040,5.47190000000
-5,2054,R1,heatpump,2040,5.47190000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index d0d8e85..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,technology,installed,capacity
-0,2045,R1,gasboiler,2035,13.09830000000
-0,2049,R1,gasboiler,2035,13.09830000000
-1,2045,R1,gasboiler,2040,13.10120000000
-1,2049,R1,gasboiler,2040,13.10120000000
-1,2050,R1,gasboiler,2040,13.10120000000
-1,2054,R1,gasboiler,2040,13.10120000000
-2,2050,R1,gasboiler,2045,12.35910000000
-2,2054,R1,gasboiler,2045,12.35910000000
-2,2055,R1,gasboiler,2045,12.35910000000
-2,2059,R1,gasboiler,2045,12.35910000000
-3,2045,R1,heatpump,2035,2.96820000000
-3,2049,R1,heatpump,2035,2.96820000000
-4,2045,R1,heatpump,2040,5.47190000000
-4,2049,R1,heatpump,2040,5.47190000000
-4,2050,R1,heatpump,2040,5.47190000000
-4,2054,R1,heatpump,2040,5.47190000000
-5,2050,R1,heatpump,2045,3.40610000000
-5,2054,R1,heatpump,2045,3.40610000000
-5,2055,R1,heatpump,2045,3.40610000000
-5,2059,R1,heatpump,2045,3.40610000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index 4877714..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,region,installed,technology,capacity
-2,2050,R1,2040,gasboiler,13.10120000000
-2,2054,R1,2040,gasboiler,13.10120000000
-3,2050,R1,2040,heatpump,5.47190000000
-3,2054,R1,2040,heatpump,5.47190000000
-4,2050,R1,2045,gasboiler,12.35910000000
-4,2054,R1,2045,gasboiler,12.35910000000
-4,2055,R1,2045,gasboiler,12.35910000000
-4,2059,R1,2045,gasboiler,12.35910000000
-5,2050,R1,2045,heatpump,3.40610000000
-5,2054,R1,2045,heatpump,3.40610000000
-5,2055,R1,2045,heatpump,3.40610000000
-5,2059,R1,2045,heatpump,3.40610000000
-6,2055,R1,2050,gasboiler,8.49730000000
-6,2059,R1,2050,gasboiler,8.49730000000
-6,2060,R1,2050,gasboiler,8.49730000000
-6,2064,R1,2050,gasboiler,8.49730000000
-7,2055,R1,2050,heatpump,3.27500000000
-7,2059,R1,2050,heatpump,3.27500000000
-7,2060,R1,2050,heatpump,3.27500000000
-7,2064,R1,2050,heatpump,3.27500000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2020.csv
deleted file mode 100644
index bab12a8..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2020,0,R1,gasboiler,2020,10.00000000000
-heat,2025,0,R1,gasboiler,2020,8.70180000000
-heat,2025,1,R1,heatpump,2020,4.63160000000
-CO2f,2020,0,R1,gasboiler,2020,647.10000000000
-CO2f,2025,0,R1,gasboiler,2020,563.09050000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2025.csv
deleted file mode 100644
index 43ae0ee..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,10 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2025,0,R1,gasboiler,2020,8.70180000000
-heat,2025,2,R1,heatpump,2020,4.63160000000
-heat,2030,0,R1,gasboiler,2020,6.03970000000
-heat,2030,1,R1,gasboiler,2025,4.85330000000
-heat,2030,2,R1,heatpump,2020,4.74550000000
-heat,2030,3,R1,heatpump,2025,1.02820000000
-CO2f,2025,0,R1,gasboiler,2020,563.09050000000
-CO2f,2030,0,R1,gasboiler,2020,390.82830000000
-CO2f,2030,1,R1,gasboiler,2025,314.05850000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2030.csv
deleted file mode 100644
index 85e6005..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2030,0,R1,gasboiler,2020,6.03970000000
-heat,2030,1,R1,gasboiler,2025,4.85330000000
-heat,2030,3,R1,heatpump,2020,4.74550000000
-heat,2030,4,R1,heatpump,2025,1.02820000000
-heat,2035,1,R1,gasboiler,2025,6.05410000000
-heat,2035,2,R1,gasboiler,2030,7.67520000000
-heat,2035,4,R1,heatpump,2025,1.28260000000
-heat,2035,5,R1,heatpump,2030,4.98810000000
-CO2f,2030,0,R1,gasboiler,2020,390.82830000000
-CO2f,2030,1,R1,gasboiler,2025,314.05850000000
-CO2f,2035,1,R1,gasboiler,2025,391.75970000000
-CO2f,2035,2,R1,gasboiler,2030,496.66430000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2035.csv
deleted file mode 100644
index 7202228..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2035,0,R1,gasboiler,2025,6.05410000000
-heat,2035,1,R1,gasboiler,2030,7.67520000000
-heat,2035,3,R1,heatpump,2025,1.28260000000
-heat,2035,4,R1,heatpump,2030,4.98810000000
-heat,2040,1,R1,gasboiler,2030,7.40300000000
-heat,2040,2,R1,gasboiler,2035,9.06490000000
-heat,2040,4,R1,heatpump,2030,4.81120000000
-heat,2040,5,R1,heatpump,2035,2.05420000000
-CO2f,2035,0,R1,gasboiler,2025,391.75970000000
-CO2f,2035,1,R1,gasboiler,2030,496.66430000000
-CO2f,2040,1,R1,gasboiler,2030,479.04770000000
-CO2f,2040,2,R1,gasboiler,2035,586.59200000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2040.csv
deleted file mode 100644
index a349db9..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2040,0,R1,gasboiler,2030,7.40300000000
-heat,2040,1,R1,gasboiler,2035,9.06490000000
-heat,2040,3,R1,heatpump,2030,4.81120000000
-heat,2040,4,R1,heatpump,2035,2.05420000000
-heat,2045,1,R1,gasboiler,2035,10.08350000000
-heat,2045,2,R1,gasboiler,2040,10.08570000000
-heat,2045,4,R1,heatpump,2035,2.28500000000
-heat,2045,5,R1,heatpump,2040,4.21250000000
-CO2f,2040,0,R1,gasboiler,2030,479.04770000000
-CO2f,2040,1,R1,gasboiler,2035,586.59200000000
-CO2f,2045,1,R1,gasboiler,2035,652.50160000000
-CO2f,2045,2,R1,gasboiler,2040,652.64480000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 4262877..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,technology,installed,supply
-heat,2045,0,R1,gasboiler,2035,10.08350000000
-heat,2045,1,R1,gasboiler,2040,10.08570000000
-heat,2045,3,R1,heatpump,2035,2.28500000000
-heat,2045,4,R1,heatpump,2040,4.21250000000
-heat,2050,1,R1,gasboiler,2040,11.44590000000
-heat,2050,2,R1,gasboiler,2045,10.79770000000
-heat,2050,4,R1,heatpump,2040,4.78060000000
-heat,2050,5,R1,heatpump,2045,2.97580000000
-CO2f,2045,0,R1,gasboiler,2035,652.50160000000
-CO2f,2045,1,R1,gasboiler,2040,652.64480000000
-CO2f,2050,1,R1,gasboiler,2040,740.66720000000
-CO2f,2050,2,R1,gasboiler,2045,698.71620000000
diff --git a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2050.csv
deleted file mode 100644
index 752daaa..0000000
--- a/case-studies/hands-on-files/HO6/default_final/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-commodity,year,asset,region,installed,technology,supply
-heat,2050,2,R1,2040,gasboiler,11.44590000000
-heat,2050,3,R1,2040,heatpump,4.78060000000
-heat,2050,4,R1,2045,gasboiler,10.79770000000
-heat,2050,5,R1,2045,heatpump,2.97580000000
-heat,2055,4,R1,2045,gasboiler,12.35910000000
-heat,2055,5,R1,2045,heatpump,3.40610000000
-heat,2055,6,R1,2050,gasboiler,8.49730000000
-heat,2055,7,R1,2050,heatpump,3.27500000000
-CO2f,2050,2,R1,2040,gasboiler,740.66720000000
-CO2f,2050,4,R1,2045,gasboiler,698.71620000000
-CO2f,2055,4,R1,2045,gasboiler,799.75820000000
-CO2f,2055,6,R1,2050,gasboiler,549.85940000000
diff --git a/case-studies/hands-on-files/HO6/default_final/input/BaseYearExport.csv b/case-studies/hands-on-files/HO6/default_final/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO6/default_final/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/input/BaseYearImport.csv b/case-studies/hands-on-files/HO6/default_final/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO6/default_final/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO6/default_final/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO6/default_final/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO6/default_final/input/Projections.csv b/case-studies/hands-on-files/HO6/default_final/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO6/default_final/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO6/default_final/settings.toml b/case-studies/hands-on-files/HO6/default_final/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO6/default_final/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/Agents.csv b/case-studies/hands-on-files/HO6/default_final/technodata/Agents.csv
deleted file mode 100644
index 82b6043..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/Agents.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,0.5,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,0.5,-1,inf,Retrofit
-Agent3,A2,R1,LCOE,EAC,,0.5,0.5,,FALSE,,,all,weighted_sum,0.5,-1,inf,New
-Agent4,A2,R1,LCOE,EAC,,0.5,0.5,,FALSE,,,all,weighted_sum,0.5,-1,inf,Retrofit
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO6/default_final/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO6/default_final/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO6/default_final/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO6/default_final/technodata/gas/Technodata.csv
deleted file mode 100644
index 4256a6a..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2,Agent4
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,0.5,0.5
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO6/default_final/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO6/default_final/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO6/default_final/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO6/default_final/technodata/power/Technodata.csv
deleted file mode 100644
index ffd2e26..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2,Agent4
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,0.5,0.5
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,0.5,0.5
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO6/default_final/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO6/default_final/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO6/default_final/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO6/default_final/technodata/residential/CommOut.csv
deleted file mode 100644
index 5e5cd62..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,0,0,64.71,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO6/default_final/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO6/default_final/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO6/default_final/technodata/residential/Technodata.csv
deleted file mode 100644
index 52341d9..0000000
--- a/case-studies/hands-on-files/HO6/default_final/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2,Agent4
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,0.5,0.5
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,0.5,0.5
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/MCACapacity.xlsx b/case-studies/hands-on-files/HO7/MCACapacity.xlsx
deleted file mode 100644
index d049b91..0000000
Binary files a/case-studies/hands-on-files/HO7/MCACapacity.xlsx and /dev/null differ
diff --git a/case-studies/hands-on-files/HO7/default.zip b/case-studies/hands-on-files/HO7/default.zip
deleted file mode 100644
index a7d9db4..0000000
Binary files a/case-studies/hands-on-files/HO7/default.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO7/default/input/BaseYearExport.csv b/case-studies/hands-on-files/HO7/default/input/BaseYearExport.csv
deleted file mode 100644
index 7218c1f..0000000
--- a/case-studies/hands-on-files/HO7/default/input/BaseYearExport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/input/BaseYearImport.csv b/case-studies/hands-on-files/HO7/default/input/BaseYearImport.csv
deleted file mode 100644
index 75b3227..0000000
--- a/case-studies/hands-on-files/HO7/default/input/BaseYearImport.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO7/default/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO7/default/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO7/default/input/Projections.csv b/case-studies/hands-on-files/HO7/default/input/Projections.csv
deleted file mode 100644
index 5b5e432..0000000
--- a/case-studies/hands-on-files/HO7/default/input/Projections.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
diff --git a/case-studies/hands-on-files/HO7/default/settings.toml b/case-studies/hands-on-files/HO7/default/settings.toml
deleted file mode 100644
index f9299c8..0000000
--- a/case-studies/hands-on-files/HO7/default/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO7/default/technodata/Agents.csv b/case-studies/hands-on-files/HO7/default/technodata/Agents.csv
deleted file mode 100644
index 739bee8..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/Agents.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
diff --git a/case-studies/hands-on-files/HO7/default/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO7/default/technodata/gas/CommIn.csv
deleted file mode 100644
index 60af1f4..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO7/default/technodata/gas/CommOut.csv
deleted file mode 100644
index 97520cd..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO7/default/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index 6862d5b..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,2 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO7/default/technodata/gas/Technodata.csv
deleted file mode 100644
index 25614cf..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
diff --git a/case-studies/hands-on-files/HO7/default/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO7/default/technodata/power/CommIn.csv
deleted file mode 100644
index c78f9c6..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
diff --git a/case-studies/hands-on-files/HO7/default/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO7/default/technodata/power/CommOut.csv
deleted file mode 100644
index 03a2f4d..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO7/default/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 2171d25..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO7/default/technodata/power/Technodata.csv
deleted file mode 100644
index 9d767cf..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
diff --git a/case-studies/hands-on-files/HO7/default/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO7/default/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index 1f2cc29..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO7/default/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index ddcb040..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,7 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO7/default/technodata/residential/CommIn.csv
deleted file mode 100644
index f72ef31..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO7/default/technodata/residential/CommOut.csv
deleted file mode 100644
index 5e5cd62..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-electric_stove,R1,2020,fixed,0,0,0,0,0
-gas_stove,R1,2020,fixed,0,0,0,64.71,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO7/default/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index f1520a3..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
diff --git a/case-studies/hands-on-files/HO7/default/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO7/default/technodata/residential/Technodata.csv
deleted file mode 100644
index aa4eb86..0000000
--- a/case-studies/hands-on-files/HO7/default/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
diff --git a/case-studies/hands-on-files/HO7/default_final.zip b/case-studies/hands-on-files/HO7/default_final.zip
deleted file mode 100644
index 94bc1bb..0000000
Binary files a/case-studies/hands-on-files/HO7/default_final.zip and /dev/null differ
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2020.csv b/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2020.csv
deleted file mode 100644
index d0ba9fd..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2020.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2020,gassupply1,R1,2020,15.00000000000
-0,2025,gassupply1,R1,2020,65.00000000000
-0,2030,gassupply1,R1,2020,57.50000000000
-0,2035,gassupply1,R1,2020,50.00000000000
-0,2040,gassupply1,R1,2020,50.00000000000
-0,2045,gassupply1,R1,2020,50.00000000000
-0,2050,gassupply1,R1,2020,50.00000000000
-0,2059,gassupply1,R1,2020,50.00000000000
-1,2020,gassupply1,R2,2020,15.00000000000
-1,2025,gassupply1,R2,2020,65.00000000000
-1,2030,gassupply1,R2,2020,57.50000000000
-1,2035,gassupply1,R2,2020,50.00000000000
-1,2040,gassupply1,R2,2020,50.00000000000
-1,2045,gassupply1,R2,2020,50.00000000000
-1,2050,gassupply1,R2,2020,50.00000000000
-1,2059,gassupply1,R2,2020,50.00000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2025.csv b/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2025.csv
deleted file mode 100644
index c01a535..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2025.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2025,gassupply1,R1,2020,65.00000000000
-0,2030,gassupply1,R1,2020,57.50000000000
-0,2035,gassupply1,R1,2020,50.00000000000
-0,2040,gassupply1,R1,2020,50.00000000000
-0,2045,gassupply1,R1,2020,50.00000000000
-0,2050,gassupply1,R1,2020,50.00000000000
-0,2059,gassupply1,R1,2020,50.00000000000
-1,2030,gassupply1,R1,2025,27.50000000000
-1,2035,gassupply1,R1,2025,27.50000000000
-1,2040,gassupply1,R1,2025,27.50000000000
-1,2045,gassupply1,R1,2025,27.50000000000
-1,2050,gassupply1,R1,2025,27.50000000000
-1,2059,gassupply1,R1,2025,27.50000000000
-1,2060,gassupply1,R1,2025,27.50000000000
-1,2064,gassupply1,R1,2025,27.50000000000
-2,2025,gassupply1,R2,2020,65.00000000000
-2,2030,gassupply1,R2,2020,57.50000000000
-2,2035,gassupply1,R2,2020,50.00000000000
-2,2040,gassupply1,R2,2020,50.00000000000
-2,2045,gassupply1,R2,2020,50.00000000000
-2,2050,gassupply1,R2,2020,50.00000000000
-2,2059,gassupply1,R2,2020,50.00000000000
-3,2030,gassupply1,R2,2025,27.50000000000
-3,2035,gassupply1,R2,2025,27.50000000000
-3,2040,gassupply1,R2,2025,27.50000000000
-3,2045,gassupply1,R2,2025,27.50000000000
-3,2050,gassupply1,R2,2025,27.50000000000
-3,2059,gassupply1,R2,2025,27.50000000000
-3,2060,gassupply1,R2,2025,27.50000000000
-3,2064,gassupply1,R2,2025,27.50000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2030.csv b/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2030.csv
deleted file mode 100644
index 1d4fd77..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2030.csv
+++ /dev/null
@@ -1,29 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gassupply1,57.50000000000
-0,2035,R1,2020,gassupply1,50.00000000000
-0,2040,R1,2020,gassupply1,50.00000000000
-0,2045,R1,2020,gassupply1,50.00000000000
-0,2050,R1,2020,gassupply1,50.00000000000
-0,2059,R1,2020,gassupply1,50.00000000000
-1,2030,R1,2025,gassupply1,27.50000000000
-1,2035,R1,2025,gassupply1,27.50000000000
-1,2040,R1,2025,gassupply1,27.50000000000
-1,2045,R1,2025,gassupply1,27.50000000000
-1,2050,R1,2025,gassupply1,27.50000000000
-1,2059,R1,2025,gassupply1,27.50000000000
-1,2060,R1,2025,gassupply1,27.50000000000
-1,2064,R1,2025,gassupply1,27.50000000000
-2,2030,R2,2020,gassupply1,57.50000000000
-2,2035,R2,2020,gassupply1,50.00000000000
-2,2040,R2,2020,gassupply1,50.00000000000
-2,2045,R2,2020,gassupply1,50.00000000000
-2,2050,R2,2020,gassupply1,50.00000000000
-2,2059,R2,2020,gassupply1,50.00000000000
-3,2030,R2,2025,gassupply1,27.50000000000
-3,2035,R2,2025,gassupply1,27.50000000000
-3,2040,R2,2025,gassupply1,27.50000000000
-3,2045,R2,2025,gassupply1,27.50000000000
-3,2050,R2,2025,gassupply1,27.50000000000
-3,2059,R2,2025,gassupply1,27.50000000000
-3,2060,R2,2025,gassupply1,27.50000000000
-3,2064,R2,2025,gassupply1,27.50000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2035.csv b/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2035.csv
deleted file mode 100644
index af15145..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2035.csv
+++ /dev/null
@@ -1,41 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2035,gassupply1,R1,2020,50.00000000000
-0,2040,gassupply1,R1,2020,50.00000000000
-0,2045,gassupply1,R1,2020,50.00000000000
-0,2050,gassupply1,R1,2020,50.00000000000
-0,2059,gassupply1,R1,2020,50.00000000000
-1,2035,gassupply1,R1,2025,27.50000000000
-1,2040,gassupply1,R1,2025,27.50000000000
-1,2045,gassupply1,R1,2025,27.50000000000
-1,2050,gassupply1,R1,2025,27.50000000000
-1,2059,gassupply1,R1,2025,27.50000000000
-1,2060,gassupply1,R1,2025,27.50000000000
-1,2064,gassupply1,R1,2025,27.50000000000
-2,2040,gassupply1,R1,2035,25.00000000000
-2,2045,gassupply1,R1,2035,25.00000000000
-2,2050,gassupply1,R1,2035,25.00000000000
-2,2059,gassupply1,R1,2035,25.00000000000
-2,2060,gassupply1,R1,2035,25.00000000000
-2,2064,gassupply1,R1,2035,25.00000000000
-2,2065,gassupply1,R1,2035,25.00000000000
-2,2074,gassupply1,R1,2035,25.00000000000
-3,2035,gassupply1,R2,2020,50.00000000000
-3,2040,gassupply1,R2,2020,50.00000000000
-3,2045,gassupply1,R2,2020,50.00000000000
-3,2050,gassupply1,R2,2020,50.00000000000
-3,2059,gassupply1,R2,2020,50.00000000000
-4,2035,gassupply1,R2,2025,27.50000000000
-4,2040,gassupply1,R2,2025,27.50000000000
-4,2045,gassupply1,R2,2025,27.50000000000
-4,2050,gassupply1,R2,2025,27.50000000000
-4,2059,gassupply1,R2,2025,27.50000000000
-4,2060,gassupply1,R2,2025,27.50000000000
-4,2064,gassupply1,R2,2025,27.50000000000
-5,2040,gassupply1,R2,2035,25.00000000000
-5,2045,gassupply1,R2,2035,25.00000000000
-5,2050,gassupply1,R2,2035,25.00000000000
-5,2059,gassupply1,R2,2035,25.00000000000
-5,2060,gassupply1,R2,2035,25.00000000000
-5,2064,gassupply1,R2,2035,25.00000000000
-5,2065,gassupply1,R2,2035,25.00000000000
-5,2074,gassupply1,R2,2035,25.00000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2040.csv b/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2040.csv
deleted file mode 100644
index d3ff76c..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2040.csv
+++ /dev/null
@@ -1,55 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2040,gassupply1,R1,2020,50.00000000000
-0,2045,gassupply1,R1,2020,50.00000000000
-0,2050,gassupply1,R1,2020,50.00000000000
-0,2059,gassupply1,R1,2020,50.00000000000
-1,2040,gassupply1,R1,2025,27.50000000000
-1,2045,gassupply1,R1,2025,27.50000000000
-1,2050,gassupply1,R1,2025,27.50000000000
-1,2059,gassupply1,R1,2025,27.50000000000
-1,2060,gassupply1,R1,2025,27.50000000000
-1,2064,gassupply1,R1,2025,27.50000000000
-2,2040,gassupply1,R1,2035,25.00000000000
-2,2045,gassupply1,R1,2035,25.00000000000
-2,2050,gassupply1,R1,2035,25.00000000000
-2,2059,gassupply1,R1,2035,25.00000000000
-2,2060,gassupply1,R1,2035,25.00000000000
-2,2064,gassupply1,R1,2035,25.00000000000
-2,2065,gassupply1,R1,2035,25.00000000000
-2,2074,gassupply1,R1,2035,25.00000000000
-3,2045,gassupply1,R1,2040,4.34820000000
-3,2050,gassupply1,R1,2040,4.34820000000
-3,2059,gassupply1,R1,2040,4.34820000000
-3,2060,gassupply1,R1,2040,4.34820000000
-3,2064,gassupply1,R1,2040,4.34820000000
-3,2065,gassupply1,R1,2040,4.34820000000
-3,2074,gassupply1,R1,2040,4.34820000000
-3,2075,gassupply1,R1,2040,4.34820000000
-3,2079,gassupply1,R1,2040,4.34820000000
-4,2040,gassupply1,R2,2020,50.00000000000
-4,2045,gassupply1,R2,2020,50.00000000000
-4,2050,gassupply1,R2,2020,50.00000000000
-4,2059,gassupply1,R2,2020,50.00000000000
-5,2040,gassupply1,R2,2025,27.50000000000
-5,2045,gassupply1,R2,2025,27.50000000000
-5,2050,gassupply1,R2,2025,27.50000000000
-5,2059,gassupply1,R2,2025,27.50000000000
-5,2060,gassupply1,R2,2025,27.50000000000
-5,2064,gassupply1,R2,2025,27.50000000000
-6,2040,gassupply1,R2,2035,25.00000000000
-6,2045,gassupply1,R2,2035,25.00000000000
-6,2050,gassupply1,R2,2035,25.00000000000
-6,2059,gassupply1,R2,2035,25.00000000000
-6,2060,gassupply1,R2,2035,25.00000000000
-6,2064,gassupply1,R2,2035,25.00000000000
-6,2065,gassupply1,R2,2035,25.00000000000
-6,2074,gassupply1,R2,2035,25.00000000000
-7,2045,gassupply1,R2,2040,4.34820000000
-7,2050,gassupply1,R2,2040,4.34820000000
-7,2059,gassupply1,R2,2040,4.34820000000
-7,2060,gassupply1,R2,2040,4.34820000000
-7,2064,gassupply1,R2,2040,4.34820000000
-7,2065,gassupply1,R2,2040,4.34820000000
-7,2074,gassupply1,R2,2040,4.34820000000
-7,2075,gassupply1,R2,2040,4.34820000000
-7,2079,gassupply1,R2,2040,4.34820000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2045.csv b/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2045.csv
deleted file mode 100644
index c1795f3..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2045.csv
+++ /dev/null
@@ -1,49 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2045,R1,2020,gassupply1,50.00000000000
-0,2050,R1,2020,gassupply1,50.00000000000
-0,2059,R1,2020,gassupply1,50.00000000000
-1,2045,R1,2025,gassupply1,27.50000000000
-1,2050,R1,2025,gassupply1,27.50000000000
-1,2059,R1,2025,gassupply1,27.50000000000
-1,2060,R1,2025,gassupply1,27.50000000000
-1,2064,R1,2025,gassupply1,27.50000000000
-2,2045,R1,2035,gassupply1,25.00000000000
-2,2050,R1,2035,gassupply1,25.00000000000
-2,2059,R1,2035,gassupply1,25.00000000000
-2,2060,R1,2035,gassupply1,25.00000000000
-2,2064,R1,2035,gassupply1,25.00000000000
-2,2065,R1,2035,gassupply1,25.00000000000
-2,2074,R1,2035,gassupply1,25.00000000000
-3,2045,R1,2040,gassupply1,4.34820000000
-3,2050,R1,2040,gassupply1,4.34820000000
-3,2059,R1,2040,gassupply1,4.34820000000
-3,2060,R1,2040,gassupply1,4.34820000000
-3,2064,R1,2040,gassupply1,4.34820000000
-3,2065,R1,2040,gassupply1,4.34820000000
-3,2074,R1,2040,gassupply1,4.34820000000
-3,2075,R1,2040,gassupply1,4.34820000000
-3,2079,R1,2040,gassupply1,4.34820000000
-4,2045,R2,2020,gassupply1,50.00000000000
-4,2050,R2,2020,gassupply1,50.00000000000
-4,2059,R2,2020,gassupply1,50.00000000000
-5,2045,R2,2025,gassupply1,27.50000000000
-5,2050,R2,2025,gassupply1,27.50000000000
-5,2059,R2,2025,gassupply1,27.50000000000
-5,2060,R2,2025,gassupply1,27.50000000000
-5,2064,R2,2025,gassupply1,27.50000000000
-6,2045,R2,2035,gassupply1,25.00000000000
-6,2050,R2,2035,gassupply1,25.00000000000
-6,2059,R2,2035,gassupply1,25.00000000000
-6,2060,R2,2035,gassupply1,25.00000000000
-6,2064,R2,2035,gassupply1,25.00000000000
-6,2065,R2,2035,gassupply1,25.00000000000
-6,2074,R2,2035,gassupply1,25.00000000000
-7,2045,R2,2040,gassupply1,4.34820000000
-7,2050,R2,2040,gassupply1,4.34820000000
-7,2059,R2,2040,gassupply1,4.34820000000
-7,2060,R2,2040,gassupply1,4.34820000000
-7,2064,R2,2040,gassupply1,4.34820000000
-7,2065,R2,2040,gassupply1,4.34820000000
-7,2074,R2,2040,gassupply1,4.34820000000
-7,2075,R2,2040,gassupply1,4.34820000000
-7,2079,R2,2040,gassupply1,4.34820000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2050.csv b/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2050.csv
deleted file mode 100644
index cbaf791..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Gas/Capacity/2050.csv
+++ /dev/null
@@ -1,49 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gassupply1,50.00000000000
-0,2055,R1,2020,gassupply1,50.00000000000
-0,2059,R1,2020,gassupply1,50.00000000000
-1,2050,R1,2025,gassupply1,27.50000000000
-1,2055,R1,2025,gassupply1,27.50000000000
-1,2059,R1,2025,gassupply1,27.50000000000
-1,2060,R1,2025,gassupply1,27.50000000000
-1,2064,R1,2025,gassupply1,27.50000000000
-2,2050,R1,2035,gassupply1,25.00000000000
-2,2055,R1,2035,gassupply1,25.00000000000
-2,2059,R1,2035,gassupply1,25.00000000000
-2,2060,R1,2035,gassupply1,25.00000000000
-2,2064,R1,2035,gassupply1,25.00000000000
-2,2065,R1,2035,gassupply1,25.00000000000
-2,2074,R1,2035,gassupply1,25.00000000000
-3,2050,R1,2040,gassupply1,4.34820000000
-3,2055,R1,2040,gassupply1,4.34820000000
-3,2059,R1,2040,gassupply1,4.34820000000
-3,2060,R1,2040,gassupply1,4.34820000000
-3,2064,R1,2040,gassupply1,4.34820000000
-3,2065,R1,2040,gassupply1,4.34820000000
-3,2074,R1,2040,gassupply1,4.34820000000
-3,2075,R1,2040,gassupply1,4.34820000000
-3,2079,R1,2040,gassupply1,4.34820000000
-4,2050,R2,2020,gassupply1,50.00000000000
-4,2055,R2,2020,gassupply1,50.00000000000
-4,2059,R2,2020,gassupply1,50.00000000000
-5,2050,R2,2025,gassupply1,27.50000000000
-5,2055,R2,2025,gassupply1,27.50000000000
-5,2059,R2,2025,gassupply1,27.50000000000
-5,2060,R2,2025,gassupply1,27.50000000000
-5,2064,R2,2025,gassupply1,27.50000000000
-6,2050,R2,2035,gassupply1,25.00000000000
-6,2055,R2,2035,gassupply1,25.00000000000
-6,2059,R2,2035,gassupply1,25.00000000000
-6,2060,R2,2035,gassupply1,25.00000000000
-6,2064,R2,2035,gassupply1,25.00000000000
-6,2065,R2,2035,gassupply1,25.00000000000
-6,2074,R2,2035,gassupply1,25.00000000000
-7,2050,R2,2040,gassupply1,4.34820000000
-7,2055,R2,2040,gassupply1,4.34820000000
-7,2059,R2,2040,gassupply1,4.34820000000
-7,2060,R2,2040,gassupply1,4.34820000000
-7,2064,R2,2040,gassupply1,4.34820000000
-7,2065,R2,2040,gassupply1,4.34820000000
-7,2074,R2,2040,gassupply1,4.34820000000
-7,2075,R2,2040,gassupply1,4.34820000000
-7,2079,R2,2040,gassupply1,4.34820000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/MCACapacity.csv b/case-studies/hands-on-files/HO7/default_final/Results/MCACapacity.csv
deleted file mode 100644
index 69dd6a8..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/MCACapacity.csv
+++ /dev/null
@@ -1,111 +0,0 @@
-technology,dst_region,region,agent,sector,type,year,capacity,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2020,10,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2020,10,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2020,1,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2020,1,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2020,15,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2020,15,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2025,17,,,,
-heatpump,R1,R1,A1,residential,retrofit,2025,7,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2025,17,,,,
-heatpump,R2,R2,A1,residential,retrofit,2025,7,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2025,6,,,,
-windturbine,R1,R1,A1,power,retrofit,2025,10,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2025,6,,,,
-windturbine,R2,R2,A1,power,retrofit,2025,10,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2025,65,region,(Multiple Items),,
-gassupply1,R2,R2,A1,gas,retrofit,2025,65,sector,power,,
-gasboiler,R1,R1,A1,residential,retrofit,2030,12,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2030,11,Sum of capacity,Column Labels,,
-heatpump,R1,R1,A1,residential,retrofit,2030,7,Row Labels,gasCCGT,windturbine,Grand Total
-gasboiler,R2,R2,A1,residential,retrofit,2030,12,2020,1,,1
-gasboiler,R2,R2,A1,residential,retrofit,2030,11,2025,6,10,16
-heatpump,R2,R2,A1,residential,retrofit,2030,7,2030,6.7556,10,16.7556
-gasCCGT,R1,R1,A1,power,retrofit,2030,5,2035,6.7556,10,16.7556
-gasCCGT,R1,R1,A1,power,retrofit,2030,1.7556,2040,6.7556,10,16.7556
-windturbine,R1,R1,A1,power,retrofit,2030,10,2045,6.7556,10,16.7556
-gasCCGT,R2,R2,A1,power,retrofit,2030,5,2050,6.7556,,6.7556
-gasCCGT,R2,R2,A1,power,retrofit,2030,1.7556,Grand Total,40.778,50,90.778
-windturbine,R2,R2,A1,power,retrofit,2030,10,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2030,57.5,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2030,27.5,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2030,57.5,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2030,27.5,region,R2,,
-gasboiler,R1,R1,A1,residential,retrofit,2035,11,sector,power,,
-gasboiler,R1,R1,A1,residential,retrofit,2035,13.15,,,,
-heatpump,R1,R1,A1,residential,retrofit,2035,3.85,Sum of capacity,Column Labels,,
-gasboiler,R2,R2,A1,residential,retrofit,2035,11,Row Labels,gasCCGT,windturbine,Grand Total
-gasboiler,R2,R2,A1,residential,retrofit,2035,13.15,2020,1,,1
-heatpump,R2,R2,A1,residential,retrofit,2035,3.85,2025,6,10,16
-gasCCGT,R1,R1,A1,power,retrofit,2035,5,2030,6.7556,10,16.7556
-gasCCGT,R1,R1,A1,power,retrofit,2035,1.7556,2035,6.7556,10,16.7556
-windturbine,R1,R1,A1,power,retrofit,2035,10,2040,6.7556,10,16.7556
-gasCCGT,R2,R2,A1,power,retrofit,2035,5,2045,6.7556,10,16.7556
-gasCCGT,R2,R2,A1,power,retrofit,2035,1.7556,2050,6.7556,,6.7556
-windturbine,R2,R2,A1,power,retrofit,2035,10,Grand Total,40.778,50,90.778
-gassupply1,R1,R1,A1,gas,retrofit,2035,50,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2035,27.5,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2035,50,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2035,27.5,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2040,13.15,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2040,19.415,,,,
-heatpump,R1,R1,A1,residential,retrofit,2040,3.85,,,,
-heatpump,R1,R1,A1,residential,retrofit,2040,0.385,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2040,13.15,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2040,19.415,,,,
-heatpump,R2,R2,A1,residential,retrofit,2040,3.85,,,,
-heatpump,R2,R2,A1,residential,retrofit,2040,0.385,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2040,5,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2040,1.7556,,,,
-windturbine,R1,R1,A1,power,retrofit,2040,10,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2040,5,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2040,1.7556,,,,
-windturbine,R2,R2,A1,power,retrofit,2040,10,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2040,50,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2040,27.5,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2040,25,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2040,50,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2040,27.5,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2040,25,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2045,19.415,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2045,16.0603,,,,
-heatpump,R1,R1,A1,residential,retrofit,2045,0.385,,,,
-heatpump,R1,R1,A1,residential,retrofit,2045,2.1368,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2045,19.415,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2045,16.0603,,,,
-heatpump,R2,R2,A1,residential,retrofit,2045,0.385,,,,
-heatpump,R2,R2,A1,residential,retrofit,2045,2.1368,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2045,5,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2045,1.7556,,,,
-windturbine,R1,R1,A1,power,retrofit,2045,10,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2045,5,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2045,1.7556,,,,
-windturbine,R2,R2,A1,power,retrofit,2045,10,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,50,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,27.5,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,25,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2045,4.3482,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2045,50,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2045,27.5,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2045,25,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2045,4.3482,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2050,16.0603,,,,
-gasboiler,R1,R1,A1,residential,retrofit,2050,17.7924,,,,
-heatpump,R1,R1,A1,residential,retrofit,2050,2.1368,,,,
-heatpump,R1,R1,A1,residential,retrofit,2050,0.3186,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2050,16.0603,,,,
-gasboiler,R2,R2,A1,residential,retrofit,2050,17.7924,,,,
-heatpump,R2,R2,A1,residential,retrofit,2050,2.1368,,,,
-heatpump,R2,R2,A1,residential,retrofit,2050,0.3186,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2050,5,,,,
-gasCCGT,R1,R1,A1,power,retrofit,2050,1.7556,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2050,5,,,,
-gasCCGT,R2,R2,A1,power,retrofit,2050,1.7556,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,50,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,27.5,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,25,,,,
-gassupply1,R1,R1,A1,gas,retrofit,2050,4.3482,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2050,50,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2050,27.5,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2050,25,,,,
-gassupply1,R2,R2,A1,gas,retrofit,2050,4.3482,,,,
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/MCAPrices.csv b/case-studies/hands-on-files/HO7/default_final/Results/MCAPrices.csv
deleted file mode 100644
index 1276233..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/MCAPrices.csv
+++ /dev/null
@@ -1,337 +0,0 @@
-timeslice,commodity,region,prices,year
-"('all-year', 'all-week', 'night')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'night')",electricity,R2,19.50000000000,2020
-"('all-year', 'all-week', 'night')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'night')",gas,R2,7.15280000000,2020
-"('all-year', 'all-week', 'night')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'night')",heat,R2,100.00000000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'night')",CO2f,R2,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",electricity,R2,19.50000000000,2020
-"('all-year', 'all-week', 'morning')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",gas,R2,7.15280000000,2020
-"('all-year', 'all-week', 'morning')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",heat,R2,100.00000000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'morning')",CO2f,R2,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",electricity,R2,19.50000000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",gas,R2,7.15280000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",heat,R2,100.00000000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'afternoon')",CO2f,R2,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",electricity,R2,19.50000000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",gas,R2,7.15280000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",heat,R2,100.00000000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'early-peak')",CO2f,R2,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",electricity,R2,19.50000000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",gas,R2,7.15280000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",heat,R2,100.00000000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'late-peak')",CO2f,R2,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R1,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",electricity,R2,19.50000000000,2020
-"('all-year', 'all-week', 'evening')",gas,R1,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",gas,R2,7.15280000000,2020
-"('all-year', 'all-week', 'evening')",heat,R1,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",heat,R2,100.00000000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.08310000000,2020
-"('all-year', 'all-week', 'evening')",CO2f,R2,0.08310000000,2020
-"('all-year', 'all-week', 'night')",electricity,R1,1.50720000000,2025
-"('all-year', 'all-week', 'night')",electricity,R2,1.50720000000,2025
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'night')",gas,R2,0.04720000000,2025
-"('all-year', 'all-week', 'night')",heat,R1,1.44320000000,2025
-"('all-year', 'all-week', 'night')",heat,R2,1.44320000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'night')",CO2f,R2,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R1,2.26070000000,2025
-"('all-year', 'all-week', 'morning')",electricity,R2,2.26070000000,2025
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",gas,R2,0.07080000000,2025
-"('all-year', 'all-week', 'morning')",heat,R1,2.16480000000,2025
-"('all-year', 'all-week', 'morning')",heat,R2,2.16480000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'morning')",CO2f,R2,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.50720000000,2025
-"('all-year', 'all-week', 'afternoon')",electricity,R2,1.50720000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",gas,R2,0.04720000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.44320000000,2025
-"('all-year', 'all-week', 'afternoon')",heat,R2,1.44320000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'afternoon')",CO2f,R2,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.26070000000,2025
-"('all-year', 'all-week', 'early-peak')",electricity,R2,2.26070000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",gas,R2,0.07080000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R1,2.16480000000,2025
-"('all-year', 'all-week', 'early-peak')",heat,R2,2.16480000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'early-peak')",CO2f,R2,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.52150000000,2025
-"('all-year', 'all-week', 'late-peak')",electricity,R2,4.52150000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",gas,R2,0.14170000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R1,4.32960000000,2025
-"('all-year', 'all-week', 'late-peak')",heat,R2,4.32960000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'late-peak')",CO2f,R2,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R1,3.01430000000,2025
-"('all-year', 'all-week', 'evening')",electricity,R2,3.01430000000,2025
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",gas,R2,0.09440000000,2025
-"('all-year', 'all-week', 'evening')",heat,R1,2.88640000000,2025
-"('all-year', 'all-week', 'evening')",heat,R2,2.88640000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.12010000000,2025
-"('all-year', 'all-week', 'evening')",CO2f,R2,0.12010000000,2025
-"('all-year', 'all-week', 'night')",electricity,R1,0.96650000000,2030
-"('all-year', 'all-week', 'night')",electricity,R2,0.96650000000,2030
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'night')",gas,R2,0.04720000000,2030
-"('all-year', 'all-week', 'night')",heat,R1,0.81070000000,2030
-"('all-year', 'all-week', 'night')",heat,R2,0.81070000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'night')",CO2f,R2,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R1,1.45340000000,2030
-"('all-year', 'all-week', 'morning')",electricity,R2,1.45340000000,2030
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",gas,R2,0.07080000000,2030
-"('all-year', 'all-week', 'morning')",heat,R1,1.22970000000,2030
-"('all-year', 'all-week', 'morning')",heat,R2,1.22970000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'morning')",CO2f,R2,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R1,0.96650000000,2030
-"('all-year', 'all-week', 'afternoon')",electricity,R2,0.96650000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",gas,R2,0.04720000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R1,0.81070000000,2030
-"('all-year', 'all-week', 'afternoon')",heat,R2,0.81070000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'afternoon')",CO2f,R2,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.45340000000,2030
-"('all-year', 'all-week', 'early-peak')",electricity,R2,1.45340000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",gas,R2,0.07080000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.22970000000,2030
-"('all-year', 'all-week', 'early-peak')",heat,R2,1.22970000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'early-peak')",CO2f,R2,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R1,2.92810000000,2030
-"('all-year', 'all-week', 'late-peak')",electricity,R2,2.92810000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",gas,R2,0.14170000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R1,2.54160000000,2030
-"('all-year', 'all-week', 'late-peak')",heat,R2,2.54160000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'late-peak')",CO2f,R2,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R1,1.94260000000,2030
-"('all-year', 'all-week', 'evening')",electricity,R2,1.94260000000,2030
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",gas,R2,0.09440000000,2030
-"('all-year', 'all-week', 'evening')",heat,R1,1.65790000000,2030
-"('all-year', 'all-week', 'evening')",heat,R2,1.65790000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.15700000000,2030
-"('all-year', 'all-week', 'evening')",CO2f,R2,0.15700000000,2030
-"('all-year', 'all-week', 'night')",electricity,R1,1.28450000000,2035
-"('all-year', 'all-week', 'night')",electricity,R2,1.28450000000,2035
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'night')",gas,R2,0.04720000000,2035
-"('all-year', 'all-week', 'night')",heat,R1,1.22150000000,2035
-"('all-year', 'all-week', 'night')",heat,R2,1.22150000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'night')",CO2f,R2,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R1,1.93030000000,2035
-"('all-year', 'all-week', 'morning')",electricity,R2,1.93030000000,2035
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",gas,R2,0.07080000000,2035
-"('all-year', 'all-week', 'morning')",heat,R1,1.83980000000,2035
-"('all-year', 'all-week', 'morning')",heat,R2,1.83980000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'morning')",CO2f,R2,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.28450000000,2035
-"('all-year', 'all-week', 'afternoon')",electricity,R2,1.28450000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",gas,R2,0.04720000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.22150000000,2035
-"('all-year', 'all-week', 'afternoon')",heat,R2,1.22150000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'afternoon')",CO2f,R2,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R1,1.93030000000,2035
-"('all-year', 'all-week', 'early-peak')",electricity,R2,1.93030000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",gas,R2,0.07080000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R1,1.83980000000,2035
-"('all-year', 'all-week', 'early-peak')",heat,R2,1.83980000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'early-peak')",CO2f,R2,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R1,3.88200000000,2035
-"('all-year', 'all-week', 'late-peak')",electricity,R2,3.88200000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",gas,R2,0.14170000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R1,3.72530000000,2035
-"('all-year', 'all-week', 'late-peak')",heat,R2,3.72530000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'late-peak')",CO2f,R2,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R1,2.57850000000,2035
-"('all-year', 'all-week', 'evening')",electricity,R2,2.57850000000,2035
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",gas,R2,0.09440000000,2035
-"('all-year', 'all-week', 'evening')",heat,R1,2.46320000000,2035
-"('all-year', 'all-week', 'evening')",heat,R2,2.46320000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.21490000000,2035
-"('all-year', 'all-week', 'evening')",CO2f,R2,0.21490000000,2035
-"('all-year', 'all-week', 'night')",electricity,R1,1.58600000000,2040
-"('all-year', 'all-week', 'night')",electricity,R2,1.58600000000,2040
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'night')",gas,R2,0.04720000000,2040
-"('all-year', 'all-week', 'night')",heat,R1,1.58450000000,2040
-"('all-year', 'all-week', 'night')",heat,R2,1.58450000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'night')",CO2f,R2,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R1,2.38250000000,2040
-"('all-year', 'all-week', 'morning')",electricity,R2,2.38250000000,2040
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",gas,R2,0.07080000000,2040
-"('all-year', 'all-week', 'morning')",heat,R1,2.38490000000,2040
-"('all-year', 'all-week', 'morning')",heat,R2,2.38490000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'morning')",CO2f,R2,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R1,1.58600000000,2040
-"('all-year', 'all-week', 'afternoon')",electricity,R2,1.58600000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",gas,R2,0.04720000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R1,1.58450000000,2040
-"('all-year', 'all-week', 'afternoon')",heat,R2,1.58450000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'afternoon')",CO2f,R2,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R1,2.38250000000,2040
-"('all-year', 'all-week', 'early-peak')",electricity,R2,2.38250000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",gas,R2,0.07080000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R1,2.38490000000,2040
-"('all-year', 'all-week', 'early-peak')",heat,R2,2.38490000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'early-peak')",CO2f,R2,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R1,4.78610000000,2040
-"('all-year', 'all-week', 'late-peak')",electricity,R2,4.78610000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",gas,R2,0.14170000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R1,4.81860000000,2040
-"('all-year', 'all-week', 'late-peak')",heat,R2,4.81860000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'late-peak')",CO2f,R2,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R1,3.18130000000,2040
-"('all-year', 'all-week', 'evening')",electricity,R2,3.18130000000,2040
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",gas,R2,0.09440000000,2040
-"('all-year', 'all-week', 'evening')",heat,R1,3.19070000000,2040
-"('all-year', 'all-week', 'evening')",heat,R2,3.19070000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.27280000000,2040
-"('all-year', 'all-week', 'evening')",CO2f,R2,0.27280000000,2040
-"('all-year', 'all-week', 'night')",electricity,R1,2.17300000000,2045
-"('all-year', 'all-week', 'night')",electricity,R2,2.17300000000,2045
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'night')",gas,R2,0.04720000000,2045
-"('all-year', 'all-week', 'night')",heat,R1,2.15890000000,2045
-"('all-year', 'all-week', 'night')",heat,R2,2.15890000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'night')",CO2f,R2,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R1,3.26330000000,2045
-"('all-year', 'all-week', 'morning')",electricity,R2,3.26330000000,2045
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",gas,R2,0.07080000000,2045
-"('all-year', 'all-week', 'morning')",heat,R1,3.24540000000,2045
-"('all-year', 'all-week', 'morning')",heat,R2,3.24540000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'morning')",CO2f,R2,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R1,2.17300000000,2045
-"('all-year', 'all-week', 'afternoon')",electricity,R2,2.17300000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",gas,R2,0.04720000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.15890000000,2045
-"('all-year', 'all-week', 'afternoon')",heat,R2,2.15890000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'afternoon')",CO2f,R2,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R1,3.26330000000,2045
-"('all-year', 'all-week', 'early-peak')",electricity,R2,3.26330000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",gas,R2,0.07080000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R1,3.24540000000,2045
-"('all-year', 'all-week', 'early-peak')",heat,R2,3.24540000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'early-peak')",CO2f,R2,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R1,6.54940000000,2045
-"('all-year', 'all-week', 'late-peak')",electricity,R2,6.54940000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",gas,R2,0.14170000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R1,6.53300000000,2045
-"('all-year', 'all-week', 'late-peak')",heat,R2,6.53300000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'late-peak')",CO2f,R2,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R1,4.35620000000,2045
-"('all-year', 'all-week', 'evening')",electricity,R2,4.35620000000,2045
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",gas,R2,0.09440000000,2045
-"('all-year', 'all-week', 'evening')",heat,R1,4.33660000000,2045
-"('all-year', 'all-week', 'evening')",heat,R2,4.33660000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.35390000000,2045
-"('all-year', 'all-week', 'evening')",CO2f,R2,0.35390000000,2045
-"('all-year', 'all-week', 'night')",electricity,R1,4.04250000000,2050
-"('all-year', 'all-week', 'night')",electricity,R2,4.04250000000,2050
-"('all-year', 'all-week', 'night')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'night')",gas,R2,0.04720000000,2050
-"('all-year', 'all-week', 'night')",heat,R1,2.64760000000,2050
-"('all-year', 'all-week', 'night')",heat,R2,2.64760000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'night')",CO2f,R2,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R1,6.06960000000,2050
-"('all-year', 'all-week', 'morning')",electricity,R2,6.06960000000,2050
-"('all-year', 'all-week', 'morning')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",gas,R2,0.07080000000,2050
-"('all-year', 'all-week', 'morning')",heat,R1,3.97960000000,2050
-"('all-year', 'all-week', 'morning')",heat,R2,3.97960000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'morning')",CO2f,R2,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R1,4.04250000000,2050
-"('all-year', 'all-week', 'afternoon')",electricity,R2,4.04250000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R1,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",gas,R2,0.04720000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R1,2.64760000000,2050
-"('all-year', 'all-week', 'afternoon')",heat,R2,2.64760000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'afternoon')",CO2f,R2,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R1,6.06960000000,2050
-"('all-year', 'all-week', 'early-peak')",electricity,R2,6.06960000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R1,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",gas,R2,0.07080000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R1,3.97960000000,2050
-"('all-year', 'all-week', 'early-peak')",heat,R2,3.97960000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'early-peak')",CO2f,R2,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R1,12.17470000000,2050
-"('all-year', 'all-week', 'late-peak')",electricity,R2,12.17470000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R1,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",gas,R2,0.14170000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R1,8.00890000000,2050
-"('all-year', 'all-week', 'late-peak')",heat,R2,8.00890000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'late-peak')",CO2f,R2,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R1,8.10070000000,2050
-"('all-year', 'all-week', 'evening')",electricity,R2,8.10070000000,2050
-"('all-year', 'all-week', 'evening')",gas,R1,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",gas,R2,0.09440000000,2050
-"('all-year', 'all-week', 'evening')",heat,R1,5.31720000000,2050
-"('all-year', 'all-week', 'evening')",heat,R2,5.31720000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R1,0.43510000000,2050
-"('all-year', 'all-week', 'evening')",CO2f,R2,0.43510000000,2050
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2020.csv b/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2020.csv
deleted file mode 100644
index efd7c41..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2020.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2020,R1,2020,gasCCGT,1.00000000000
-0,2025,R1,2020,gasCCGT,6.00000000000
-0,2030,R1,2020,gasCCGT,5.00000000000
-0,2035,R1,2020,gasCCGT,5.00000000000
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2025,R1,2020,windturbine,10.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2020,R2,2020,gasCCGT,1.00000000000
-2,2025,R2,2020,gasCCGT,6.00000000000
-2,2030,R2,2020,gasCCGT,5.00000000000
-2,2035,R2,2020,gasCCGT,5.00000000000
-2,2040,R2,2020,gasCCGT,5.00000000000
-2,2045,R2,2020,gasCCGT,5.00000000000
-2,2049,R2,2020,gasCCGT,5.00000000000
-2,2050,R2,2020,gasCCGT,5.00000000000
-2,2059,R2,2020,gasCCGT,5.00000000000
-3,2025,R2,2020,windturbine,10.00000000000
-3,2030,R2,2020,windturbine,10.00000000000
-3,2035,R2,2020,windturbine,10.00000000000
-3,2040,R2,2020,windturbine,10.00000000000
-3,2045,R2,2020,windturbine,10.00000000000
-3,2049,R2,2020,windturbine,10.00000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2025.csv b/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2025.csv
deleted file mode 100644
index e845b64..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2025.csv
+++ /dev/null
@@ -1,47 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2025,R1,2020,gasCCGT,6.00000000000
-0,2030,R1,2020,gasCCGT,5.00000000000
-0,2035,R1,2020,gasCCGT,5.00000000000
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2025,R1,2020,windturbine,10.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2030,R1,2025,gasCCGT,1.75560000000
-2,2035,R1,2025,gasCCGT,1.75560000000
-2,2040,R1,2025,gasCCGT,1.75560000000
-2,2045,R1,2025,gasCCGT,1.75560000000
-2,2049,R1,2025,gasCCGT,1.75560000000
-2,2050,R1,2025,gasCCGT,1.75560000000
-2,2059,R1,2025,gasCCGT,1.75560000000
-2,2060,R1,2025,gasCCGT,1.75560000000
-2,2064,R1,2025,gasCCGT,1.75560000000
-3,2025,R2,2020,gasCCGT,6.00000000000
-3,2030,R2,2020,gasCCGT,5.00000000000
-3,2035,R2,2020,gasCCGT,5.00000000000
-3,2040,R2,2020,gasCCGT,5.00000000000
-3,2045,R2,2020,gasCCGT,5.00000000000
-3,2049,R2,2020,gasCCGT,5.00000000000
-3,2050,R2,2020,gasCCGT,5.00000000000
-3,2059,R2,2020,gasCCGT,5.00000000000
-4,2025,R2,2020,windturbine,10.00000000000
-4,2030,R2,2020,windturbine,10.00000000000
-4,2035,R2,2020,windturbine,10.00000000000
-4,2040,R2,2020,windturbine,10.00000000000
-4,2045,R2,2020,windturbine,10.00000000000
-4,2049,R2,2020,windturbine,10.00000000000
-5,2030,R2,2025,gasCCGT,1.75560000000
-5,2035,R2,2025,gasCCGT,1.75560000000
-5,2040,R2,2025,gasCCGT,1.75560000000
-5,2045,R2,2025,gasCCGT,1.75560000000
-5,2049,R2,2025,gasCCGT,1.75560000000
-5,2050,R2,2025,gasCCGT,1.75560000000
-5,2059,R2,2025,gasCCGT,1.75560000000
-5,2060,R2,2025,gasCCGT,1.75560000000
-5,2064,R2,2025,gasCCGT,1.75560000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2030.csv b/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2030.csv
deleted file mode 100644
index 166c8bc..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2030.csv
+++ /dev/null
@@ -1,43 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2030,R1,2020,gasCCGT,5.00000000000
-0,2035,R1,2020,gasCCGT,5.00000000000
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2030,R1,2020,windturbine,10.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2030,R1,2025,gasCCGT,1.75560000000
-2,2035,R1,2025,gasCCGT,1.75560000000
-2,2040,R1,2025,gasCCGT,1.75560000000
-2,2045,R1,2025,gasCCGT,1.75560000000
-2,2049,R1,2025,gasCCGT,1.75560000000
-2,2050,R1,2025,gasCCGT,1.75560000000
-2,2059,R1,2025,gasCCGT,1.75560000000
-2,2060,R1,2025,gasCCGT,1.75560000000
-2,2064,R1,2025,gasCCGT,1.75560000000
-3,2030,R2,2020,gasCCGT,5.00000000000
-3,2035,R2,2020,gasCCGT,5.00000000000
-3,2040,R2,2020,gasCCGT,5.00000000000
-3,2045,R2,2020,gasCCGT,5.00000000000
-3,2049,R2,2020,gasCCGT,5.00000000000
-3,2050,R2,2020,gasCCGT,5.00000000000
-3,2059,R2,2020,gasCCGT,5.00000000000
-4,2030,R2,2020,windturbine,10.00000000000
-4,2035,R2,2020,windturbine,10.00000000000
-4,2040,R2,2020,windturbine,10.00000000000
-4,2045,R2,2020,windturbine,10.00000000000
-4,2049,R2,2020,windturbine,10.00000000000
-5,2030,R2,2025,gasCCGT,1.75560000000
-5,2035,R2,2025,gasCCGT,1.75560000000
-5,2040,R2,2025,gasCCGT,1.75560000000
-5,2045,R2,2025,gasCCGT,1.75560000000
-5,2049,R2,2025,gasCCGT,1.75560000000
-5,2050,R2,2025,gasCCGT,1.75560000000
-5,2059,R2,2025,gasCCGT,1.75560000000
-5,2060,R2,2025,gasCCGT,1.75560000000
-5,2064,R2,2025,gasCCGT,1.75560000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2035.csv b/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2035.csv
deleted file mode 100644
index 3d1f2b2..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2035.csv
+++ /dev/null
@@ -1,37 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2035,R1,2020,gasCCGT,5.00000000000
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2035,R1,2020,windturbine,10.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2035,R1,2025,gasCCGT,1.75560000000
-2,2040,R1,2025,gasCCGT,1.75560000000
-2,2045,R1,2025,gasCCGT,1.75560000000
-2,2049,R1,2025,gasCCGT,1.75560000000
-2,2050,R1,2025,gasCCGT,1.75560000000
-2,2059,R1,2025,gasCCGT,1.75560000000
-2,2060,R1,2025,gasCCGT,1.75560000000
-2,2064,R1,2025,gasCCGT,1.75560000000
-3,2035,R2,2020,gasCCGT,5.00000000000
-3,2040,R2,2020,gasCCGT,5.00000000000
-3,2045,R2,2020,gasCCGT,5.00000000000
-3,2049,R2,2020,gasCCGT,5.00000000000
-3,2050,R2,2020,gasCCGT,5.00000000000
-3,2059,R2,2020,gasCCGT,5.00000000000
-4,2035,R2,2020,windturbine,10.00000000000
-4,2040,R2,2020,windturbine,10.00000000000
-4,2045,R2,2020,windturbine,10.00000000000
-4,2049,R2,2020,windturbine,10.00000000000
-5,2035,R2,2025,gasCCGT,1.75560000000
-5,2040,R2,2025,gasCCGT,1.75560000000
-5,2045,R2,2025,gasCCGT,1.75560000000
-5,2049,R2,2025,gasCCGT,1.75560000000
-5,2050,R2,2025,gasCCGT,1.75560000000
-5,2059,R2,2025,gasCCGT,1.75560000000
-5,2060,R2,2025,gasCCGT,1.75560000000
-5,2064,R2,2025,gasCCGT,1.75560000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2040.csv b/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2040.csv
deleted file mode 100644
index 0efc77c..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2040.csv
+++ /dev/null
@@ -1,31 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2040,R1,2020,gasCCGT,5.00000000000
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2040,R1,2020,windturbine,10.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2040,R1,2025,gasCCGT,1.75560000000
-2,2045,R1,2025,gasCCGT,1.75560000000
-2,2049,R1,2025,gasCCGT,1.75560000000
-2,2050,R1,2025,gasCCGT,1.75560000000
-2,2059,R1,2025,gasCCGT,1.75560000000
-2,2060,R1,2025,gasCCGT,1.75560000000
-2,2064,R1,2025,gasCCGT,1.75560000000
-3,2040,R2,2020,gasCCGT,5.00000000000
-3,2045,R2,2020,gasCCGT,5.00000000000
-3,2049,R2,2020,gasCCGT,5.00000000000
-3,2050,R2,2020,gasCCGT,5.00000000000
-3,2059,R2,2020,gasCCGT,5.00000000000
-4,2040,R2,2020,windturbine,10.00000000000
-4,2045,R2,2020,windturbine,10.00000000000
-4,2049,R2,2020,windturbine,10.00000000000
-5,2040,R2,2025,gasCCGT,1.75560000000
-5,2045,R2,2025,gasCCGT,1.75560000000
-5,2049,R2,2025,gasCCGT,1.75560000000
-5,2050,R2,2025,gasCCGT,1.75560000000
-5,2059,R2,2025,gasCCGT,1.75560000000
-5,2060,R2,2025,gasCCGT,1.75560000000
-5,2064,R2,2025,gasCCGT,1.75560000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2045.csv b/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2045.csv
deleted file mode 100644
index b43a084..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2045.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2045,R1,2020,gasCCGT,5.00000000000
-0,2049,R1,2020,gasCCGT,5.00000000000
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-1,2045,R1,2020,windturbine,10.00000000000
-1,2049,R1,2020,windturbine,10.00000000000
-2,2045,R1,2025,gasCCGT,1.75560000000
-2,2049,R1,2025,gasCCGT,1.75560000000
-2,2050,R1,2025,gasCCGT,1.75560000000
-2,2059,R1,2025,gasCCGT,1.75560000000
-2,2060,R1,2025,gasCCGT,1.75560000000
-2,2064,R1,2025,gasCCGT,1.75560000000
-3,2045,R2,2020,gasCCGT,5.00000000000
-3,2049,R2,2020,gasCCGT,5.00000000000
-3,2050,R2,2020,gasCCGT,5.00000000000
-3,2059,R2,2020,gasCCGT,5.00000000000
-4,2045,R2,2020,windturbine,10.00000000000
-4,2049,R2,2020,windturbine,10.00000000000
-5,2045,R2,2025,gasCCGT,1.75560000000
-5,2049,R2,2025,gasCCGT,1.75560000000
-5,2050,R2,2025,gasCCGT,1.75560000000
-5,2059,R2,2025,gasCCGT,1.75560000000
-5,2060,R2,2025,gasCCGT,1.75560000000
-5,2064,R2,2025,gasCCGT,1.75560000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2050.csv b/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2050.csv
deleted file mode 100644
index cc0ad2b..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Power/Capacity/2050.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-asset,year,region,installed,technology,capacity
-0,2050,R1,2020,gasCCGT,5.00000000000
-0,2055,R1,2020,gasCCGT,5.00000000000
-0,2059,R1,2020,gasCCGT,5.00000000000
-2,2050,R1,2025,gasCCGT,1.75560000000
-2,2055,R1,2025,gasCCGT,1.75560000000
-2,2059,R1,2025,gasCCGT,1.75560000000
-2,2060,R1,2025,gasCCGT,1.75560000000
-2,2064,R1,2025,gasCCGT,1.75560000000
-3,2050,R2,2020,gasCCGT,5.00000000000
-3,2055,R2,2020,gasCCGT,5.00000000000
-3,2059,R2,2020,gasCCGT,5.00000000000
-5,2050,R2,2025,gasCCGT,1.75560000000
-5,2055,R2,2025,gasCCGT,1.75560000000
-5,2059,R2,2025,gasCCGT,1.75560000000
-5,2060,R2,2025,gasCCGT,1.75560000000
-5,2064,R2,2025,gasCCGT,1.75560000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2020.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2020.csv
deleted file mode 100644
index 03601ca..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2020.csv
+++ /dev/null
@@ -1,15 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2020,gasboiler,R1,2020,10.00000000000
-0,2025,gasboiler,R1,2020,17.00000000000
-0,2030,gasboiler,R1,2020,12.00000000000
-0,2034,gasboiler,R1,2020,12.00000000000
-1,2020,gasboiler,R2,2020,10.00000000000
-1,2025,gasboiler,R2,2020,17.00000000000
-1,2030,gasboiler,R2,2020,12.00000000000
-1,2034,gasboiler,R2,2020,12.00000000000
-2,2025,heatpump,R1,2020,7.00000000000
-2,2030,heatpump,R1,2020,7.00000000000
-2,2034,heatpump,R1,2020,7.00000000000
-3,2025,heatpump,R2,2020,7.00000000000
-3,2030,heatpump,R2,2020,7.00000000000
-3,2034,heatpump,R2,2020,7.00000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2025.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2025.csv
deleted file mode 100644
index 7325300..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2025.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2025,gasboiler,R1,2020,17.00000000000
-0,2030,gasboiler,R1,2020,12.00000000000
-0,2034,gasboiler,R1,2020,12.00000000000
-1,2030,gasboiler,R1,2025,11.00000000000
-1,2034,gasboiler,R1,2025,11.00000000000
-1,2035,gasboiler,R1,2025,11.00000000000
-1,2039,gasboiler,R1,2025,11.00000000000
-2,2025,gasboiler,R2,2020,17.00000000000
-2,2030,gasboiler,R2,2020,12.00000000000
-2,2034,gasboiler,R2,2020,12.00000000000
-3,2030,gasboiler,R2,2025,11.00000000000
-3,2034,gasboiler,R2,2025,11.00000000000
-3,2035,gasboiler,R2,2025,11.00000000000
-3,2039,gasboiler,R2,2025,11.00000000000
-4,2025,heatpump,R1,2020,7.00000000000
-4,2030,heatpump,R1,2020,7.00000000000
-4,2034,heatpump,R1,2020,7.00000000000
-5,2025,heatpump,R2,2020,7.00000000000
-5,2030,heatpump,R2,2020,7.00000000000
-5,2034,heatpump,R2,2020,7.00000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2030.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2030.csv
deleted file mode 100644
index 39a2ab1..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2030.csv
+++ /dev/null
@@ -1,33 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2030,gasboiler,R1,2020,12.00000000000
-0,2034,gasboiler,R1,2020,12.00000000000
-1,2030,gasboiler,R1,2025,11.00000000000
-1,2034,gasboiler,R1,2025,11.00000000000
-1,2035,gasboiler,R1,2025,11.00000000000
-1,2039,gasboiler,R1,2025,11.00000000000
-2,2035,gasboiler,R1,2030,13.15000000000
-2,2039,gasboiler,R1,2030,13.15000000000
-2,2040,gasboiler,R1,2030,13.15000000000
-2,2044,gasboiler,R1,2030,13.15000000000
-3,2030,gasboiler,R2,2020,12.00000000000
-3,2034,gasboiler,R2,2020,12.00000000000
-4,2030,gasboiler,R2,2025,11.00000000000
-4,2034,gasboiler,R2,2025,11.00000000000
-4,2035,gasboiler,R2,2025,11.00000000000
-4,2039,gasboiler,R2,2025,11.00000000000
-5,2035,gasboiler,R2,2030,13.15000000000
-5,2039,gasboiler,R2,2030,13.15000000000
-5,2040,gasboiler,R2,2030,13.15000000000
-5,2044,gasboiler,R2,2030,13.15000000000
-6,2030,heatpump,R1,2020,7.00000000000
-6,2034,heatpump,R1,2020,7.00000000000
-7,2035,heatpump,R1,2030,3.85000000000
-7,2039,heatpump,R1,2030,3.85000000000
-7,2040,heatpump,R1,2030,3.85000000000
-7,2044,heatpump,R1,2030,3.85000000000
-8,2030,heatpump,R2,2020,7.00000000000
-8,2034,heatpump,R2,2020,7.00000000000
-9,2035,heatpump,R2,2030,3.85000000000
-9,2039,heatpump,R2,2030,3.85000000000
-9,2040,heatpump,R2,2030,3.85000000000
-9,2044,heatpump,R2,2030,3.85000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2035.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2035.csv
deleted file mode 100644
index 532182a..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2035.csv
+++ /dev/null
@@ -1,37 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2035,gasboiler,R1,2025,11.00000000000
-0,2039,gasboiler,R1,2025,11.00000000000
-1,2035,gasboiler,R1,2030,13.15000000000
-1,2039,gasboiler,R1,2030,13.15000000000
-1,2040,gasboiler,R1,2030,13.15000000000
-1,2044,gasboiler,R1,2030,13.15000000000
-2,2040,gasboiler,R1,2035,19.41500000000
-2,2044,gasboiler,R1,2035,19.41500000000
-2,2045,gasboiler,R1,2035,19.41500000000
-2,2049,gasboiler,R1,2035,19.41500000000
-3,2035,gasboiler,R2,2025,11.00000000000
-3,2039,gasboiler,R2,2025,11.00000000000
-4,2035,gasboiler,R2,2030,13.15000000000
-4,2039,gasboiler,R2,2030,13.15000000000
-4,2040,gasboiler,R2,2030,13.15000000000
-4,2044,gasboiler,R2,2030,13.15000000000
-5,2040,gasboiler,R2,2035,19.41500000000
-5,2044,gasboiler,R2,2035,19.41500000000
-5,2045,gasboiler,R2,2035,19.41500000000
-5,2049,gasboiler,R2,2035,19.41500000000
-6,2035,heatpump,R1,2030,3.85000000000
-6,2039,heatpump,R1,2030,3.85000000000
-6,2040,heatpump,R1,2030,3.85000000000
-6,2044,heatpump,R1,2030,3.85000000000
-7,2040,heatpump,R1,2035,0.38500000000
-7,2044,heatpump,R1,2035,0.38500000000
-7,2045,heatpump,R1,2035,0.38500000000
-7,2049,heatpump,R1,2035,0.38500000000
-8,2035,heatpump,R2,2030,3.85000000000
-8,2039,heatpump,R2,2030,3.85000000000
-8,2040,heatpump,R2,2030,3.85000000000
-8,2044,heatpump,R2,2030,3.85000000000
-9,2040,heatpump,R2,2035,0.38500000000
-9,2044,heatpump,R2,2035,0.38500000000
-9,2045,heatpump,R2,2035,0.38500000000
-9,2049,heatpump,R2,2035,0.38500000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2040.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2040.csv
deleted file mode 100644
index 0722af5..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2040.csv
+++ /dev/null
@@ -1,41 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2040,gasboiler,R1,2030,13.15000000000
-0,2044,gasboiler,R1,2030,13.15000000000
-1,2040,gasboiler,R1,2035,19.41500000000
-1,2044,gasboiler,R1,2035,19.41500000000
-1,2045,gasboiler,R1,2035,19.41500000000
-1,2049,gasboiler,R1,2035,19.41500000000
-2,2045,gasboiler,R1,2040,16.06030000000
-2,2049,gasboiler,R1,2040,16.06030000000
-2,2050,gasboiler,R1,2040,16.06030000000
-2,2054,gasboiler,R1,2040,16.06030000000
-3,2040,gasboiler,R2,2030,13.15000000000
-3,2044,gasboiler,R2,2030,13.15000000000
-4,2040,gasboiler,R2,2035,19.41500000000
-4,2044,gasboiler,R2,2035,19.41500000000
-4,2045,gasboiler,R2,2035,19.41500000000
-4,2049,gasboiler,R2,2035,19.41500000000
-5,2045,gasboiler,R2,2040,16.06030000000
-5,2049,gasboiler,R2,2040,16.06030000000
-5,2050,gasboiler,R2,2040,16.06030000000
-5,2054,gasboiler,R2,2040,16.06030000000
-6,2040,heatpump,R1,2030,3.85000000000
-6,2044,heatpump,R1,2030,3.85000000000
-7,2040,heatpump,R1,2035,0.38500000000
-7,2044,heatpump,R1,2035,0.38500000000
-7,2045,heatpump,R1,2035,0.38500000000
-7,2049,heatpump,R1,2035,0.38500000000
-8,2045,heatpump,R1,2040,2.13680000000
-8,2049,heatpump,R1,2040,2.13680000000
-8,2050,heatpump,R1,2040,2.13680000000
-8,2054,heatpump,R1,2040,2.13680000000
-9,2040,heatpump,R2,2030,3.85000000000
-9,2044,heatpump,R2,2030,3.85000000000
-10,2040,heatpump,R2,2035,0.38500000000
-10,2044,heatpump,R2,2035,0.38500000000
-10,2045,heatpump,R2,2035,0.38500000000
-10,2049,heatpump,R2,2035,0.38500000000
-11,2045,heatpump,R2,2040,2.13680000000
-11,2049,heatpump,R2,2040,2.13680000000
-11,2050,heatpump,R2,2040,2.13680000000
-11,2054,heatpump,R2,2040,2.13680000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2045.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2045.csv
deleted file mode 100644
index fbdc1bd..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2045.csv
+++ /dev/null
@@ -1,41 +0,0 @@
-asset,year,technology,region,installed,capacity
-0,2045,gasboiler,R1,2035,19.41500000000
-0,2049,gasboiler,R1,2035,19.41500000000
-1,2045,gasboiler,R1,2040,16.06030000000
-1,2049,gasboiler,R1,2040,16.06030000000
-1,2050,gasboiler,R1,2040,16.06030000000
-1,2054,gasboiler,R1,2040,16.06030000000
-2,2050,gasboiler,R1,2045,17.79240000000
-2,2054,gasboiler,R1,2045,17.79240000000
-2,2055,gasboiler,R1,2045,17.79240000000
-2,2059,gasboiler,R1,2045,17.79240000000
-3,2045,gasboiler,R2,2035,19.41500000000
-3,2049,gasboiler,R2,2035,19.41500000000
-4,2045,gasboiler,R2,2040,16.06030000000
-4,2049,gasboiler,R2,2040,16.06030000000
-4,2050,gasboiler,R2,2040,16.06030000000
-4,2054,gasboiler,R2,2040,16.06030000000
-5,2050,gasboiler,R2,2045,17.79240000000
-5,2054,gasboiler,R2,2045,17.79240000000
-5,2055,gasboiler,R2,2045,17.79240000000
-5,2059,gasboiler,R2,2045,17.79240000000
-6,2045,heatpump,R1,2035,0.38500000000
-6,2049,heatpump,R1,2035,0.38500000000
-7,2045,heatpump,R1,2040,2.13680000000
-7,2049,heatpump,R1,2040,2.13680000000
-7,2050,heatpump,R1,2040,2.13680000000
-7,2054,heatpump,R1,2040,2.13680000000
-8,2050,heatpump,R1,2045,0.31860000000
-8,2054,heatpump,R1,2045,0.31860000000
-8,2055,heatpump,R1,2045,0.31860000000
-8,2059,heatpump,R1,2045,0.31860000000
-9,2045,heatpump,R2,2035,0.38500000000
-9,2049,heatpump,R2,2035,0.38500000000
-10,2045,heatpump,R2,2040,2.13680000000
-10,2049,heatpump,R2,2040,2.13680000000
-10,2050,heatpump,R2,2040,2.13680000000
-10,2054,heatpump,R2,2040,2.13680000000
-11,2050,heatpump,R2,2045,0.31860000000
-11,2054,heatpump,R2,2045,0.31860000000
-11,2055,heatpump,R2,2045,0.31860000000
-11,2059,heatpump,R2,2045,0.31860000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2050.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2050.csv
deleted file mode 100644
index bf0b207..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Capacity/2050.csv
+++ /dev/null
@@ -1,41 +0,0 @@
-asset,year,region,installed,technology,capacity
-2,2050,R1,2040,gasboiler,16.06030000000
-2,2054,R1,2040,gasboiler,16.06030000000
-3,2050,R1,2040,heatpump,2.13680000000
-3,2054,R1,2040,heatpump,2.13680000000
-4,2050,R1,2045,gasboiler,17.79240000000
-4,2054,R1,2045,gasboiler,17.79240000000
-4,2055,R1,2045,gasboiler,17.79240000000
-4,2059,R1,2045,gasboiler,17.79240000000
-5,2050,R1,2045,heatpump,0.31860000000
-5,2054,R1,2045,heatpump,0.31860000000
-5,2055,R1,2045,heatpump,0.31860000000
-5,2059,R1,2045,heatpump,0.31860000000
-6,2055,R1,2050,gasboiler,10.76660000000
-6,2059,R1,2050,gasboiler,10.76660000000
-6,2060,R1,2050,gasboiler,10.76660000000
-6,2064,R1,2050,gasboiler,10.76660000000
-7,2055,R1,2050,heatpump,1.30570000000
-7,2059,R1,2050,heatpump,1.30570000000
-7,2060,R1,2050,heatpump,1.30570000000
-7,2064,R1,2050,heatpump,1.30570000000
-10,2050,R2,2040,gasboiler,16.06030000000
-10,2054,R2,2040,gasboiler,16.06030000000
-11,2050,R2,2040,heatpump,2.13680000000
-11,2054,R2,2040,heatpump,2.13680000000
-12,2050,R2,2045,gasboiler,17.79240000000
-12,2054,R2,2045,gasboiler,17.79240000000
-12,2055,R2,2045,gasboiler,17.79240000000
-12,2059,R2,2045,gasboiler,17.79240000000
-13,2050,R2,2045,heatpump,0.31860000000
-13,2054,R2,2045,heatpump,0.31860000000
-13,2055,R2,2045,heatpump,0.31860000000
-13,2059,R2,2045,heatpump,0.31860000000
-14,2055,R2,2050,gasboiler,10.76660000000
-14,2059,R2,2050,gasboiler,10.76660000000
-14,2060,R2,2050,gasboiler,10.76660000000
-14,2064,R2,2050,gasboiler,10.76660000000
-15,2055,R2,2050,heatpump,1.30570000000
-15,2059,R2,2050,heatpump,1.30570000000
-15,2060,R2,2050,heatpump,1.30570000000
-15,2064,R2,2050,heatpump,1.30570000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2020.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2020.csv
deleted file mode 100644
index 16174d5..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2020.csv
+++ /dev/null
@@ -1,11 +0,0 @@
-commodity,asset,year,technology,installed,supply
-heat,0,2020,gasboiler,2020,10.00000000000
-heat,0,2025,gasboiler,2020,13.33330000000
-heat,1,2020,gasboiler,2020,10.00000000000
-heat,1,2025,gasboiler,2020,13.33330000000
-heat,2,2025,heatpump,2020,7.00000000000
-heat,3,2025,heatpump,2020,7.00000000000
-CO2f,0,2020,gasboiler,2020,647.10000000000
-CO2f,0,2025,gasboiler,2020,862.80000000000
-CO2f,1,2020,gasboiler,2020,647.10000000000
-CO2f,1,2025,gasboiler,2020,862.80000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2025.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2025.csv
deleted file mode 100644
index c6df2b2..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2025.csv
+++ /dev/null
@@ -1,17 +0,0 @@
-commodity,asset,year,technology,installed,supply
-heat,0,2025,gasboiler,2020,13.33330000000
-heat,0,2030,gasboiler,2020,12.00000000000
-heat,1,2030,gasboiler,2025,11.00000000000
-heat,2,2025,gasboiler,2020,13.33330000000
-heat,2,2030,gasboiler,2020,12.00000000000
-heat,3,2030,gasboiler,2025,11.00000000000
-heat,4,2025,heatpump,2020,7.00000000000
-heat,4,2030,heatpump,2020,7.00000000000
-heat,5,2025,heatpump,2020,7.00000000000
-heat,5,2030,heatpump,2020,7.00000000000
-CO2f,0,2025,gasboiler,2020,862.80000000000
-CO2f,0,2030,gasboiler,2020,776.52000000000
-CO2f,1,2030,gasboiler,2025,711.81000000000
-CO2f,2,2025,gasboiler,2020,862.80000000000
-CO2f,2,2030,gasboiler,2020,776.52000000000
-CO2f,3,2030,gasboiler,2025,711.81000000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2030.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2030.csv
deleted file mode 100644
index b84c2ba..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2030.csv
+++ /dev/null
@@ -1,21 +0,0 @@
-commodity,asset,year,technology,installed,supply
-heat,0,2030,gasboiler,2020,12.00000000000
-heat,1,2030,gasboiler,2025,11.00000000000
-heat,1,2035,gasboiler,2025,11.00000000000
-heat,2,2035,gasboiler,2030,13.15000000000
-heat,3,2030,gasboiler,2020,12.00000000000
-heat,4,2030,gasboiler,2025,11.00000000000
-heat,4,2035,gasboiler,2025,11.00000000000
-heat,5,2035,gasboiler,2030,13.15000000000
-heat,6,2030,heatpump,2020,7.00000000000
-heat,7,2035,heatpump,2030,3.85000000000
-heat,8,2030,heatpump,2020,7.00000000000
-heat,9,2035,heatpump,2030,3.85000000000
-CO2f,0,2030,gasboiler,2020,776.52000000000
-CO2f,1,2030,gasboiler,2025,711.81000000000
-CO2f,1,2035,gasboiler,2025,711.81000000000
-CO2f,2,2035,gasboiler,2030,850.93650000000
-CO2f,3,2030,gasboiler,2020,776.52000000000
-CO2f,4,2030,gasboiler,2025,711.81000000000
-CO2f,4,2035,gasboiler,2025,711.81000000000
-CO2f,5,2035,gasboiler,2030,850.93650000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2035.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2035.csv
deleted file mode 100644
index f688469..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2035.csv
+++ /dev/null
@@ -1,23 +0,0 @@
-commodity,asset,year,technology,installed,supply
-heat,0,2035,gasboiler,2025,11.00000000000
-heat,1,2035,gasboiler,2030,13.15000000000
-heat,1,2040,gasboiler,2030,13.15000000000
-heat,2,2040,gasboiler,2035,19.41500000000
-heat,3,2035,gasboiler,2025,11.00000000000
-heat,4,2035,gasboiler,2030,13.15000000000
-heat,4,2040,gasboiler,2030,13.15000000000
-heat,5,2040,gasboiler,2035,19.41500000000
-heat,6,2035,heatpump,2030,3.85000000000
-heat,6,2040,heatpump,2030,3.85000000000
-heat,7,2040,heatpump,2035,0.38500000000
-heat,8,2035,heatpump,2030,3.85000000000
-heat,8,2040,heatpump,2030,3.85000000000
-heat,9,2040,heatpump,2035,0.38500000000
-CO2f,0,2035,gasboiler,2025,711.81000000000
-CO2f,1,2035,gasboiler,2030,850.93650000000
-CO2f,1,2040,gasboiler,2030,850.93650000000
-CO2f,2,2040,gasboiler,2035,1256.34470000000
-CO2f,3,2035,gasboiler,2025,711.81000000000
-CO2f,4,2035,gasboiler,2030,850.93650000000
-CO2f,4,2040,gasboiler,2030,850.93650000000
-CO2f,5,2040,gasboiler,2035,1256.34470000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2040.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2040.csv
deleted file mode 100644
index cb306bc..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2040.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-commodity,asset,year,technology,installed,supply
-heat,0,2040,gasboiler,2030,13.15000000000
-heat,1,2040,gasboiler,2035,19.41500000000
-heat,1,2045,gasboiler,2035,19.41500000000
-heat,2,2045,gasboiler,2040,16.06030000000
-heat,3,2040,gasboiler,2030,13.15000000000
-heat,4,2040,gasboiler,2035,19.41500000000
-heat,4,2045,gasboiler,2035,19.41500000000
-heat,5,2045,gasboiler,2040,16.06030000000
-heat,6,2040,heatpump,2030,3.85000000000
-heat,7,2040,heatpump,2035,0.38500000000
-heat,7,2045,heatpump,2035,0.38500000000
-heat,8,2045,heatpump,2040,2.13680000000
-heat,9,2040,heatpump,2030,3.85000000000
-heat,10,2040,heatpump,2035,0.38500000000
-heat,10,2045,heatpump,2035,0.38500000000
-heat,11,2045,heatpump,2040,2.13680000000
-CO2f,0,2040,gasboiler,2030,850.93650000000
-CO2f,1,2040,gasboiler,2035,1256.34470000000
-CO2f,1,2045,gasboiler,2035,1256.34460000000
-CO2f,2,2045,gasboiler,2040,1039.26260000000
-CO2f,3,2040,gasboiler,2030,850.93650000000
-CO2f,4,2040,gasboiler,2035,1256.34470000000
-CO2f,4,2045,gasboiler,2035,1256.34460000000
-CO2f,5,2045,gasboiler,2040,1039.26260000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2045.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2045.csv
deleted file mode 100644
index 65a6acb..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2045.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-commodity,asset,year,technology,installed,supply
-heat,0,2045,gasboiler,2035,19.41500000000
-heat,1,2045,gasboiler,2040,16.06030000000
-heat,1,2050,gasboiler,2040,16.06030000000
-heat,2,2050,gasboiler,2045,17.79240000000
-heat,3,2045,gasboiler,2035,19.41500000000
-heat,4,2045,gasboiler,2040,16.06030000000
-heat,4,2050,gasboiler,2040,16.06030000000
-heat,5,2050,gasboiler,2045,17.79240000000
-heat,6,2045,heatpump,2035,0.38500000000
-heat,7,2045,heatpump,2040,2.13680000000
-heat,7,2050,heatpump,2040,2.13680000000
-heat,8,2050,heatpump,2045,0.31860000000
-heat,9,2045,heatpump,2035,0.38500000000
-heat,10,2045,heatpump,2040,2.13680000000
-heat,10,2050,heatpump,2040,2.13680000000
-heat,11,2050,heatpump,2045,0.31860000000
-CO2f,0,2045,gasboiler,2035,1256.34460000000
-CO2f,1,2045,gasboiler,2040,1039.26260000000
-CO2f,1,2050,gasboiler,2040,1039.26260000000
-CO2f,2,2050,gasboiler,2045,1151.34470000000
-CO2f,3,2045,gasboiler,2035,1256.34460000000
-CO2f,4,2045,gasboiler,2040,1039.26260000000
-CO2f,4,2050,gasboiler,2040,1039.26260000000
-CO2f,5,2050,gasboiler,2045,1151.34470000000
diff --git a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2050.csv b/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2050.csv
deleted file mode 100644
index 41ffcfd..0000000
--- a/case-studies/hands-on-files/HO7/default_final/Results/Residential/Supply/2050.csv
+++ /dev/null
@@ -1,25 +0,0 @@
-commodity,asset,year,installed,technology,supply
-heat,2,2050,2040,gasboiler,16.06030000000
-heat,3,2050,2040,heatpump,2.13680000000
-heat,4,2050,2045,gasboiler,17.79240000000
-heat,4,2055,2045,gasboiler,17.79240000000
-heat,5,2050,2045,heatpump,0.31860000000
-heat,5,2055,2045,heatpump,0.31860000000
-heat,6,2055,2050,gasboiler,10.76660000000
-heat,7,2055,2050,heatpump,1.30570000000
-heat,10,2050,2040,gasboiler,16.06030000000
-heat,11,2050,2040,heatpump,2.13680000000
-heat,12,2050,2045,gasboiler,17.79240000000
-heat,12,2055,2045,gasboiler,17.79240000000
-heat,13,2050,2045,heatpump,0.31860000000
-heat,13,2055,2045,heatpump,0.31860000000
-heat,14,2055,2050,gasboiler,10.76660000000
-heat,15,2055,2050,heatpump,1.30570000000
-CO2f,2,2050,2040,gasboiler,1039.26260000000
-CO2f,4,2050,2045,gasboiler,1151.34470000000
-CO2f,4,2055,2045,gasboiler,1151.34470000000
-CO2f,6,2055,2050,gasboiler,696.70440000000
-CO2f,10,2050,2040,gasboiler,1039.26260000000
-CO2f,12,2050,2045,gasboiler,1151.34470000000
-CO2f,12,2055,2045,gasboiler,1151.34470000000
-CO2f,14,2055,2050,gasboiler,696.70440000000
diff --git a/case-studies/hands-on-files/HO7/default_final/input/BaseYearExport.csv b/case-studies/hands-on-files/HO7/default_final/input/BaseYearExport.csv
deleted file mode 100644
index 6954d98..0000000
--- a/case-studies/hands-on-files/HO7/default_final/input/BaseYearExport.csv
+++ /dev/null
@@ -1,40 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Exports,2010,0,0,0,0,0
-R1,Exports,2015,0,0,0,0,0
-R1,Exports,2020,0,0,0,0,0
-R1,Exports,2025,0,0,0,0,0
-R1,Exports,2030,0,0,0,0,0
-R1,Exports,2035,0,0,0,0,0
-R1,Exports,2040,0,0,0,0,0
-R1,Exports,2045,0,0,0,0,0
-R1,Exports,2050,0,0,0,0,0
-R1,Exports,2055,0,0,0,0,0
-R1,Exports,2060,0,0,0,0,0
-R1,Exports,2065,0,0,0,0,0
-R1,Exports,2070,0,0,0,0,0
-R1,Exports,2075,0,0,0,0,0
-R1,Exports,2080,0,0,0,0,0
-R1,Exports,2085,0,0,0,0,0
-R1,Exports,2090,0,0,0,0,0
-R1,Exports,2095,0,0,0,0,0
-R1,Exports,2100,0,0,0,0,0
-R2,Exports,2010,0,0,0,0,0
-R2,Exports,2015,0,0,0,0,0
-R2,Exports,2020,0,0,0,0,0
-R2,Exports,2025,0,0,0,0,0
-R2,Exports,2030,0,0,0,0,0
-R2,Exports,2035,0,0,0,0,0
-R2,Exports,2040,0,0,0,0,0
-R2,Exports,2045,0,0,0,0,0
-R2,Exports,2050,0,0,0,0,0
-R2,Exports,2055,0,0,0,0,0
-R2,Exports,2060,0,0,0,0,0
-R2,Exports,2065,0,0,0,0,0
-R2,Exports,2070,0,0,0,0,0
-R2,Exports,2075,0,0,0,0,0
-R2,Exports,2080,0,0,0,0,0
-R2,Exports,2085,0,0,0,0,0
-R2,Exports,2090,0,0,0,0,0
-R2,Exports,2095,0,0,0,0,0
-R2,Exports,2100,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/input/BaseYearImport.csv b/case-studies/hands-on-files/HO7/default_final/input/BaseYearImport.csv
deleted file mode 100644
index dee99f0..0000000
--- a/case-studies/hands-on-files/HO7/default_final/input/BaseYearImport.csv
+++ /dev/null
@@ -1,40 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,PJ,PJ,PJ,kt,PJ
-R1,Imports,2010,0,0,0,0,0
-R1,Imports,2015,0,0,0,0,0
-R1,Imports,2020,0,0,0,0,0
-R1,Imports,2025,0,0,0,0,0
-R1,Imports,2030,0,0,0,0,0
-R1,Imports,2035,0,0,0,0,0
-R1,Imports,2040,0,0,0,0,0
-R1,Imports,2045,0,0,0,0,0
-R1,Imports,2050,0,0,0,0,0
-R1,Imports,2055,0,0,0,0,0
-R1,Imports,2060,0,0,0,0,0
-R1,Imports,2065,0,0,0,0,0
-R1,Imports,2070,0,0,0,0,0
-R1,Imports,2075,0,0,0,0,0
-R1,Imports,2080,0,0,0,0,0
-R1,Imports,2085,0,0,0,0,0
-R1,Imports,2090,0,0,0,0,0
-R1,Imports,2095,0,0,0,0,0
-R1,Imports,2100,0,0,0,0,0
-R2,Imports,2010,0,0,0,0,0
-R2,Imports,2015,0,0,0,0,0
-R2,Imports,2020,0,0,0,0,0
-R2,Imports,2025,0,0,0,0,0
-R2,Imports,2030,0,0,0,0,0
-R2,Imports,2035,0,0,0,0,0
-R2,Imports,2040,0,0,0,0,0
-R2,Imports,2045,0,0,0,0,0
-R2,Imports,2050,0,0,0,0,0
-R2,Imports,2055,0,0,0,0,0
-R2,Imports,2060,0,0,0,0,0
-R2,Imports,2065,0,0,0,0,0
-R2,Imports,2070,0,0,0,0,0
-R2,Imports,2075,0,0,0,0,0
-R2,Imports,2080,0,0,0,0,0
-R2,Imports,2085,0,0,0,0,0
-R2,Imports,2090,0,0,0,0,0
-R2,Imports,2095,0,0,0,0,0
-R2,Imports,2100,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/input/GlobalCommodities.csv b/case-studies/hands-on-files/HO7/default_final/input/GlobalCommodities.csv
deleted file mode 100644
index 0d4c58d..0000000
--- a/case-studies/hands-on-files/HO7/default_final/input/GlobalCommodities.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-Commodity,CommodityType,CommodityName,CommodityEmissionFactor_CO2,HeatRate,Unit
-Electricity,Energy,electricity,0,1,PJ
-Gas,Energy,gas,56.1,1,PJ
-Heat,Energy,heat,0,1,PJ
-Wind,Energy,wind,0,1,PJ
-CO2fuelcomsbustion,Environmental,CO2f,0,1,kt
diff --git a/case-studies/hands-on-files/HO7/default_final/input/Projections.csv b/case-studies/hands-on-files/HO7/default_final/input/Projections.csv
deleted file mode 100644
index f435a60..0000000
--- a/case-studies/hands-on-files/HO7/default_final/input/Projections.csv
+++ /dev/null
@@ -1,40 +0,0 @@
-RegionName,Attribute,Time,electricity,gas,heat,CO2f,wind
-Unit,-,Year,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/PJ,MUS$2010/kt,MUS$2010/kt
-R1,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R1,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R1,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R1,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R1,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R1,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R1,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R1,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R1,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R1,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R1,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R1,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R1,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R1,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R1,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R1,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R1,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R1,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R1,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
-R2,CommodityPrice,2010,14.81481472,6.6759,100,0,0
-R2,CommodityPrice,2015,17.89814806,6.914325,100,0.052913851,0
-R2,CommodityPrice,2020,19.5,7.15275,100,0.08314119,0
-R2,CommodityPrice,2025,21.93518528,8.10645,100,0.120069795,0
-R2,CommodityPrice,2030,26.50925917,9.06015,100,0.156998399,0
-R2,CommodityPrice,2035,26.51851861,9.2191,100,0.214877567,0
-R2,CommodityPrice,2040,23.85185194,9.37805,100,0.272756734,0
-R2,CommodityPrice,2045,23.97222222,9.193829337,100,0.35394801,0
-R2,CommodityPrice,2050,24.06481472,9.009608674,100,0.435139285,0
-R2,CommodityPrice,2055,25.3425925,8.832625604,100,0.542365578,0
-R2,CommodityPrice,2060,25.53703694,8.655642534,100,0.649591871,0
-R2,CommodityPrice,2065,25.32407417,8.485612708,100,0.780892624,0
-R2,CommodityPrice,2070,23.36111111,8.315582883,100,0.912193378,0
-R2,CommodityPrice,2075,22.27777778,8.152233126,100,1.078321687,0
-R2,CommodityPrice,2080,22.25925917,7.988883368,100,1.244449995,0
-R2,CommodityPrice,2085,22.17592583,7.831951236,100,1.4253503,0
-R2,CommodityPrice,2090,22.03703694,7.675019103,100,1.606250604,0
-R2,CommodityPrice,2095,21.94444444,7.524252461,100,1.73877515,0
-R2,CommodityPrice,2100,21.39814806,7.373485819,100,1.871299697,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/settings.toml b/case-studies/hands-on-files/HO7/default_final/settings.toml
deleted file mode 100644
index 41e6521..0000000
--- a/case-studies/hands-on-files/HO7/default_final/settings.toml
+++ /dev/null
@@ -1,146 +0,0 @@
-# Global settings - most REQUIRED
-time_framework = [2020, 2025, 2030, 2035, 2040, 2045, 2050]
-foresight = 5 # Has to be a multiple of the minimum separation between the years in time framework
-regions = ["R1", "R2"]
-interest_rate = 0.1
-interpolation_mode = 'Active'
-log_level = 'info'
-
-# Convergence parameters
-equilibrium_variable = 'demand'
-maximum_iterations = 100
-tolerance = 0.1
-tolerance_unmet_demand = -0.1
-
-[[outputs]]
-quantity = "prices"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-
-[[outputs]]
-quantity = "capacity"
-sink = "aggregate"
-filename = "{cwd}/{default_output_dir}/MCA{Quantity}.csv"
-index = false
-keep_columns = ['technology', 'dst_region', 'region', 'agent', 'sector', 'type', 'year', 'capacity']
-
-# Carbon budget control
-[carbon_budget_control]
-budget = []
-
-[global_input_files]
-projections = '{path}/input/Projections.csv'
-global_commodities = '{path}/input/GlobalCommodities.csv'
-
-
-[sectors.residential]
-type = 'default'
-priority = 1
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/residential/Technodata.csv'
-commodities_in = '{path}/technodata/residential/CommIn.csv'
-commodities_out = '{path}/technodata/residential/CommOut.csv'
-
-[sectors.residential.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/residential/ExistingCapacity.csv'
-lpsolver = "adhoc" # Optional, defaults to "adhoc"
-constraints = [ # Optional, defaults to the constraints below
- "max_production",
- "max_capacity_expansion",
- "demand",
- "search_space",
-]
-demand_share = "new_and_retro" # Optional, default to new_and_retro
-forecast = 5 # Optional, defaults to 5
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.residential.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity.name = "supply"
-quantity.sum_over = "timeslice"
-quantity.drop = ["comm_usage", "units_prices"]
-sink = 'csv'
-overwrite = true
-
-
-[[sectors.residential.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-
-[sectors.power.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/power/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-[[sectors.power.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.power.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.gas]
-type = 'default'
-priority = 3
-dispatch_production = 'share'
-
-technodata = '{path}/technodata/gas/Technodata.csv'
-commodities_in = '{path}/technodata/gas/CommIn.csv'
-commodities_out = '{path}/technodata/gas/CommOut.csv'
-
-[sectors.gas.subsectors.retro_and_new]
-agents = '{path}/technodata/Agents.csv'
-existing_capacity = '{path}/technodata/gas/ExistingCapacity.csv'
-lpsolver = "adhoc"
-
-
-[[sectors.gas.outputs]]
-filename = '{cwd}/{default_output_dir}/{Sector}/{Quantity}/{year}{suffix}'
-quantity = "capacity"
-sink = 'csv'
-overwrite = true
-index = false
-
-[[sectors.gas.interactions]]
-net = 'new_to_retro'
-interaction = 'transfer'
-
-
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-
-
-[timeslices]
-all-year.all-week.night = 1460
-all-year.all-week.morning = 1460
-all-year.all-week.afternoon = 1460
-all-year.all-week.early-peak = 1460
-all-year.all-week.late-peak = 1460
-all-year.all-week.evening = 1460
-level_names = ["month", "day", "hour"]
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/Agents.csv b/case-studies/hands-on-files/HO7/default_final/technodata/Agents.csv
deleted file mode 100644
index 672fcc2..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/Agents.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-AgentShare,Name,RegionName,Objective1,Objective2,Objective3,ObjData1,ObjData2,ObjData3,Objsort1,Objsort2,Objsort3,SearchRule,DecisionMethod,Quantity,MaturityThreshold,Budget,Type
-Agent1,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R1,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
-Agent1,A1,R2,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,New
-Agent2,A1,R2,LCOE,,,1,,,FALSE,,,all,singleObj,1,-1,inf,Retrofit
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/gas/CommIn.csv b/case-studies/hands-on-files/HO7/default_final/technodata/gas/CommIn.csv
deleted file mode 100644
index 8a96d57..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/gas/CommIn.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,0,0,0,0
-gassupply1,R2,2020,fixed,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/gas/CommOut.csv b/case-studies/hands-on-files/HO7/default_final/technodata/gas/CommOut.csv
deleted file mode 100644
index f05a0c2..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/gas/CommOut.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gassupply1,R1,2020,fixed,0,1,0,0,0
-gassupply1,R2,2020,fixed,0,1,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/gas/ExistingCapacity.csv b/case-studies/hands-on-files/HO7/default_final/technodata/gas/ExistingCapacity.csv
deleted file mode 100644
index c83a5f0..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/gas/ExistingCapacity.csv
+++ /dev/null
@@ -1,3 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gassupply1,R1,PJ/y,15,15,7.5,0,0,0,0
-gassupply1,R2,PJ/y,15,15,7.5,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/gas/Technodata.csv b/case-studies/hands-on-files/HO7/default_final/technodata/gas/Technodata.csv
deleted file mode 100644
index 27c0070..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/gas/Technodata.csv
+++ /dev/null
@@ -1,4 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gassupply1,R1,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
-gassupply1,R2,2020,fixed,0,1,0,1,2.55,1,5,1,60,35,0.9,0.00000189,86,0.1,energy,gas,gas,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/power/CommIn.csv b/case-studies/hands-on-files/HO7/default_final/technodata/power/CommIn.csv
deleted file mode 100644
index d713c66..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/power/CommIn.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,0,1.67,0,0,0
-windturbine,R1,2020,fixed,0,0,0,0,1
-gasCCGT,R2,2020,fixed,0,1.67,0,0,0
-windturbine,R2,2020,fixed,0,0,0,0,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/power/CommOut.csv b/case-studies/hands-on-files/HO7/default_final/technodata/power/CommOut.csv
deleted file mode 100644
index 483255c..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/power/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasCCGT,R1,2020,fixed,1,0,0,91.67,0
-windturbine,R1,2020,fixed,1,0,0,0,0
-gasCCGT,R2,2020,fixed,1,0,0,91.67,0
-windturbine,R2,2020,fixed,1,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/power/ExistingCapacity.csv b/case-studies/hands-on-files/HO7/default_final/technodata/power/ExistingCapacity.csv
deleted file mode 100644
index 6934794..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/power/ExistingCapacity.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasCCGT,R1,PJ/y,1,1,0,0,0,0,0
-windturbine,R1,PJ/y,0,0,0,0,0,0,0
-gasCCGT,R2,PJ/y,1,1,0,0,0,0,0
-windturbine,R2,PJ/y,0,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/power/Technodata.csv b/case-studies/hands-on-files/HO7/default_final/technodata/power/Technodata.csv
deleted file mode 100644
index 52cb628..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/power/Technodata.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasCCGT,R1,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R1,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
-gasCCGT,R2,2020,fixed,23.78234399,1,0,1,0,1,2,1,60,35,0.9,0.00000189,86,0.1,energy,gas,electricity,1
-windturbine,R2,2020,fixed,36.30771182,1,0,1,0,1,2,1,60,25,0.4,0.00000189,86,0.1,energy,wind,electricity,1
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/preset/Residential2020Consumption.csv b/case-studies/hands-on-files/HO7/default_final/technodata/preset/Residential2020Consumption.csv
deleted file mode 100644
index ac9ba7c..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/preset/Residential2020Consumption.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,1,0,0
-1,R1,gasboiler,2,0,0,1.5,0,0
-2,R1,gasboiler,3,0,0,1,0,0
-3,R1,gasboiler,4,0,0,1.5,0,0
-4,R1,gasboiler,5,0,0,3,0,0
-5,R1,gasboiler,6,0,0,2,0,0
-6,R2,gasboiler,1,0,0,1,0,0
-7,R2,gasboiler,2,0,0,1.5,0,0
-8,R2,gasboiler,3,0,0,1,0,0
-9,R2,gasboiler,4,0,0,1.5,0,0
-10,R2,gasboiler,5,0,0,3,0,0
-11,R2,gasboiler,6,0,0,2,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/preset/Residential2050Consumption.csv b/case-studies/hands-on-files/HO7/default_final/technodata/preset/Residential2050Consumption.csv
deleted file mode 100644
index 90bb100..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/preset/Residential2050Consumption.csv
+++ /dev/null
@@ -1,13 +0,0 @@
-,RegionName,ProcessName,Timeslice,electricity,gas,heat,CO2f,wind
-0,R1,gasboiler,1,0,0,3,0,0
-1,R1,gasboiler,2,0,0,4.5,0,0
-2,R1,gasboiler,3,0,0,3,0,0
-3,R1,gasboiler,4,0,0,4.5,0,0
-4,R1,gasboiler,5,0,0,9,0,0
-5,R1,gasboiler,6,0,0,6,0,0
-6,R2,gasboiler,1,0,0,3,0,0
-7,R2,gasboiler,2,0,0,4.5,0,0
-8,R2,gasboiler,3,0,0,3,0,0
-9,R2,gasboiler,4,0,0,4.5,0,0
-10,R2,gasboiler,5,0,0,9,0,0
-11,R2,gasboiler,6,0,0,6,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/residential/CommIn.csv b/case-studies/hands-on-files/HO7/default_final/technodata/residential/CommIn.csv
deleted file mode 100644
index a2d6db4..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/residential/CommIn.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,1.16,0,0,0
-heatpump,R1,2020,fixed,0.4,0,0,0,0
-gasboiler,R2,2020,fixed,0,1.16,0,0,0
-heatpump,R2,2020,fixed,0.4,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/residential/CommOut.csv b/case-studies/hands-on-files/HO7/default_final/technodata/residential/CommOut.csv
deleted file mode 100644
index 8411c85..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/residential/CommOut.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,electricity,gas,heat,CO2f,wind
-Unit,-,Year,-,PJ/PJ,PJ/PJ,PJ/PJ,kt/PJ,PJ/PJ
-gasboiler,R1,2020,fixed,0,0,1,64.71,0
-heatpump,R1,2020,fixed,0,0,1,0,0
-gasboiler,R2,2020,fixed,0,0,1,64.71,0
-heatpump,R2,2020,fixed,0,0,1,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/residential/ExistingCapacity.csv b/case-studies/hands-on-files/HO7/default_final/technodata/residential/ExistingCapacity.csv
deleted file mode 100644
index 45fa693..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/residential/ExistingCapacity.csv
+++ /dev/null
@@ -1,5 +0,0 @@
-ProcessName,RegionName,Unit,2020,2025,2030,2035,2040,2045,2050
-gasboiler,R1,PJ/y,10,5,0,0,0,0,0
-heatpump,R1,PJ/y,0,0,0,0,0,0,0
-gasboiler,R2,PJ/y,10,5,0,0,0,0,0
-heatpump,R2,PJ/y,0,0,0,0,0,0,0
\ No newline at end of file
diff --git a/case-studies/hands-on-files/HO7/default_final/technodata/residential/Technodata.csv b/case-studies/hands-on-files/HO7/default_final/technodata/residential/Technodata.csv
deleted file mode 100644
index 12db766..0000000
--- a/case-studies/hands-on-files/HO7/default_final/technodata/residential/Technodata.csv
+++ /dev/null
@@ -1,6 +0,0 @@
-ProcessName,RegionName,Time,Level,cap_par,cap_exp,fix_par,fix_exp,var_par,var_exp,MaxCapacityAddition,MaxCapacityGrowth,TotalCapacityLimit,TechnicalLife,UtilizationFactor,ScalingSize,efficiency,InterestRate,Type,Fuel,EndUse,Agent2
-Unit,-,Year,-,MUS$2010/PJ_a,-,MUS$2010/PJ,-,MUS$2010/PJ,-,PJ,%,PJ,Years,-,PJ,%,-,-,-,-,Retrofit
-gasboiler,R1,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R1,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
-gasboiler,R2,2020,fixed,3.8,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,gas,heat,1
-heatpump,R2,2020,fixed,8.866667,1,0,1,0,1,10,0.02,60,10,1,0.00000189,86,0.1,energy,electricity,heat,1
\ No newline at end of file
diff --git a/docs/Results/MCACapacity.csv b/docs/Results/MCACapacity.csv
new file mode 100644
index 0000000..f797d42
--- /dev/null
+++ b/docs/Results/MCACapacity.csv
@@ -0,0 +1,57 @@
+agent,capacity,dst_region,installed,region,sector,technology,type,year
+A1,10.00000000000,R1,2020,R1,residential,gasboiler,newcapa,2020
+A1,1.00000000000,R1,2020,R1,power,gasCCGT,newcapa,2020
+A1,15.00000000000,R1,2020,R1,gas,gassupply1,newcapa,2020
+A1,5.00000000000,R1,2020,R1,residential,gasboiler,newcapa,2025
+A1,19.00000000000,R1,2020,R1,residential,heatpump,newcapa,2025
+A1,6.00000000000,R1,2020,R1,power,gasCCGT,newcapa,2025
+A1,5.00000000000,R1,2020,R1,power,windturbine,newcapa,2025
+A1,16.73530000000,R1,2020,R1,gas,gassupply1,newcapa,2025
+A1,19.00000000000,R1,2020,R1,residential,heatpump,newcapa,2030
+A1,10.00000000000,R1,2025,R1,residential,heatpump,newcapa,2030
+A1,5.00000000000,R1,2020,R1,power,gasCCGT,newcapa,2030
+A1,6.00000000000,R1,2025,R1,power,gasCCGT,newcapa,2030
+A1,5.00000000000,R1,2020,R1,power,windturbine,newcapa,2030
+A1,9.23530000000,R1,2020,R1,gas,gassupply1,newcapa,2030
+A1,9.28910000000,R1,2025,R1,gas,gassupply1,newcapa,2030
+A1,10.00000000000,R1,2025,R1,residential,heatpump,newcapa,2035
+A1,26.00000000000,R1,2030,R1,residential,heatpump,newcapa,2035
+A1,5.00000000000,R1,2020,R1,power,gasCCGT,newcapa,2035
+A1,6.00000000000,R1,2025,R1,power,gasCCGT,newcapa,2035
+A1,5.00000000000,R1,2020,R1,power,windturbine,newcapa,2035
+A1,6.00000000000,R1,2030,R1,power,windturbine,newcapa,2035
+A1,1.73530000000,R1,2020,R1,gas,gassupply1,newcapa,2035
+A1,9.28910000000,R1,2025,R1,gas,gassupply1,newcapa,2035
+A1,7.47410000000,R1,2030,R1,gas,gassupply1,newcapa,2035
+A1,26.00000000000,R1,2030,R1,residential,heatpump,newcapa,2040
+A1,16.00000000000,R1,2035,R1,residential,heatpump,newcapa,2040
+A1,5.00000000000,R1,2020,R1,power,gasCCGT,newcapa,2040
+A1,6.00000000000,R1,2025,R1,power,gasCCGT,newcapa,2040
+A1,5.00000000000,R1,2020,R1,power,windturbine,newcapa,2040
+A1,6.00000000000,R1,2030,R1,power,windturbine,newcapa,2040
+A1,6.00000000000,R1,2035,R1,power,windturbine,newcapa,2040
+A1,1.73530000000,R1,2020,R1,gas,gassupply1,newcapa,2040
+A1,9.28910000000,R1,2025,R1,gas,gassupply1,newcapa,2040
+A1,7.47410000000,R1,2030,R1,gas,gassupply1,newcapa,2040
+A1,16.00000000000,R1,2035,R1,residential,heatpump,newcapa,2045
+A1,32.00000000000,R1,2040,R1,residential,heatpump,newcapa,2045
+A1,5.00000000000,R1,2020,R1,power,gasCCGT,newcapa,2045
+A1,6.00000000000,R1,2025,R1,power,gasCCGT,newcapa,2045
+A1,5.00000000000,R1,2020,R1,power,windturbine,newcapa,2045
+A1,6.00000000000,R1,2030,R1,power,windturbine,newcapa,2045
+A1,6.00000000000,R1,2035,R1,power,windturbine,newcapa,2045
+A1,6.00000000000,R1,2040,R1,power,windturbine,newcapa,2045
+A1,1.73530000000,R1,2020,R1,gas,gassupply1,newcapa,2045
+A1,9.28910000000,R1,2025,R1,gas,gassupply1,newcapa,2045
+A1,7.47410000000,R1,2030,R1,gas,gassupply1,newcapa,2045
+A1,32.00000000000,R1,2040,R1,residential,heatpump,newcapa,2050
+A1,22.00000000000,R1,2045,R1,residential,heatpump,newcapa,2050
+A1,5.00000000000,R1,2020,R1,power,gasCCGT,newcapa,2050
+A1,6.00000000000,R1,2025,R1,power,gasCCGT,newcapa,2050
+A1,6.00000000000,R1,2030,R1,power,windturbine,newcapa,2050
+A1,6.00000000000,R1,2035,R1,power,windturbine,newcapa,2050
+A1,6.00000000000,R1,2040,R1,power,windturbine,newcapa,2050
+A1,11.00000000000,R1,2045,R1,power,windturbine,newcapa,2050
+A1,1.73530000000,R1,2020,R1,gas,gassupply1,newcapa,2050
+A1,9.28910000000,R1,2025,R1,gas,gassupply1,newcapa,2050
+A1,7.47410000000,R1,2030,R1,gas,gassupply1,newcapa,2050
diff --git a/docs/Results/MCAPrices.csv b/docs/Results/MCAPrices.csv
new file mode 100644
index 0000000..20946c7
--- /dev/null
+++ b/docs/Results/MCAPrices.csv
@@ -0,0 +1,169 @@
+commodity,day,hour,month,prices,region,timeslice,units_prices,year
+electricity,all-week,night,all-year,19.50000000000,R1,0,MUS$2010/PJ,2020
+gas,all-week,night,all-year,7.15280000000,R1,0,MUS$2010/PJ,2020
+heat,all-week,night,all-year,100.00000000000,R1,0,MUS$2010/PJ,2020
+CO2f,all-week,night,all-year,0.08310000000,R1,0,MUS$2010/kt,2020
+electricity,all-week,morning,all-year,19.50000000000,R1,1,MUS$2010/PJ,2020
+gas,all-week,morning,all-year,7.15280000000,R1,1,MUS$2010/PJ,2020
+heat,all-week,morning,all-year,100.00000000000,R1,1,MUS$2010/PJ,2020
+CO2f,all-week,morning,all-year,0.08310000000,R1,1,MUS$2010/kt,2020
+electricity,all-week,afternoon,all-year,19.50000000000,R1,2,MUS$2010/PJ,2020
+gas,all-week,afternoon,all-year,7.15280000000,R1,2,MUS$2010/PJ,2020
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diff --git a/docs/_config.yml b/docs/_config.yml
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deleted file mode 100644
index a0b47b8..0000000
--- a/docs/hands_on_01/bibliography.bib
+++ /dev/null
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-@book{Brooks2016,
-abstract = {Calls for accountability and impactful research are fundamentally reshaping the academy, giving rise to a large, critical scholarship on neoliberal regimes of accountability and their pernicious effects. But these calls also animate other institutional forms and practices that have received less critical attention. These include new forms of science that promise accountability through interdisciplinarity, collaborating with stakeholders, and addressing real-world problems. This article considers one example of such accountable science: human dimensions of climate change field research. This research endeavour has produced surprising results, including the uncritical adoption of controversial Euro-American ideas about traditional Others. In exploring how this has come about, the article considers how theoretical and disciplinary diversity are managed within this arena, and the organizing logics that enable climate sciences and scientists to work together. We ultimately argue that accountable science - like other neoliberal modes of accountability - can produce outcomes for which no one can be held to account.},
-author = {Brooks, Nick},
-booktitle = {Tyndall Centre for Climate Change Research},
-isbn = {0165-0009},
-issn = {14679655},
-title = {{Vulnerability ,risk and adaptation : A conceptual framework}},
-year = {2016}
-}
-
-@techreport{McCarthy2001,
-abstract = {TAR is being prepared to provide a synthesis of the findings of the three Working Groups and will focus on questions addressing particular policy issues raised ... SUMMARY FOR POLICYMAKERS CLIMATE CHANGE 2001 : IMPACTS , ADAPTATION , AND VULNERABILITY ...},
-author = {McCarthy, J J},
-booktitle = {contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change},
-title = {{Summary for Policymakers; Climate Change 2001: impacts, adaptation, and vulnerability: }},
-year = {2001}
-}
-
-@techreport{GPSS2019,
-author = {GPSS},
-institution = {World Bank},
-title = {{Fragility and Vulnerability Assessment Guide}},
-url = {https://gpss.worldbank.org/sites/gpss/files/2019-10/Fragility and Vulnerability Assessment Guide.pdf},
-year = {2019}
-}
-
-@techreport{Jones2003,
-author = {Jones, R and Boer, R},
-institution = {UNDP},
-title = {{Assessing current climate risks Adaptation Policy Framework: A Guide for Policies to Facilitate Adaptation to Climate Change, UNDP}},
-url = {https://www4.unfccc.int/sites/NAPC/Country Documents/General/apf technical paper04.pdf},
-year = {2003}
-}
-
-@incollection{Allen2003,
-author = {Allen, Katrina},
-booktitle = {Natural Disasters and Development in a Globalizing World},
-doi = {10.4324/9780203402375},
-isbn = {0203402375},
-title = {{Vulnerability reduction and the community-based approach: A Philippines study}},
-year = {2003}
-}
-
-@article{Hall2003,
-abstract = {Risk analysis provides a rational basis for flood management decision-making at a national scale, as well as regionally and locally. National-scale flood risk assessment can provide consistent information to support the development of flood management policy, allocation of resources and monitoring of the performance of flood mitigation activities. However, national-scale risk assessment presents particular challenges in terms of data acquisition and manipulation, numerical computation and presentation of results. A methodology that addresses these difficulties through appropriate approximations has been developed and applied in England and Wales. The methodology represents the processes of fluvial and coastal flooding over linear flood defence systems in sufficient detail to test alternative policy options for investment in flood management. Flood outlines and depths are generated, in the absence of a consistent national topographic and water level data set, using a rapid parametric inundation routine. Potential economic and social impacts of flooding are assessed using national databases of flood- plain properties and demography. A case study of the river Parrett catchment and adjoining sea defences in Bridgwater Bay in England demonstrates the application of the method and presentation of results in a geographical information system.},
-author = {Hall, Jim W. and Dawson, Richard J. and Sayers, Paul B. and Rosu, Corina and Chatterton, John B. and Deakin, Robert},
-doi = {10.1680/wame.2003.156.3.235},
-issn = {14724561},
-journal = {Proceedings of the Institution of Civil Engineers: Water and Maritime Engineering},
-keywords = {Floods and floodworks,Risk and probability analysis,River engineering,Sea defences},
-title = {{A methodology for national-scale flood risk assessment}},
-year = {2003}
-}
-
-@article{Hall2003,
-abstract = {Risk analysis provides a rational basis for flood management decision-making at a national scale, as well as regionally and locally. National-scale flood risk assessment can provide consistent information to support the development of flood management policy, allocation of resources and monitoring of the performance of flood mitigation activities. However, national-scale risk assessment presents particular challenges in terms of data acquisition and manipulation, numerical computation and presentation of results. A methodology that addresses these difficulties through appropriate approximations has been developed and applied in England and Wales. The methodology represents the processes of fluvial and coastal flooding over linear flood defence systems in sufficient detail to test alternative policy options for investment in flood management. Flood outlines and depths are generated, in the absence of a consistent national topographic and water level data set, using a rapid parametric inundation routine. Potential economic and social impacts of flooding are assessed using national databases of flood- plain properties and demography. A case study of the river Parrett catchment and adjoining sea defences in Bridgwater Bay in England demonstrates the application of the method and presentation of results in a geographical information system.},
-author = {Hall, Jim W. and Dawson, Richard J. and Sayers, Paul B. and Rosu, Corina and Chatterton, John B. and Deakin, Robert},
-doi = {10.1680/wame.2003.156.3.235},
-issn = {14724561},
-journal = {Proceedings of the Institution of Civil Engineers: Water and Maritime Engineering},
-keywords = {Floods and floodworks,Risk and probability analysis,River engineering,Sea defences},
-title = {{A methodology for national-scale flood risk assessment}},
-year = {2003}
-}
-
-@techreport{WorldBank2019,
-author = {{World Bank}},
-title = {{INCREASING INFRASTRUCTURE RESILIENCE BACKGROUND REPORT}},
-url = {http://documents1.worldbank.org/curated/en/620731560526509220/pdf/Technical-Annex.pdf},
-year = {2019}
-}
-
-@techreport{FEMA2013a,
-address = {Washington DC, USA},
-author = {FEMA},
-title = {{Multihazard Loss Estimation Methodology, Earthquake Model Technical Manual}},
-year = {2013}
-}
-
-@techreport{FEMA2013b,
-address = {Washington DC, USA},
-author = {FEMA},
-title = {{Multihazard Loss Estimation Methodology, Hurricane Model Technical Manual}},
-year = {2013}
-}
-
-@article{Lamb2019,
-abstract = {Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system-wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.},
-author = {Lamb, Rob and Garside, Paige and Pant, Raghav and Hall, Jim W.},
-doi = {10.1111/risa.13370},
-issn = {15396924},
-journal = {Risk Analysis},
-keywords = {Bridge,flood risk,infrastructure,rail network,scour},
-pmid = {31318475},
-title = {{A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods}},
-year = {2019}
-}
-
-@article{Towe2018,
-abstract = {Floods in England and Wales have the potential to cause billions of pounds of damage. You might think such extreme events are rare, but they are likely to occur more frequently than expected. By Ross Towe, Jonathan Tawn and Rob Lamb.},
-author = {Towe, Ross and Tawn, Jonathan and Lamb, Rob},
-doi = {10.1111/j.1740-9713.2018.01209.x},
-issn = {17409713},
-journal = {Significance},
-title = {{Why extreme floods are more common than you might think}},
-year = {2018}
-}
-
-@article{Lamb2010,
-abstract = {To date, national- and regional-scale flood risk assessments have provided valuable information about the annual expected consequences of flooding, but not the exposure to widespread concurrent flooding that could have damaging consequences for people and the economy. We present a new method for flood risk assessment that accommodates the risk of widespread flooding. It is based on a statistical conditional exceedance model, which is fitted to gauged data and describes the joint probability of extreme river flows or sea levels at multiple locations. The method can be applied together with data from models for flood defence systems and economic damages to calculate a risk profile describing the probability distribution of economic losses or other consequences aggregated over a region. The method has the potential to augment national or regional risk assessments of expected annual damage with new information about the likelihoods, extent and impacts of events that could contribute to the risk. {\textcopyright} The Authors. Journal of Flood Risk Management {\textcopyright} 2010 The Chartered Institution of Water and Environmental Management.},
-author = {Lamb, R. and Keef, C. and Tawn, J. and Laeger, S. and Meadowcroft, I. and Surendran, S. and Dunning, P. and Batstone, C.},
-doi = {10.1111/j.1753-318X.2010.01081.x},
-issn = {1753318X},
-journal = {Journal of Flood Risk Management},
-keywords = {Economic damages,Flood risk,Joint probability,Spatial dependence},
-title = {{A new method to assess the risk of local and widespread flooding on rivers and coasts}},
-year = {2010}
-}
-
-@article{Koks2019,
- title={Understanding business disruption and economic losses due to electricity failures and flooding},
- author={Koks, Elco and Pant, Raghav and Thacker, Scott and Hall, Jim W},
- journal={International Journal of Disaster Risk Science},
- volume={10},
- number={4},
- pages={421--438},
- year={2019},
- publisher={Springer}
-}
\ No newline at end of file
diff --git a/docs/hands_on_01/hands_on_1.md b/docs/hands_on_01/hands_on_1.md
deleted file mode 100644
index dad1088..0000000
--- a/docs/hands_on_01/hands_on_1.md
+++ /dev/null
@@ -1,195 +0,0 @@
----
-title: "Hands On Exercise 1: Installing MUSE"
-keywords:
-- MUSE
-- Python
-authors:
-- Alexander J. M. Kell
----
-
-## Short description
-
-This hands-on exercise will allow you to install MUSE on your computer. We will then take you though an example to run and visualise a default MUSE example.
-
-If at any point you get stuck with these hands-on exercises, feel free to post a question in the purpose-made MUSE google, group if your question hasn't already been answered there!:
-
-[https://groups.google.com/g/muse-model](https://groups.google.com/g/muse-model)
-
-For an in-depth look at the MUSE documentation have a look here:
-[http://muse-docs.readthedocs.io](http://muse-docs.readthedocs.io)
-
-## Learning objectives
-
-- Install MUSE
-- Run an example
-- Visualise the results of the example
-
-# Exercise content
-
-In all of the hands-on, there is an accompanying video linked at the start of the lectuer. Here we show you how to do the hands-on. This should make the process simpler to follow.
-
-Hands-on accompanying video:
-[https://youtu.be/Gppj1Gl-ajA](https://youtu.be/Gppj1Gl-ajA)
-
-### For Windows users only
-
-Windows users and developers may need to install `Windows Build Tools`. These tools include C/C++ compilers which are needed to build some python dependencies.
-
-MacOS includes compilers by default, hence no action is needed for Mac users.
-
-Linux users may need to install a C compiler, whether GNU gcc or Clang, as well python development packages, depending on their distribution.
-
-If you have MacOS or Linux you can skip this section and head to the next section below here.
-
-1. Install Visual Studio from the following link: [https://visualstudio.microsoft.com/vs/older-downloads/](https://visualstudio.microsoft.com/vs/older-downloads/). Please select the 2019 version. Click on download.
-
-2. Select the "Visual Studio Community" version. Click on "Download" and save the executable vs_Commmunity.exe.
-
-3. Install Visual Studio by selecting the default options. You may be asked to reboot your computer to complete the installation.
-
-4. Download the Microsoft Visual C++ Build Tools from the following link: [https://visualstudio.microsoft.com/vs/older-downloads/](https://visualstudio.microsoft.com/vs/older-downloads/).
-
-5. Please select the "Build Tools for Visual Studio 2019 (version 16.9)". Click on download. Save the vs_BuildTools.exe.
-
-6. Run the installer
-
-7. Install options: select only the “Windows 10 SDK” (assuming the computer is Windows 10)]. This will come up on the right-hand side of the screen.
-
-The installation screen should look similar to the following:
-
-{width=100%}
-
-**Figure 1.1:** Visual Studio Installer window
-
-
-## Installing MUSE
-
-MUSE is developed using python, an open-source programming language, which means that there are two steps to the installation process. First, python should be installed. Then so should MUSE.
-
-The simplest method to install python is by downloading the [Anaconda distribution](https://www.anaconda.com/products/individual). Make sure to choose the appropriate operating system (e.g. windows), python version 3.9, and the 64 bit installer. Once this has been done follow the steps for the anaconda installer, as prompted.
-
-Open the Anaconda prompt for Windows machines or terminal if on MacOS or Linux and create a new environment hosting python 3.8.
-
-Activate the new environment.
-
-```
-conda create --name muse python=3.8
-activate muse
-```
-
-After python is installed we can install MUSE. MUSE can be installed via the Anaconda Prompt or CMD.exe (or any terminal on Mac and Linux). This is a command-line interface to python and the python eco-system. In the anaconda prompt, run:
-
-```
-python -m pip install --user git+https://github.com/SGIModel/MUSE_OS
-```
-
-If you get an error such as the following:
-
-```
-Cannot find command 'git' - do you have 'git' installed and in your PATH
-```
-
-Then you have to install git in anaconda. This can be completed by running the following command:
-
-```
-conda install git pip
-```
-
-
-It should now be possible to run muse. Again, this can be done in the anaconda prompt as follows:
-
-```
-python -m muse --help
-```
-
-## Running your first example
-
-In this section we run an example simulation of MUSE, in the next section we will visualise the results.
-
-First we need to download the MUSE source code. To do that navigate to the MUSE GitHub repository:
-[https://github.com/SGIModel/MUSE_OS](https://github.com/SGIModel/MUSE_OS)
-
-Click on the green `Code` button in the top right-hand corner and then click on `Download ZIP`. Figure 2.1 shows how to do this, once you are on the relevant page.
-
-{width=100%}
-
-**Figure 2.1:** How to download MUSE
-
-Once you have downloaded the source code, unzip the folder and move it to a location that is convenient for you.
-
-We will place ours on the desktop for simplicity, but feel free to make a folder in your documents or otherwise.
-
-To run MUSE, we must open the anaconda prompt for Windows machines or terminal if on MacOS or Linux. Then we must navigate to the directory using the prompt or terminal to find the MUSE examples. Ours is in Desktop, so we will run the following command:
-
-```
-python -m muse -model --default
-```
-
-Alternatively, once we have navigated to the directory containing the example settings (settings.toml) we can run the simulation using the following command in the anaconda prompt or terminal:
-
-```
-python -m muse settings.toml
-```
-
-If running correctly, your prompt should output text similar to the following:
-```
--- 2020-11-03 15:58:29 - muse.sectors.register - INFO
-Sector legacy registered.
-
--- 2020-11-03 15:58:29 - muse.sectors.register - INFO
-Sector preset registered, with alias presets.
-
--- 2020-11-03 15:58:29 - muse.sectors.register - INFO
-Sector default registered.
-
--- 2020-11-03 15:58:29 - muse.readers.toml - INFO
-Reading MUSE settings
-
--- 2020-11-03 15:58:29 - muse.readers.toml - INFO
- Default input values used: carbon_budget_control.commodities, carbon_budget_control.method, carbon_budget_control.debug, carbon_budget_control.control_undershoot, carbon_budget_control.control_overshoot, carbon_budget_control.method_options
-```
-
-## Results
-
-If the default MUSE example has run successfully, you should now have a folder called `Results` in the same directory as `settings.toml`.
-
-This directory should contain results for each sector contained within this example (Gas, Power and Residential) as well as results for the entire simulation in the form of the files `MCACapacity.csv` and `MCAPrices.csv`.
-
-`MCACapacity.csv` contains information about the energy capacity each agent has per technology per benchmark year. Each benchmark year is the modelled year in the `settings.toml` file. In our example, this is 2020, 2025, …, 2050.
-
-`MCAPrices.csv` has the converged price of each commodity per benchmark year and timeslice. For example, it has the cost of electricity at night for electricity in 2020, and other similar results.
-
-Within each of the sector result folders, there is an output for capacity for each commodity in each year. Future years, which the simulation has not run to, refers to the technology capacity as it retires. Within the Residential folder there is also a folder for Supply within each year. This refers to how much end-use commodity was output.
-
-Some of these terms will not be familiar to you yet, but do not worry about this for now. This hands-on is just guiding you the basic process from installation to data visualisation and later hands-on material will give more information.
-
-## Visualisation
-
-There are many different ways to visualise the results of MUSE. For example, you could use a programming language such as python or R. In this course, however, we will use Excel for simplicity.
-
-There are also many different variables and combinations of data that we can plot. In this course we will primarily explore the capacity installed over the time horizon (2020 to 2050). Through this, we can see which of the technologies are invested in and understand the competition between technologies.
-
-To start the visualisation process of the default example, navigate to the folder where you run the `default` example in anaconda prompt or CMD.exe. For instance:
-
-```
-cd {MUSE_download_location}/StarMuse/run/example/default/
-```
-
-Go into the folder called `/Results/` and right click on the file called `MCACapacity.csv` and open it with Microsoft Excel. Once you've opened the file with Excel, we can begin the data visualisation process.
-
-First, select the PivotChart button under the insert menu. Ensure that the "Select a table or range" highlights all of the data, and the "Choose where you want the PivotChart to be placed" is selected to "Existing worksheet" like in the figure above. Then within the "Table/Range" box, click the cell where you would like the figure to be placed. Click "OK" when finished.
-
-You can choose to filter with "sector", add "technology" to columns, "year" to rows, and display "capacity" as sum.
-
-You can then display a barchart like the one in Figure 1.3, below:
-
-{width=50%}
-
-**Figure 1.3:** Insert -> Create PivotChart
-
-
-
-## Summary
-
-In this hands-on we have installed MUSE, learnt how to run a demo example and visualised the results of this demo example. In the next lectures and hands-on we will learn in detail the fundamentals of MUSE and how to edit the demo example for a case study of our choice.
-
diff --git a/docs/hands_on_02/Hands_On_2.md b/docs/hands_on_02/Hands_On_2.md
deleted file mode 100644
index 0aa917b..0000000
--- a/docs/hands_on_02/Hands_On_2.md
+++ /dev/null
@@ -1,168 +0,0 @@
----
-title: "Hands On Exercise 2: Modifying a service demand"
-keywords:
-- Preset sector
-- Demand by correlation
-authors:
-- Alexander J. M. Kell
----
-
-This hands-on will allow users to define their own service demand for an exogenous sector.
-
-
-# Learning objectives
-
-- Define own service demand for an exogenous sector
-
-# Adding an exogenous service demand
-
-Hands-on accompanying video:
-[https://youtu.be/btcWsSK5pnw](https://youtu.be/btcWsSK5pnw)
-
-As a quick example, in the residential sector a service demand could be cooking. Houses require energy to cook food and a technology to service this demand, such as an electric stove.
-
-We will start by looking at the `default` example. This can be found in your MUSE download at `/src/muse/data/example/default/example`, or you can download it at the following link:
-
-[https://zenodo.org/record/6340451#.YiiY5y-l1pQ](https://zenodo.org/record/6340451#.YiiY5y-l1pQ)
-
-The file you will need to start with is called `default.zip`, the finished version for your records is called `final_version.zip`
-
-Next, download this and place it in a convenient location on your computer. We will now start by adding a cooking demand to this example. The default example currently only has a service demand of `heat`, so we will need to do some editing.
-
-To achieve this, we will need to edit the `Residential2020Consumption.csv` and `Residential2050Consumption.csv`files found within the `technodata/preset/` directory. The `Residential2020Consumption.csv` file allows us to specify the demand in 2020 for each region and technology per timeslice. The `Residential2050Consumption.csv` file does the same but for the year 2050. The datapoints between these are interpolated. We will explain further details on interpolation in lecture 5.
-
-Firstly, we must add the new service demand `cook` as a column in these two files. Next, we add the demand. We can do this in Excel, or an editor of your choice. This is how it may look like for you when you open the `Residential2020Consumption.csv` file:
-
-{width=100%}
-
-**Figure 2.1:** Residential2020Consumption file opened in Excel.
-
-We will add a new column called `cook` and enter some values for each timeslice. This can be seen through the addition of a positive number in the `cook` column.
-
-{width=100%}
-
-**Figure 2.2:** Modified Residential2020Consumption file opened in Excel.
-
-The process is very similar for the `Residential2050Consumption.csv` file, however, for this example, we often placed larger numbers to indicate higher demand in 2050.
-
-Next, we must edit the files within the `input` folder. For this, we must add the cook service demand to each of these files.
-
-First, we will amend the `BaseYearExport.csv` and `BaseYearImport.csv` files. For this, we say that there is no import or export of the cook service demand. A brief example is outlined below for `BaseYearExport.csv`:
-
-{width=100%}
-
-**Figure 2.3:** Modified BaseYearImport file opened in Excel.
-
-The same is true for the `BaseYearImport.csv` file:
-
-{width=100%}
-
-**Figure 2.4:** Modified BaseYearExport file opened in Excel.
-
-Next, we must edit the `GlobalCommodities.csv` file. This is where we define the new commodity `cook`. It tells MUSE the commodity type, name, emissions factor of CO2 and heat rate, amongst other things.
-
-The default version used for this tutorial is below:
-
-{width=100%}
-
-**Figure 2.5:** Non-edited GlobalCommodities file opened in Excel.
-
-We then add a new row at the bottom to include the cook commodity:
-
-{width=100%}
-
-**Figure 2.6:** Edited GlobalCommodities file opened in Excel.
-
-The `CommodityName` column must be consistent internally within the model. Whereas the `Commodity` column is for your reference.
-
-Finally, the `Projections.csv` file must be changed. This is a large file which details the expected future costs of the technology in the first benchmark year of the simulation, the subsequent and actual simulated costs will be calculated during the running of the model. We have highlighted in **bold** the changed column for this example.
-
-{width=100%}
-
-**Figure 2.7:** Edited Projections file opened in Excel.
-
-## Addition of a cooking technology
-
-Next, we must add a technology to service this new demand. During this process we must be careful to specify the end-use of the technology as `cook`, which is case-sensitive.
-
-For this example, we will add two competing technologies to the residential sector to service the cooking demand: `electric_stove` and `gas_stove` to the `Technodata.csv` file in `/technodata/residential/Technodata.csv`.
-
-For this, we copy the `gasboiler` row for `R1` and paste it for the new `electric_stove`. For `gas_stove` we copy and paste the data for `heatpump` from region R1. In the figure below we show this, but only show the first few columns for the interest of space. We will also relax the growth constraints to ensure that the growth in technologies can meet demand.
-
-The growth constraints are:
-
-- MaxCapacityAddition: The maximum absolute capacity that the technology can grow in a single year.
-- MaxCapacityGrowth: The maximum percentage that the technology can grow in a particular year.
-- TotalCapacityLimit: The total absolute number that cannot be exceeded for a particular technology.
-
-Due to space constraints we can't show all the values as the technodata file is very wide, but we can set the parameters to be the following for both technologies:
-
-- MaxCapacityAddition: 100 PJ
-- MaxCapacityGrowth: 20 PJ
-- TotalCapacityLimit: 120 PJ
-
-
-{width=100%}
-
-**Figure 2.8:** Edited technodata file opened in Excel.
-
-As can be seen we have added two technologies with different cap_par costs to each other. We specified their respective fuels, and the enduse for both is cook.
-
-We must also add the data for these new technologies to the following files:
-
-- CommIn.csv
-- CommOut.csv
-- ExistingCapacity.csv
-
-The `CommIn.csv` file details the input commodities for each technology. In this case, the inputs are gas and electricity. The `CommOut` file details the outputs of the technology, which will be the `cook` commodity.
-
-We must add the input to each of the technologies (gas and electricity for `electric_stove` and `gas_stove` respectively), outputs of `cook` for both and the existing capacity for each technology.
-
-{width=100%}
-
-**Figure 2.9:** Edited CommIn file opened in Excel.
-
-Notice in Figure 2.9 that we had to add a column for the new `cook`. We must also do the same for the CommOut file, below:
-
-{width=100%}
-
-**Figure 2.10:** Edited CommOut file opened in Excel.
-
-We must do this for the `gas` and `power` sector as well. This is just for consistency within MUSE.
-
-Next, we must edit the `residsential/ExistingCapacity.csv` file to detail how much existing capacity there is in the base year and beyond. The existing capacity details power plants, or other technologies, which are already installed in the real world and therefore not invested in by the model. It is important to have a clear idea about the real world system in the base year before we run MUSE.
-
-{width=100%}
-
-**Figure 2.11:** Edited ExistingCapacity file opened in Excel.
-
-Due to the additional demand for gas and electricity brought on by the new cook demand, it is necessary to relax the growth constraints for `gassupply1` in the `technodata/gas/technodata.csv` file. For this example, we set this file as follows (see bold cells):
-
-{width=100%}
-
-**Figure 2.12:** Edited gas/technodata file opened in Excel.
-
-We must also ensure there are no `0` in the `ExistingCapacity.csv` for any of the sectors. This is because the MUSE model will produce an error. For error debugging it is helpful to go to the [MUSE google groups](https://groups.google.com/g/muse-model). So to do this, go through the `gas/ExistingCapacity.csv` and `power/ExistingCapacity.csv` and replace them with a non-zero value, such as `0.01`. Below is an example for the `gas` sector:
-
-{width=100%}
-
-**Figure 2.13:** Edited gas/ExistingCapacity.csv file opened in Excel.
-
-Next, we must run the simulation with our modified input files using the following command in the directory where you saved the default example. To do this follow the instructions shown in hands-on 1:
-
-```
-python -m pip muse settings.toml
-```
-
-The figure below shows the results for this new demand in the residential sector:
-
-{width=100%}
-
-**Figure 2.14:** Capacity results for the residential sector.
-
-We can see that `electric_stove` takes over completely. This is because of the lower `cap_par` value when compared to `gas_stove`. Do not be surprised if your results differ from this, as the MUSE model will change over time. The important thing is to understand the outputs from the inputs.
-
-For the final example input data (`final_version.zip`) showed in this tutorial and results spreadsheet, please refer to the link below:
-
-[https://zenodo.org/record/6340451#.YiiY5y-l1pQ](https://zenodo.org/record/6340451#.YiiY5y-l1pQ)
-
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@@ -1,129 +0,0 @@
-@book{Brooks2016,
-abstract = {Calls for accountability and impactful research are fundamentally reshaping the academy, giving rise to a large, critical scholarship on neoliberal regimes of accountability and their pernicious effects. But these calls also animate other institutional forms and practices that have received less critical attention. These include new forms of science that promise accountability through interdisciplinarity, collaborating with stakeholders, and addressing real-world problems. This article considers one example of such accountable science: human dimensions of climate change field research. This research endeavour has produced surprising results, including the uncritical adoption of controversial Euro-American ideas about traditional Others. In exploring how this has come about, the article considers how theoretical and disciplinary diversity are managed within this arena, and the organizing logics that enable climate sciences and scientists to work together. We ultimately argue that accountable science - like other neoliberal modes of accountability - can produce outcomes for which no one can be held to account.},
-author = {Brooks, Nick},
-booktitle = {Tyndall Centre for Climate Change Research},
-isbn = {0165-0009},
-issn = {14679655},
-title = {{Vulnerability ,risk and adaptation : A conceptual framework}},
-year = {2016}
-}
-
-@techreport{McCarthy2001,
-abstract = {TAR is being prepared to provide a synthesis of the findings of the three Working Groups and will focus on questions addressing particular policy issues raised ... SUMMARY FOR POLICYMAKERS CLIMATE CHANGE 2001 : IMPACTS , ADAPTATION , AND VULNERABILITY ...},
-author = {McCarthy, J J},
-booktitle = {contribution of Working Group II to the third assessment report of the Intergovernmental Panel on Climate Change},
-title = {{Summary for Policymakers; Climate Change 2001: impacts, adaptation, and vulnerability: }},
-year = {2001}
-}
-
-@techreport{GPSS2019,
-author = {GPSS},
-institution = {World Bank},
-title = {{Fragility and Vulnerability Assessment Guide}},
-url = {https://gpss.worldbank.org/sites/gpss/files/2019-10/Fragility and Vulnerability Assessment Guide.pdf},
-year = {2019}
-}
-
-@techreport{Jones2003,
-author = {Jones, R and Boer, R},
-institution = {UNDP},
-title = {{Assessing current climate risks Adaptation Policy Framework: A Guide for Policies to Facilitate Adaptation to Climate Change, UNDP}},
-url = {https://www4.unfccc.int/sites/NAPC/Country Documents/General/apf technical paper04.pdf},
-year = {2003}
-}
-
-@incollection{Allen2003,
-author = {Allen, Katrina},
-booktitle = {Natural Disasters and Development in a Globalizing World},
-doi = {10.4324/9780203402375},
-isbn = {0203402375},
-title = {{Vulnerability reduction and the community-based approach: A Philippines study}},
-year = {2003}
-}
-
-@article{Hall2003,
-abstract = {Risk analysis provides a rational basis for flood management decision-making at a national scale, as well as regionally and locally. National-scale flood risk assessment can provide consistent information to support the development of flood management policy, allocation of resources and monitoring of the performance of flood mitigation activities. However, national-scale risk assessment presents particular challenges in terms of data acquisition and manipulation, numerical computation and presentation of results. A methodology that addresses these difficulties through appropriate approximations has been developed and applied in England and Wales. The methodology represents the processes of fluvial and coastal flooding over linear flood defence systems in sufficient detail to test alternative policy options for investment in flood management. Flood outlines and depths are generated, in the absence of a consistent national topographic and water level data set, using a rapid parametric inundation routine. Potential economic and social impacts of flooding are assessed using national databases of flood- plain properties and demography. A case study of the river Parrett catchment and adjoining sea defences in Bridgwater Bay in England demonstrates the application of the method and presentation of results in a geographical information system.},
-author = {Hall, Jim W. and Dawson, Richard J. and Sayers, Paul B. and Rosu, Corina and Chatterton, John B. and Deakin, Robert},
-doi = {10.1680/wame.2003.156.3.235},
-issn = {14724561},
-journal = {Proceedings of the Institution of Civil Engineers: Water and Maritime Engineering},
-keywords = {Floods and floodworks,Risk and probability analysis,River engineering,Sea defences},
-title = {{A methodology for national-scale flood risk assessment}},
-year = {2003}
-}
-
-@article{Hall2003,
-abstract = {Risk analysis provides a rational basis for flood management decision-making at a national scale, as well as regionally and locally. National-scale flood risk assessment can provide consistent information to support the development of flood management policy, allocation of resources and monitoring of the performance of flood mitigation activities. However, national-scale risk assessment presents particular challenges in terms of data acquisition and manipulation, numerical computation and presentation of results. A methodology that addresses these difficulties through appropriate approximations has been developed and applied in England and Wales. The methodology represents the processes of fluvial and coastal flooding over linear flood defence systems in sufficient detail to test alternative policy options for investment in flood management. Flood outlines and depths are generated, in the absence of a consistent national topographic and water level data set, using a rapid parametric inundation routine. Potential economic and social impacts of flooding are assessed using national databases of flood- plain properties and demography. A case study of the river Parrett catchment and adjoining sea defences in Bridgwater Bay in England demonstrates the application of the method and presentation of results in a geographical information system.},
-author = {Hall, Jim W. and Dawson, Richard J. and Sayers, Paul B. and Rosu, Corina and Chatterton, John B. and Deakin, Robert},
-doi = {10.1680/wame.2003.156.3.235},
-issn = {14724561},
-journal = {Proceedings of the Institution of Civil Engineers: Water and Maritime Engineering},
-keywords = {Floods and floodworks,Risk and probability analysis,River engineering,Sea defences},
-title = {{A methodology for national-scale flood risk assessment}},
-year = {2003}
-}
-
-@techreport{WorldBank2019,
-author = {{World Bank}},
-title = {{INCREASING INFRASTRUCTURE RESILIENCE BACKGROUND REPORT}},
-url = {http://documents1.worldbank.org/curated/en/620731560526509220/pdf/Technical-Annex.pdf},
-year = {2019}
-}
-
-@techreport{FEMA2013a,
-address = {Washington DC, USA},
-author = {FEMA},
-title = {{Multihazard Loss Estimation Methodology, Earthquake Model Technical Manual}},
-year = {2013}
-}
-
-@techreport{FEMA2013b,
-address = {Washington DC, USA},
-author = {FEMA},
-title = {{Multihazard Loss Estimation Methodology, Hurricane Model Technical Manual}},
-year = {2013}
-}
-
-@article{Lamb2019,
-abstract = {Scour (localized erosion by water) is an important risk to bridges, and hence many infrastructure networks, around the world. In Britain, scour has caused the failure of railway bridges crossing rivers in more than 50 flood events. These events have been investigated in detail, providing a data set with which we develop and test a model to quantify scour risk. The risk analysis is formulated in terms of a generic, transferrable infrastructure network risk model. For some bridge failures, the severity of the causative flood was recorded or can be reconstructed. These data are combined with the background failure rate, and records of bridges that have not failed, to construct fragility curves that quantify the failure probability conditional on the severity of a flood event. The fragility curves generated are to some extent sensitive to the way in which these data are incorporated into the statistical analysis. The new fragility analysis is tested using flood events simulated from a spatial joint probability model for extreme river flows for all river gauging sites in Britain. The combined models appear robust in comparison with historical observations of the expected number of bridge failures in a flood event. The analysis is used to estimate the probability of single or multiple bridge failures in Britain's rail network. Combined with a model for passenger journey disruption in the event of bridge failure, we calculate a system-wide estimate for the risk of scour failures in terms of passenger journey disruptions and associated economic costs.},
-author = {Lamb, Rob and Garside, Paige and Pant, Raghav and Hall, Jim W.},
-doi = {10.1111/risa.13370},
-issn = {15396924},
-journal = {Risk Analysis},
-keywords = {Bridge,flood risk,infrastructure,rail network,scour},
-pmid = {31318475},
-title = {{A Probabilistic Model of the Economic Risk to Britain's Railway Network from Bridge Scour During Floods}},
-year = {2019}
-}
-
-@article{Towe2018,
-abstract = {Floods in England and Wales have the potential to cause billions of pounds of damage. You might think such extreme events are rare, but they are likely to occur more frequently than expected. By Ross Towe, Jonathan Tawn and Rob Lamb.},
-author = {Towe, Ross and Tawn, Jonathan and Lamb, Rob},
-doi = {10.1111/j.1740-9713.2018.01209.x},
-issn = {17409713},
-journal = {Significance},
-title = {{Why extreme floods are more common than you might think}},
-year = {2018}
-}
-
-@article{Lamb2010,
-abstract = {To date, national- and regional-scale flood risk assessments have provided valuable information about the annual expected consequences of flooding, but not the exposure to widespread concurrent flooding that could have damaging consequences for people and the economy. We present a new method for flood risk assessment that accommodates the risk of widespread flooding. It is based on a statistical conditional exceedance model, which is fitted to gauged data and describes the joint probability of extreme river flows or sea levels at multiple locations. The method can be applied together with data from models for flood defence systems and economic damages to calculate a risk profile describing the probability distribution of economic losses or other consequences aggregated over a region. The method has the potential to augment national or regional risk assessments of expected annual damage with new information about the likelihoods, extent and impacts of events that could contribute to the risk. {\textcopyright} The Authors. Journal of Flood Risk Management {\textcopyright} 2010 The Chartered Institution of Water and Environmental Management.},
-author = {Lamb, R. and Keef, C. and Tawn, J. and Laeger, S. and Meadowcroft, I. and Surendran, S. and Dunning, P. and Batstone, C.},
-doi = {10.1111/j.1753-318X.2010.01081.x},
-issn = {1753318X},
-journal = {Journal of Flood Risk Management},
-keywords = {Economic damages,Flood risk,Joint probability,Spatial dependence},
-title = {{A new method to assess the risk of local and widespread flooding on rivers and coasts}},
-year = {2010}
-}
-
-@article{Koks2019,
- title={Understanding business disruption and economic losses due to electricity failures and flooding},
- author={Koks, Elco and Pant, Raghav and Thacker, Scott and Hall, Jim W},
- journal={International Journal of Disaster Risk Science},
- volume={10},
- number={4},
- pages={421--438},
- year={2019},
- publisher={Springer}
-}
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-@book{EuropeanCommissionExpertGrouponFAIRData2018,
-abstract = {In addressing the remit assigned, the FAIR Data Expert Group chose to take a holistic and systemic approach to describe the broad range of changes required to “turn FAIR data into reality”. The notions of findability, accessibility, interoperability and reusability - and the actions needed to enable them - are so deeply intertwined that it does not make sense to address them individually. Instead, this report focuses on actions needed in terms of research culture and technology to ensure data, code and other research outputs are made FAIR. Research culture and technology are two sides of one whole. Coordinated, simultaneous interventions are needed in each to enable FAIR in this broad sense. The implementation of FAIR will be supported through the European Open Science Cloud (EOSC). The federation of data infrastructure and application of standards will enable the discovery and interoperability of data. Member States should support this movement by aligning their policies and investments in relation to FAIR data and Open Science. In a wider global context, parallel initiatives such as the NIH Data Commons, the Australian Research Data Commons and also the proposed African Open Science Platform are important for the implementation of FAIR. Developments in the EOSC should align with these international movements and ensure that data are FAIR across disciplines and geographic boundaries beyond Europe. The central sections of this Report focus on existing practice in certain fields to ascertain what can be learned from those research areas that have already developed standards, international agreements and infrastructure to enable FAIR. These examples have helped to define models for FAIR Digital Objects and the essential components of a FAIR ecosystem. Naturally the main building blocks in the ecosystem are technology-based services. However, the social aspects that drive the system and enable culture change – namely skills, metrics, incentives and sustainable investment – are also addressed. The report makes a number of detailed recommendations and specifies actions for different stakeholder groups to enable the changes required. Implementing FAIR is a significant undertaking and requires changes in terms of research culture and infrastructure provision. These changes are important in the context of the European Open Science Cloud and the direction for European Commission and Member State policy, but go beyond that: FAIR requires global agreements to ensure the broadest interoperability and reusability of data - beyond disciplinary and geographic boundaries. Twenty-seven recommendations are made, which are grouped into ‘Priority' and ‘Supporting' Recommendations. The fifteen priority recommendations should be considered the initial set of changes or steps to take in order to implement FAIR. The Supporting Recommendations may be considered as following on from the Priority Recommendations, adding specifics or further detail for implementation. Each individual Recommendation is followed by a set of Actions. Each Recommendation and each Action is numbered for unambiguous referencing. The full set of Recommendations and Actions are presented in the FAIR Action Plan at the end of this report.},
-author = {{European Commission Expert Group on FAIR Data}},
-booktitle = {Final Report and Action Plan from the European Commission Expert Group on FAIR Data},
-doi = {10.2777/1524},
-file = {:Users/wusher/Documents/Mendeley Desktop/European Commission Expert Group on FAIR Data - 2018 - Turning FAIR into reality.pdf:pdf},
-isbn = {9789279965470},
-keywords = {DMP (Data Management Plan),EOSC (European Open Science Cloud),European politics,FAIR principle (Findable Accessible Interoperable,PGD (Plan de Gestion des Donn{\'{e}}es),Standardised data,identifiants p{\'{e}}rennes,m{\'{e}}tadonn{\'{e}}es,politique europ{\'{e}}enne},
-pages = {78},
-title = {{Turning FAIR into reality}},
-year = {2018}
-}
-@article{Ramos2020,
-abstract = {蚯蚓仿生机器人},
-author = {Ramos, Eunice Pereira and Howells, Mark and Sridharan, Vignesh and Engstr{\"{o}}m, Rebecka Ericsdotter and Taliotis, Constantinos and Mentis, Dimitris and Gardumi, Francesco and de Strasser, Lucia and Pappis, Ioannis and {Pe{\~{n}}a Balderrama}, Gabriela and Almulla, Youssef and Beltramo, Agnese and {Ramirez Gomez}, Camilo and Sundin, Caroline and Alfstad, Thomas and Lipponen, Annukka and Zepeda, Eduardo and Niet, Taco and Quir{\'{o}}s-Tort{\'{o}}s, Jairo and Angulo-Paniagua, Jam and Shivakumar, Abhishek and Ulloa, Silvia and Rogner, Holger},
-doi = {10.1088/1748-9326/abd34f},
-file = {:Users/wusher/Documents/Mendeley Desktop/Ramos et al. - 2020 - The Climate, Land, Energy, and Water systems (CLEWs) framework a retrospective of activities and advances to 2019.pdf:pdf},
-issn = {1748-9326},
-journal = {Environmental Research Letters},
-month = {dec},
-pages = {0--31},
-title = {{The Climate, Land, Energy, and Water systems (CLEWs) framework: a retrospective of activities and advances to 2019}},
-url = {https://iopscience.iop.org/article/10.1088/1748-9326/abd34f},
-volume = {2},
-year = {2020}
-}
-@article{Flyvbjerg2021,
-abstract = {Scale-up is the process of growing a venture in size. The paper identifies modularity and speed as keys to successful scale-up. On that basis four types of scale-up are identified: Smart, dumb, forced, and fumbled. Smart scale-up combines modularity and speed. Dumb scale-up is bespoke and slow, and very common. The paper presents examples of each type of scale-up, explaining why they were successful or not. Whether you are a small startup or Elon Musk trying to grow Tesla and SpaceX or Jeff Bezos scaling up Amazon – or you are the US, UK, Chinese, or other government trying to increase power production, expand your infrastructure, or make your health, education, and social services work better – modularity and speed are the answer to effective delivery, or so the paper argues. How well you deal with modularity and speed decides whether your efforts succeed or fail. Most ventures, existing or planned, are neither fully smart nor fully dumb, but have elements of both. Successful organizations work to tip the balance towards smart by (a) introducing elements of smart scale-up into existing ventures and (b) starting new, fully smart-scaled ventures, to make themselves less dumb and ever smarter},
-author = {Flyvbjerg, By Bent},
-file = {:Users/wusher/Documents/Mendeley Desktop/Flyvbjerg - 2021 - Four Ways to Scale Up Smart , Dumb , Forced , and Fumbled.pdf:pdf},
-number = {January},
-pages = {1--37},
-title = {{Four Ways to Scale Up : Smart , Dumb , Forced , and Fumbled}},
-year = {2021}
-}
-
-@article{Hallegatte2012,
-abstract = {The impact of climate change on economic losses from tropical cyclones is a major concern. New research shows that — like changes in population and assets — climate change may double global losses from hurricanes.},
-author = {Hallegatte, St{\'{e}}phane},
-doi = {10.1038/nclimate1427},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {3},
-pages = {148--149},
-publisher = {Nature Publishing Group},
-title = {{Economics: The rising costs of hurricanes}},
-url = {http://dx.doi.org/10.1038/nclimate1427},
-volume = {2},
-year = {2012}
-}
-
-@article{Vitousek2017,
-author = {Vitousek, S and Barnard, P L and Fletcher, C H and Frazer, N and Erikson, L and Storlazzi, C D},
-journal = {Scientific Reports},
-number = {1399},
-pages = {1--9},
-title = {{Doubling of coastal flooding frequency within decades due to sea-level rise}},
-volume = {7},
-year = {2017}
-}
-
-@article{Elsner2008,
-abstract = {Atlantic tropical cyclones are getting stronger on average, with a 30-year trend that has been related to an increase in ocean temperatures over the Atlantic Ocean and elsewhere. Over the rest of the tropics, however, possible trends in tropical cyclone intensity are less obvious, owing to the unreliability and incompleteness of the observational record and to a restricted focus, in previous trend analyses, on changes in average intensity. Here we overcome these two limitations by examining trends in the upper quantiles of per-cyclone maximum wind speeds (that is, the maximum intensities that cyclones achieve during their lifetimes), estimated from homogeneous data derived from an archive of satellite records. We find significant upward trends for wind speed quantiles above the 70th percentile, with trends as high as 0.3 +/- 0.09 m s(-1) yr(-1) (s.e.) for the strongest cyclones. We note separate upward trends in the estimated lifetime-maximum wind speeds of the very strongest tropical cyclones (99th percentile) over each ocean basin, with the largest increase at this quantile occurring over the North Atlantic, although not all basins show statistically significant increases. Our results are qualitatively consistent with the hypothesis that as the seas warm, the ocean has more energy to convert to tropical cyclone wind.},
-author = {Elsner, James B and Kossin, James P and Jagger, Thomas H},
-doi = {10.1038/nature07234},
-isbn = {1476-4687 (Electronic){\$}\backslash{\$}n0028-0836 (Linking)},
-issn = {14764687},
-journal = {Nature},
-number = {7209},
-pages = {92--95},
-pmid = {18769438},
-title = {{The increasing intensity of the strongest tropical cyclones}},
-volume = {455},
-year = {2008}
-}
-
-
-@article{Bloschl2019,
-abstract = {Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.},
-author = {Bl{\"{o}}schl, G{\"{u}}nter and Hall, Julia and Viglione, Alberto and Perdig{\~{a}}o, Rui A.P. and Parajka, Juraj and Merz, Bruno and Lun, David and Arheimer, Berit and Aronica, Giuseppe T. and Bilibashi, Ardian and Boh{\'{a}}{\v{c}}, Miloň and Bonacci, Ognjen and Borga, Marco and {\v{C}}anjevac, Ivan and Castellarin, Attilio and Chirico, Giovanni B. and Claps, Pierluigi and Frolova, Natalia and Ganora, Daniele and Gorbachova, Liudmyla and G{\"{u}}l, Ali and Hannaford, Jamie and Harrigan, Shaun and Kireeva, Maria and Kiss, Andrea and Kjeldsen, Thomas R. and Kohnov{\'{a}}, Silvia and Koskela, Jarkko J. and Ledvinka, Ondrej and Macdonald, Neil and Mavrova-Guirguinova, Maria and Mediero, Luis and Merz, Ralf and Molnar, Peter and Montanari, Alberto and Murphy, Conor and Osuch, Marzena and Ovcharuk, Valeryia and Radevski, Ivan and Salinas, Jos{\'{e}} L. and Sauquet, Eric and {\v{S}}raj, Mojca and Szolgay, Jan and Volpi, Elena and Wilson, Donna and Zaimi, Klodian and {\v{Z}}ivkovi{\'{c}}, Nenad},
-doi = {10.1038/s41586-019-1495-6},
-issn = {14764687},
-journal = {Nature},
-number = {7772},
-pages = {108--111},
-pmid = {31462777},
-publisher = {Springer US},
-title = {{Changing climate both increases and decreases European river floods}},
-url = {http://dx.doi.org/10.1038/s41586-019-1495-6},
-volume = {573},
-year = {2019}
-}
-@article{Lehmann2019,
-abstract = {Deforestation of steep slopes may temporarily reduce evapotranspiration and lessen root reinforcement thus potentially enhancing landslide susceptibility. Quantifying the effects of deforestation and associated perturbations on landslide characteristics remains a challenge, especially for predictions in remote areas with limited information. We applied the STEP-TRAMM model that uses publicly available climatic and landscape information to assess effects of forest alteration on hydro-mechanical processes. The model considers two types of forest alterations: (i) removal of root reinforcement following permanent forest conversion, and (ii) time dependent root decay and regrowth following clear-cut timber harvesting. The model was applied to four study areas in different climatic regions (New Zealand, Oregon, Sumatra and Cambodia). We compared model predictions of landslide metrics with satellite-imaging of landslides following deforestation. Although we observe a higher propensity and larger landslides in deforested areas, effects were sensitive to deforestation practices and patterns. The largest increase in landslide area was associated with large and interconnected deforested tracts within a few years after deforestation as determined by competition between root decay and forest regrowth. For patchy small-scale forest conversion, the landslide areas were smaller but could occur many years after deforestation ({\textgreater} 10 years). The modeling framework offers ability to evaluate forest alteration scenarios through their potential impact on landslide hazard in specific regions of the landscape.},
-author = {Lehmann, Peter and von Ruette, Jonas and Or, Dani},
-doi = {10.1029/2019WR025233},
-issn = {19447973},
-journal = {Water Resources Research},
-keywords = {Landslide,Modeling,Rainfall,Remote Sensing},
-number = {11},
-pages = {9962--9976},
-title = {{Deforestation Effects on Rainfall-Induced Shallow Landslides: Remote Sensing and Physically-Based Modelling}},
-volume = {55},
-year = {2019}
-}
-@article{Du2015,
-abstract = {To date, limited attention has been paid to the role of impervious surface (IS) location in influencing flood processes. However, this topic is of tremendous significance for developing guidelines for urban planning and flood management. This study uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to investigate the impact of land-use change on flood processes and proposes a new index to quantify the impact of IS location on basin peak discharge. The results indicate that rapid urban expansion in the Longhua Basin, China, has increased peak discharge and flood volume by 140 and 162 {\%} over the past 30 years, respectively. The new index, named the Impervious Surface Impact Index, describes the spatially varying effects of IS increase in individual sub-basins on a basin's peak discharge. For the Longhua Basin, the index varies from 0.43 in downstream sub-basins to 5.91 in upstream sub-basins. An increase in upstream IS increases peak discharge nearly 14 times more than the same increase in downstream IS. Accordingly, the location of newly created IS can influence flood processes significantly. These findings can help to find suitable locations for urban development while mitigating the impact of land development on flood risks.},
-author = {Du, Shiqiang and Shi, Peijun and {Van Rompaey}, Anton and Wen, Jiahong},
-doi = {10.1007/s11069-014-1463-2},
-issn = {15730840},
-journal = {Natural Hazards},
-keywords = {Flood mitigation,Flood risk,Impervious surface,Low-impact development,Peak flow},
-number = {3},
-pages = {1457--1471},
-title = {{Quantifying the impact of impervious surface location on flood peak discharge in urban areas}},
-volume = {76},
-year = {2015}
-}
-@article{DiBaldassarre2018,
-abstract = {The expansion of reservoirs to cope with droughts and water shortages is hotly debated in many places around the world. We argue that there are two counterintuitive dynamics that should be considered in this debate: supply–demand cycles and reservoir effects. Supply–demand cycles describe instances where increasing water supply enables higher water demand, which can quickly offset the initial benefits of reservoirs. Reservoir effects refer to cases where over-reliance on reservoirs increases vulnerability, and therefore increases the potential damage caused by droughts. Here we illustrate these counterintuitive dynamics with global and local examples, and discuss policy and research implications.},
-author = {{Di Baldassarre}, Giuliano and Wanders, Niko and AghaKouchak, Amir and Kuil, Linda and Rangecroft, Sally and Veldkamp, Ted I.E. and Garcia, Margaret and van Oel, Pieter R. and Breinl, Korbinian and {Van Loon}, Anne F.},
-doi = {10.1038/s41893-018-0159-0},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {11},
-pages = {617--622},
-publisher = {Springer US},
-title = {{Water shortages worsened by reservoir effects}},
-url = {http://dx.doi.org/10.1038/s41893-018-0159-0},
-volume = {1},
-year = {2018}
-}
-@article{Tanoue2016,
-abstract = {The impacts of flooding are expected to rise due to population$\backslash$nincreases, economic growth and climate change. Hence, understanding the$\backslash$nphysical and spatiotemporal characteristics of risk drivers (hazard,$\backslash$nexposure and vulnerability) is required to develop effective flood$\backslash$nmitigation measures. Here, the long-term trend in flood vulnerability$\backslash$nwas analysed globally, calculated from the ratio of the reported flood$\backslash$nloss or damage to the modelled flood exposure using a global river and$\backslash$ninundation model. A previous study showed decreasing global flood$\backslash$nvulnerability over a shorter period using different disaster data. The$\backslash$nlong-term analysis demonstrated for the first time that flood$\backslash$nvulnerability to economic losses in upper-middle, lower-middle and$\backslash$nlow-income countries shows an inverted U-shape, as a result of the$\backslash$nbalance between economic growth and various historical socioeconomic$\backslash$nefforts to reduce damage, leading to non-significant upward or downward$\backslash$ntrends. We also show that the flood-exposed population is affected by$\backslash$nhistorical changes in population distribution, with changes in flood$\backslash$nvulnerability of up to 48.9{\%}. Both increasing and decreasing trends in$\backslash$nflood vulnerability were observed in different countries, implying that$\backslash$npopulation growth scenarios considering spatial distribution changes$\backslash$ncould affect flood risk projections.},
-author = {Tanoue, Masahiro and Hirabayashi, Yukiko and Ikeuchi, Hiroaki},
-doi = {10.1038/srep36021},
-issn = {20452322},
-journal = {Scientific Reports},
-pages = {1--9},
-publisher = {Nature Publishing Group},
-title = {{Global-scale river flood vulnerability in the last 50 years}},
-url = {http://dx.doi.org/10.1038/srep36021},
-volume = {6},
-year = {2016}
-}
-@article{Curtis2018,
-abstract = {Global maps of forest loss depict the scale and magnitude of forest disturbance,yet companies,governments,and nongovernmental organizations need to distinguish permanent conversion (i.e.,deforestation) from temporary loss from forestry or wildfire.Using satellite imagery,we developed a forest loss classification model to determine a spatial attribution of forest disturbance to the dominant drivers of land cover and land use change over the period 2001 to 2015.Our results indicate that 27{\%} of global forest loss can be attributed to deforestation through permanent land use change for commodity production.The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26{\%}),shifting agriculture (24{\%}),and wildfire (23{\%}).Despite corporate commitments,the rate of commodity-driven deforestation has not declined.To end deforestation,companies must eliminate 5 million hectares of conversion from supply chains each year.},
-author = {Curtis, Philip G. and Slay, Christy M. and Harris, Nancy L. and Tyukavina, Alexandra and Hansen, Matthew C.},
-doi = {10.1126/science.aau3445},
-
-issn = {10959203},
-journal = {Science},
-number = {6407},
-pages = {1108--1111},
-pmid = {30213911},
-title = {{Classifying drivers of global forest loss}},
-volume = {361},
-year = {2018}
-}
-
-@article{Liu2020,
-abstract = {High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones.},
-author = {Liu, Xiaoping and Huang, Yinghuai and Xu, Xiaocong and Li, Xuecao and Li, Xia and Ciais, Philippe and Lin, Peirong and Gong, Kai and Ziegler, Alan D. and Chen, Anping and Gong, Peng and Chen, Jun and Hu, Guohua and Chen, Yimin and Wang, Shaojian and Wu, Qiusheng and Huang, Kangning and Estes, Lyndon and Zeng, Zhenzhong},
-doi = {10.1038/s41893-020-0521-x},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {7},
-pages = {564--570},
-publisher = {Springer US},
-title = {{High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015}},
-url = {http://dx.doi.org/10.1038/s41893-020-0521-x},
-volume = {3},
-year = {2020}
-}
-
-
-@article{Vuuren2011,
-author = {van Vuuren, D P and Edmonds, J and Kainuma, M and Riahi, K and Thomson, A and Hibbard, K and Hurtt, G C and Kram, T and Krey, V and Lamarque, J F and Masui, T and Meinshausen, M and Nakicenovic, N and Smith, S J and Rose, S K},
-journal = {Climatic Change},
-pages = {5--31},
-title = {{The representative concentration pathways: an overview}},
-volume = {109},
-year = {2011}
-}
-
-
-@article{Eyring2016,
-abstract = {By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850-near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: How does the Earth system respond to forcing What are the origins and consequences of systematic model biases? How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.},
-author = {Eyring, Veronika and Bony, Sandrine and Meehl, Gerald A. and Senior, Catherine A. and Stevens, Bjorn and Stouffer, Ronald J. and Taylor, Karl E.},
-doi = {10.5194/gmd-9-1937-2016},
-issn = {19919603},
-journal = {Geoscientific Model Development},
-number = {5},
-pages = {1937--1958},
-title = {{Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization}},
-volume = {9},
-year = {2016}
-}
-
-@article{Almazroui2020,
-author = {Almazroui, Mansour and Saeed, Fahad and Saeed, Sajjad and {Nazrul Islam}, M. and Ismail, Muhammad and Klutse, Nana Ama Browne and Siddiqui, Muhammad Haroon},
-doi = {10.1007/s41748-020-00161-x},
-isbn = {0123456789},
-issn = {25099434},
-journal = {Earth Systems and Environment},
-keywords = {Africa,CMIP6,Future climate projections,Precipitation,Temperature},
-number = {3},
-pages = {455--475},
-publisher = {Springer International Publishing},
-title = {{Projected Change in Temperature and Precipitation Over Africa from CMIP6}},
-url = {https://doi.org/10.1007/s41748-020-00161-x},
-volume = {4},
-year = {2020}
-}
-
-@article{Pendergrass2018,
-author = {Pendergrass, Angeline G. and Knutti, Reto},
-doi = {10.1029/2018GL080298},
-issn = {19448007},
-journal = {Geophysical Research Letters},
-keywords = {climate change,climate models,distribution,extremes,observations,precipitation},
-number = {21},
-pages = {11,980--11,988},
-title = {{The Uneven Nature of Daily Precipitation and Its Change}},
-volume = {45},
-year = {2018}
-}
-
-@article{Nerem2018,
-author = {Nerem, R S and Beckley, B D and Fasullo, J T and Hamlington, B D and Masters, D and Mitchum, G T},
-doi = {10.1073/pnas.1717312115},
-issn = {0027-8424},
-journal = {Proceedings of the National Academy of Sciences},
-month = {feb},
-number = {9},
-pages = {2022--2025},
-title = {{Climate-change–driven accelerated sea-level rise detected in the altimeter era}},
-url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1717312115},
-volume = {115},
-year = {2018}
-}
-
-@article{Hirabayashi2013,
-author = {Hirabayashi, Yukiko and Mahendran, Roobavannan and Koirala, Sujan and Konoshima, Lisako and Yamazaki, Dai and Watanabe, Satoshi and Kim, Hyungjun and Kanae, Shinjiro},
-doi = {10.1038/nclimate1911},
-isbn = {doi:10.1038/nclimate1911},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {9},
-pages = {816--821},
-pmid = {2054449},
-publisher = {Nature Publishing Group},
-title = {{Global flood risk under climate change}},
-url = {http://dx.doi.org/10.1038/nclimate1911},
-volume = {3},
-year = {2013}
-}
-
-@article{Knutson2020,
-author = {Knutson, Thomas and Camargo, Suzana J. and Chan, Johnny C.L. and Emanuel, Kerry and Ho, Chang Hoi and Kossin, James and Mohapatra, Mrutyunjay and Satoh, Masaki and Sugi, Masato and Walsh, Kevin and Wu, Liguang},
-doi = {10.1175/BAMS-D-18-0194.1},
-issn = {00030007},
-journal = {Bulletin of the American Meteorological Society},
-number = {3},
-pages = {E303--E322},
-title = {{Tropical cyclones and climate change assessment part II: Projected response to anthropogenic warming}},
-volume = {101},
-year = {2020}
-}
-
-@article{Dai2013,
-author = {Dai, Aiguo},
-doi = {10.1038/nclimate1633},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {1},
-pages = {52--58},
-pmid = {5860533},
-publisher = {Nature Publishing Group},
-title = {{Increasing drought under global warming in observations and models}},
-url = {http://dx.doi.org/10.1038/nclimate1633},
-volume = {3},
-year = {2013}
-}
-
-@article{Kulp2019,
-author = {Kulp, Scott A. and Strauss, Benjamin H.},
-doi = {10.1038/s41467-019-12808-z},
-issn = {20411723},
-journal = {Nature Communications},
-number = {1},
-pmid = {31664024},
-publisher = {Springer US},
-title = {{New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding}},
-volume = {10},
-year = {2019}
-}
-
-@article{Christodoulou2018,
-author = {Christodoulou, Aris and Christidis, Panayotis and Demirel, Hande},
-doi = {10.1057/s41278-018-0114-z},
-journal = {Maritime Economics {\&} Logistics},
-keywords = {Climate change,Foreland,Hinterland,Maritime transport,Ports,Sea-level rise},
-month = {dec},
-number = {4},
-pages = {482--496},
-title = {{Sea-level rise in ports: a wider focus on impacts}},
-url = {http://link.springer.com/10.1057/s41278-018-0114-z},
-volume = {21},
-year = {2019}
-}
-
-@article{Yesudian2021,
-author = {Yesudian, Aaron N. and Dawson, Richard J.},
-doi = {10.1016/j.crm.2020.100266},
-issn = {22120963},
-journal = {Climate Risk Management},
-number = {November 2020},
-pages = {100266},
-publisher = {Elsevier B.V.},
-title = {{Global analysis of sea level rise risk to airports}},
-url = {https://doi.org/10.1016/j.crm.2020.100266},
-volume = {31},
-year = {2021}
-}
-
-@article{Forzieri2018,
-author = {Forzieri, Giovanni and Bianchi, Alessandra and Silva, Filipe Batista e. and {Marin Herrera}, Mario A. and Leblois, Antoine and Lavalle, Carlo and Aerts, Jeroen C.J.H. and Feyen, Luc},
-doi = {10.1016/j.gloenvcha.2017.11.007},
-journal = {Global Environmental Change},
-keywords = {Climate change impact,Critical infrastructures,Loss and damage,Multiple climate hazards},
-number = {April 2017},
-pages = {97--107},
-pmid = {29606806},
-publisher = {Elsevier Ltd},
-title = {{Escalating impacts of climate extremes on critical infrastructures in Europe}},
-volume = {48},
-year = {2018}
-}
-
-@article{Wong2017,
-author = {Wong, Tony E and Bakker, Alexander M R and Keller, Klaus},
-doi = {10.1007/s10584-017-2039-4},
-journal = {Climatic Change},
-month = {sep},
-number = {2},
-pages = {347--364},
-title = {{Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense}},
-url = {http://link.springer.com/10.1007/s10584-017-2039-4},
-volume = {144},
-year = {2017}
-}
-
-
-@techreport{Pant2019,
-address = {Oxford, United Kingdom},
-author = {Pant, Raghav and Koks, Elco E. and Paltan, Homero and Russell, Tom and Hall, Jim W.},
-institution = {Oxford Infrastructure Analytics},
-number = {August},
-pages = {1--154},
-title = {{Argentina – Transport risk analysis}},
-year = {2019}
-}
-
-@phdthesis{White1942,
-address = {Chicago},
-archivePrefix = {arXiv},
-arxivId = {arXiv:1011.1669v3},
-author = {White, Gilbert Fowler},
-booktitle = {Department of Geography Research Papers},
-eprint = {arXiv:1011.1669v3},
-isbn = {9788578110796},
-issn = {1098-6596},
-pages = {11--238},
-pmid = {25246403},
-school = {University of Chicago},
-title = {{Human Ajustment to floods: A Geographical aproach to the flood problem in the United States}},
-type = {PhD},
-year = {1942}
-}
-
-@misc{EuropeanEnvironmentalAgence2020,
-author = {{European Environmental Agence}},
-title = {{Global average near surface temperature since the pre-industrial period}},
-url = {https://www.eea.europa.eu/data-and-maps/figures/global-average-near-surface-temperature},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-@misc{GFDL,
-author = {GFDL},
-booktitle = {GFDL Model Development},
-title = {{Climate Modeling}},
-url = {https://www.gfdl.noaa.gov/climate-modeling/},
-urldate = {18-03-2021}
-}
-
-@misc{CarbonBrief2019,
-author = {CarbonBrief},
-booktitle = {Climate Modelling},
-title = {{CMIP6: the next generation of climate models explained}},
-url = {https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained},
-urldate = {18-03-2021},
-year = {2019}
-}
-
-@misc{TheIrishTimes2017,
-author = {{The Irish Times}},
-booktitle = {Climate Change},
-title = {{Climate change link to the timing of European floods}},
-url = {https://www.irishtimes.com/news/environment/climate-change-link-to-the-timing-of-european-floods-1.3182865},
-urldate = {18-03-2021},
-year = {2017}
-}
-
-@misc{NASA2020,
-author = {NASA},
-title = {{Climate Change Could Trigger More Landslides in High Mountain Asia}},
-url = {https://www.nasa.gov/feature/goddard/2020/climate-change-could-trigger-more-landslides-in-high-mountain-asia},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-
-@article{Hirabayashi2021,
-author = {Hirabayashi, Yukiko and Tanoue, Masahiro and Sasaki, Orie and Zhou, Xudong and Yamazaki, Dai},
-doi = {10.1038/s41598-021-83279-w},
-isbn = {0123456789},
-issn = {20452322},
-journal = {Scientific Reports},
-number = {1},
-pages = {1--7},
-publisher = {Nature Publishing Group UK},
-title = {{Global exposure to flooding from the new CMIP6 climate model projections}},
-url = {https://doi.org/10.1038/s41598-021-83279-w},
-volume = {11},
-year = {2021}
-}
-
-@article{owidnaturaldisasters,
- author = {Hannah Ritchie and Max Roser},
- title = {Natural Disasters},
- journal = {Our World in Data},
- year = {2014},
- note = {https://ourworldindata.org/natural-disasters}
-}
-
-@misc{EspaceMondial,
-author = {{Espace Mondial}},
-booktitle = {Maps and charrts},
-title = {{Extension of urban sprawl in selected cities, 1975-2015}},
-url = {https://espace-mondial-atlas.sciencespo.fr/en/topic-mobility/map-2C14-EN-extension-of-urban-sprawl-in-selected-cities-1975-2015.html},
-urldate = {2021-03-24}
-}
-@book{EuropeanCommissionExpertGrouponFAIRData2018,
-abstract = {In addressing the remit assigned, the FAIR Data Expert Group chose to take a holistic and systemic approach to describe the broad range of changes required to “turn FAIR data into reality”. The notions of findability, accessibility, interoperability and reusability - and the actions needed to enable them - are so deeply intertwined that it does not make sense to address them individually. Instead, this report focuses on actions needed in terms of research culture and technology to ensure data, code and other research outputs are made FAIR. Research culture and technology are two sides of one whole. Coordinated, simultaneous interventions are needed in each to enable FAIR in this broad sense. The implementation of FAIR will be supported through the European Open Science Cloud (EOSC). The federation of data infrastructure and application of standards will enable the discovery and interoperability of data. Member States should support this movement by aligning their policies and investments in relation to FAIR data and Open Science. In a wider global context, parallel initiatives such as the NIH Data Commons, the Australian Research Data Commons and also the proposed African Open Science Platform are important for the implementation of FAIR. Developments in the EOSC should align with these international movements and ensure that data are FAIR across disciplines and geographic boundaries beyond Europe. The central sections of this Report focus on existing practice in certain fields to ascertain what can be learned from those research areas that have already developed standards, international agreements and infrastructure to enable FAIR. These examples have helped to define models for FAIR Digital Objects and the essential components of a FAIR ecosystem. Naturally the main building blocks in the ecosystem are technology-based services. However, the social aspects that drive the system and enable culture change – namely skills, metrics, incentives and sustainable investment – are also addressed. The report makes a number of detailed recommendations and specifies actions for different stakeholder groups to enable the changes required. Implementing FAIR is a significant undertaking and requires changes in terms of research culture and infrastructure provision. These changes are important in the context of the European Open Science Cloud and the direction for European Commission and Member State policy, but go beyond that: FAIR requires global agreements to ensure the broadest interoperability and reusability of data - beyond disciplinary and geographic boundaries. Twenty-seven recommendations are made, which are grouped into ‘Priority' and ‘Supporting' Recommendations. The fifteen priority recommendations should be considered the initial set of changes or steps to take in order to implement FAIR. The Supporting Recommendations may be considered as following on from the Priority Recommendations, adding specifics or further detail for implementation. Each individual Recommendation is followed by a set of Actions. Each Recommendation and each Action is numbered for unambiguous referencing. The full set of Recommendations and Actions are presented in the FAIR Action Plan at the end of this report.},
-author = {{European Commission Expert Group on FAIR Data}},
-booktitle = {Final Report and Action Plan from the European Commission Expert Group on FAIR Data},
-doi = {10.2777/1524},
-file = {:Users/wusher/Documents/Mendeley Desktop/European Commission Expert Group on FAIR Data - 2018 - Turning FAIR into reality.pdf:pdf},
-isbn = {9789279965470},
-keywords = {DMP (Data Management Plan),EOSC (European Open Science Cloud),European politics,FAIR principle (Findable Accessible Interoperable,PGD (Plan de Gestion des Donn{\'{e}}es),Standardised data,identifiants p{\'{e}}rennes,m{\'{e}}tadonn{\'{e}}es,politique europ{\'{e}}enne},
-pages = {78},
-title = {{Turning FAIR into reality}},
-year = {2018}
-}
-@article{Ramos2020,
-abstract = {蚯蚓仿生机器人},
-author = {Ramos, Eunice Pereira and Howells, Mark and Sridharan, Vignesh and Engstr{\"{o}}m, Rebecka Ericsdotter and Taliotis, Constantinos and Mentis, Dimitris and Gardumi, Francesco and de Strasser, Lucia and Pappis, Ioannis and {Pe{\~{n}}a Balderrama}, Gabriela and Almulla, Youssef and Beltramo, Agnese and {Ramirez Gomez}, Camilo and Sundin, Caroline and Alfstad, Thomas and Lipponen, Annukka and Zepeda, Eduardo and Niet, Taco and Quir{\'{o}}s-Tort{\'{o}}s, Jairo and Angulo-Paniagua, Jam and Shivakumar, Abhishek and Ulloa, Silvia and Rogner, Holger},
-doi = {10.1088/1748-9326/abd34f},
-file = {:Users/wusher/Documents/Mendeley Desktop/Ramos et al. - 2020 - The Climate, Land, Energy, and Water systems (CLEWs) framework a retrospective of activities and advances to 2019.pdf:pdf},
-issn = {1748-9326},
-journal = {Environmental Research Letters},
-month = {dec},
-pages = {0--31},
-title = {{The Climate, Land, Energy, and Water systems (CLEWs) framework: a retrospective of activities and advances to 2019}},
-url = {https://iopscience.iop.org/article/10.1088/1748-9326/abd34f},
-volume = {2},
-year = {2020}
-}
-@article{Flyvbjerg2021,
-abstract = {Scale-up is the process of growing a venture in size. The paper identifies modularity and speed as keys to successful scale-up. On that basis four types of scale-up are identified: Smart, dumb, forced, and fumbled. Smart scale-up combines modularity and speed. Dumb scale-up is bespoke and slow, and very common. The paper presents examples of each type of scale-up, explaining why they were successful or not. Whether you are a small startup or Elon Musk trying to grow Tesla and SpaceX or Jeff Bezos scaling up Amazon – or you are the US, UK, Chinese, or other government trying to increase power production, expand your infrastructure, or make your health, education, and social services work better – modularity and speed are the answer to effective delivery, or so the paper argues. How well you deal with modularity and speed decides whether your efforts succeed or fail. Most ventures, existing or planned, are neither fully smart nor fully dumb, but have elements of both. Successful organizations work to tip the balance towards smart by (a) introducing elements of smart scale-up into existing ventures and (b) starting new, fully smart-scaled ventures, to make themselves less dumb and ever smarter},
-author = {Flyvbjerg, By Bent},
-file = {:Users/wusher/Documents/Mendeley Desktop/Flyvbjerg - 2021 - Four Ways to Scale Up Smart , Dumb , Forced , and Fumbled.pdf:pdf},
-number = {January},
-pages = {1--37},
-title = {{Four Ways to Scale Up : Smart , Dumb , Forced , and Fumbled}},
-year = {2021}
-}
-
-@article{Hallegatte2012,
-abstract = {The impact of climate change on economic losses from tropical cyclones is a major concern. New research shows that — like changes in population and assets — climate change may double global losses from hurricanes.},
-author = {Hallegatte, St{\'{e}}phane},
-doi = {10.1038/nclimate1427},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {3},
-pages = {148--149},
-publisher = {Nature Publishing Group},
-title = {{Economics: The rising costs of hurricanes}},
-url = {http://dx.doi.org/10.1038/nclimate1427},
-volume = {2},
-year = {2012}
-}
-
-@article{Vitousek2017,
-author = {Vitousek, S and Barnard, P L and Fletcher, C H and Frazer, N and Erikson, L and Storlazzi, C D},
-journal = {Scientific Reports},
-number = {1399},
-pages = {1--9},
-title = {{Doubling of coastal flooding frequency within decades due to sea-level rise}},
-volume = {7},
-year = {2017}
-}
-
-@article{Elsner2008,
-abstract = {Atlantic tropical cyclones are getting stronger on average, with a 30-year trend that has been related to an increase in ocean temperatures over the Atlantic Ocean and elsewhere. Over the rest of the tropics, however, possible trends in tropical cyclone intensity are less obvious, owing to the unreliability and incompleteness of the observational record and to a restricted focus, in previous trend analyses, on changes in average intensity. Here we overcome these two limitations by examining trends in the upper quantiles of per-cyclone maximum wind speeds (that is, the maximum intensities that cyclones achieve during their lifetimes), estimated from homogeneous data derived from an archive of satellite records. We find significant upward trends for wind speed quantiles above the 70th percentile, with trends as high as 0.3 +/- 0.09 m s(-1) yr(-1) (s.e.) for the strongest cyclones. We note separate upward trends in the estimated lifetime-maximum wind speeds of the very strongest tropical cyclones (99th percentile) over each ocean basin, with the largest increase at this quantile occurring over the North Atlantic, although not all basins show statistically significant increases. Our results are qualitatively consistent with the hypothesis that as the seas warm, the ocean has more energy to convert to tropical cyclone wind.},
-author = {Elsner, James B and Kossin, James P and Jagger, Thomas H},
-doi = {10.1038/nature07234},
-isbn = {1476-4687 (Electronic){\$}\backslash{\$}n0028-0836 (Linking)},
-issn = {14764687},
-journal = {Nature},
-number = {7209},
-pages = {92--95},
-pmid = {18769438},
-title = {{The increasing intensity of the strongest tropical cyclones}},
-volume = {455},
-year = {2008}
-}
-
-
-@article{Bloschl2019,
-abstract = {Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.},
-author = {Bl{\"{o}}schl, G{\"{u}}nter and Hall, Julia and Viglione, Alberto and Perdig{\~{a}}o, Rui A.P. and Parajka, Juraj and Merz, Bruno and Lun, David and Arheimer, Berit and Aronica, Giuseppe T. and Bilibashi, Ardian and Boh{\'{a}}{\v{c}}, Miloň and Bonacci, Ognjen and Borga, Marco and {\v{C}}anjevac, Ivan and Castellarin, Attilio and Chirico, Giovanni B. and Claps, Pierluigi and Frolova, Natalia and Ganora, Daniele and Gorbachova, Liudmyla and G{\"{u}}l, Ali and Hannaford, Jamie and Harrigan, Shaun and Kireeva, Maria and Kiss, Andrea and Kjeldsen, Thomas R. and Kohnov{\'{a}}, Silvia and Koskela, Jarkko J. and Ledvinka, Ondrej and Macdonald, Neil and Mavrova-Guirguinova, Maria and Mediero, Luis and Merz, Ralf and Molnar, Peter and Montanari, Alberto and Murphy, Conor and Osuch, Marzena and Ovcharuk, Valeryia and Radevski, Ivan and Salinas, Jos{\'{e}} L. and Sauquet, Eric and {\v{S}}raj, Mojca and Szolgay, Jan and Volpi, Elena and Wilson, Donna and Zaimi, Klodian and {\v{Z}}ivkovi{\'{c}}, Nenad},
-doi = {10.1038/s41586-019-1495-6},
-issn = {14764687},
-journal = {Nature},
-number = {7772},
-pages = {108--111},
-pmid = {31462777},
-publisher = {Springer US},
-title = {{Changing climate both increases and decreases European river floods}},
-url = {http://dx.doi.org/10.1038/s41586-019-1495-6},
-volume = {573},
-year = {2019}
-}
-@article{Lehmann2019,
-abstract = {Deforestation of steep slopes may temporarily reduce evapotranspiration and lessen root reinforcement thus potentially enhancing landslide susceptibility. Quantifying the effects of deforestation and associated perturbations on landslide characteristics remains a challenge, especially for predictions in remote areas with limited information. We applied the STEP-TRAMM model that uses publicly available climatic and landscape information to assess effects of forest alteration on hydro-mechanical processes. The model considers two types of forest alterations: (i) removal of root reinforcement following permanent forest conversion, and (ii) time dependent root decay and regrowth following clear-cut timber harvesting. The model was applied to four study areas in different climatic regions (New Zealand, Oregon, Sumatra and Cambodia). We compared model predictions of landslide metrics with satellite-imaging of landslides following deforestation. Although we observe a higher propensity and larger landslides in deforested areas, effects were sensitive to deforestation practices and patterns. The largest increase in landslide area was associated with large and interconnected deforested tracts within a few years after deforestation as determined by competition between root decay and forest regrowth. For patchy small-scale forest conversion, the landslide areas were smaller but could occur many years after deforestation ({\textgreater} 10 years). The modeling framework offers ability to evaluate forest alteration scenarios through their potential impact on landslide hazard in specific regions of the landscape.},
-author = {Lehmann, Peter and von Ruette, Jonas and Or, Dani},
-doi = {10.1029/2019WR025233},
-issn = {19447973},
-journal = {Water Resources Research},
-keywords = {Landslide,Modeling,Rainfall,Remote Sensing},
-number = {11},
-pages = {9962--9976},
-title = {{Deforestation Effects on Rainfall-Induced Shallow Landslides: Remote Sensing and Physically-Based Modelling}},
-volume = {55},
-year = {2019}
-}
-@article{Du2015,
-abstract = {To date, limited attention has been paid to the role of impervious surface (IS) location in influencing flood processes. However, this topic is of tremendous significance for developing guidelines for urban planning and flood management. This study uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to investigate the impact of land-use change on flood processes and proposes a new index to quantify the impact of IS location on basin peak discharge. The results indicate that rapid urban expansion in the Longhua Basin, China, has increased peak discharge and flood volume by 140 and 162 {\%} over the past 30 years, respectively. The new index, named the Impervious Surface Impact Index, describes the spatially varying effects of IS increase in individual sub-basins on a basin's peak discharge. For the Longhua Basin, the index varies from 0.43 in downstream sub-basins to 5.91 in upstream sub-basins. An increase in upstream IS increases peak discharge nearly 14 times more than the same increase in downstream IS. Accordingly, the location of newly created IS can influence flood processes significantly. These findings can help to find suitable locations for urban development while mitigating the impact of land development on flood risks.},
-author = {Du, Shiqiang and Shi, Peijun and {Van Rompaey}, Anton and Wen, Jiahong},
-doi = {10.1007/s11069-014-1463-2},
-issn = {15730840},
-journal = {Natural Hazards},
-keywords = {Flood mitigation,Flood risk,Impervious surface,Low-impact development,Peak flow},
-number = {3},
-pages = {1457--1471},
-title = {{Quantifying the impact of impervious surface location on flood peak discharge in urban areas}},
-volume = {76},
-year = {2015}
-}
-@article{DiBaldassarre2018,
-abstract = {The expansion of reservoirs to cope with droughts and water shortages is hotly debated in many places around the world. We argue that there are two counterintuitive dynamics that should be considered in this debate: supply–demand cycles and reservoir effects. Supply–demand cycles describe instances where increasing water supply enables higher water demand, which can quickly offset the initial benefits of reservoirs. Reservoir effects refer to cases where over-reliance on reservoirs increases vulnerability, and therefore increases the potential damage caused by droughts. Here we illustrate these counterintuitive dynamics with global and local examples, and discuss policy and research implications.},
-author = {{Di Baldassarre}, Giuliano and Wanders, Niko and AghaKouchak, Amir and Kuil, Linda and Rangecroft, Sally and Veldkamp, Ted I.E. and Garcia, Margaret and van Oel, Pieter R. and Breinl, Korbinian and {Van Loon}, Anne F.},
-doi = {10.1038/s41893-018-0159-0},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {11},
-pages = {617--622},
-publisher = {Springer US},
-title = {{Water shortages worsened by reservoir effects}},
-url = {http://dx.doi.org/10.1038/s41893-018-0159-0},
-volume = {1},
-year = {2018}
-}
-@article{Tanoue2016,
-abstract = {The impacts of flooding are expected to rise due to population$\backslash$nincreases, economic growth and climate change. Hence, understanding the$\backslash$nphysical and spatiotemporal characteristics of risk drivers (hazard,$\backslash$nexposure and vulnerability) is required to develop effective flood$\backslash$nmitigation measures. Here, the long-term trend in flood vulnerability$\backslash$nwas analysed globally, calculated from the ratio of the reported flood$\backslash$nloss or damage to the modelled flood exposure using a global river and$\backslash$ninundation model. A previous study showed decreasing global flood$\backslash$nvulnerability over a shorter period using different disaster data. The$\backslash$nlong-term analysis demonstrated for the first time that flood$\backslash$nvulnerability to economic losses in upper-middle, lower-middle and$\backslash$nlow-income countries shows an inverted U-shape, as a result of the$\backslash$nbalance between economic growth and various historical socioeconomic$\backslash$nefforts to reduce damage, leading to non-significant upward or downward$\backslash$ntrends. We also show that the flood-exposed population is affected by$\backslash$nhistorical changes in population distribution, with changes in flood$\backslash$nvulnerability of up to 48.9{\%}. Both increasing and decreasing trends in$\backslash$nflood vulnerability were observed in different countries, implying that$\backslash$npopulation growth scenarios considering spatial distribution changes$\backslash$ncould affect flood risk projections.},
-author = {Tanoue, Masahiro and Hirabayashi, Yukiko and Ikeuchi, Hiroaki},
-doi = {10.1038/srep36021},
-issn = {20452322},
-journal = {Scientific Reports},
-pages = {1--9},
-publisher = {Nature Publishing Group},
-title = {{Global-scale river flood vulnerability in the last 50 years}},
-url = {http://dx.doi.org/10.1038/srep36021},
-volume = {6},
-year = {2016}
-}
-@article{Curtis2018,
-abstract = {Global maps of forest loss depict the scale and magnitude of forest disturbance,yet companies,governments,and nongovernmental organizations need to distinguish permanent conversion (i.e.,deforestation) from temporary loss from forestry or wildfire.Using satellite imagery,we developed a forest loss classification model to determine a spatial attribution of forest disturbance to the dominant drivers of land cover and land use change over the period 2001 to 2015.Our results indicate that 27{\%} of global forest loss can be attributed to deforestation through permanent land use change for commodity production.The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26{\%}),shifting agriculture (24{\%}),and wildfire (23{\%}).Despite corporate commitments,the rate of commodity-driven deforestation has not declined.To end deforestation,companies must eliminate 5 million hectares of conversion from supply chains each year.},
-author = {Curtis, Philip G. and Slay, Christy M. and Harris, Nancy L. and Tyukavina, Alexandra and Hansen, Matthew C.},
-doi = {10.1126/science.aau3445},
-
-issn = {10959203},
-journal = {Science},
-number = {6407},
-pages = {1108--1111},
-pmid = {30213911},
-title = {{Classifying drivers of global forest loss}},
-volume = {361},
-year = {2018}
-}
-
-@article{Liu2020,
-abstract = {High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones.},
-author = {Liu, Xiaoping and Huang, Yinghuai and Xu, Xiaocong and Li, Xuecao and Li, Xia and Ciais, Philippe and Lin, Peirong and Gong, Kai and Ziegler, Alan D. and Chen, Anping and Gong, Peng and Chen, Jun and Hu, Guohua and Chen, Yimin and Wang, Shaojian and Wu, Qiusheng and Huang, Kangning and Estes, Lyndon and Zeng, Zhenzhong},
-doi = {10.1038/s41893-020-0521-x},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {7},
-pages = {564--570},
-publisher = {Springer US},
-title = {{High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015}},
-url = {http://dx.doi.org/10.1038/s41893-020-0521-x},
-volume = {3},
-year = {2020}
-}
-
-
-@article{Vuuren2011,
-author = {van Vuuren, D P and Edmonds, J and Kainuma, M and Riahi, K and Thomson, A and Hibbard, K and Hurtt, G C and Kram, T and Krey, V and Lamarque, J F and Masui, T and Meinshausen, M and Nakicenovic, N and Smith, S J and Rose, S K},
-journal = {Climatic Change},
-pages = {5--31},
-title = {{The representative concentration pathways: an overview}},
-volume = {109},
-year = {2011}
-}
-
-
-@article{Eyring2016,
-abstract = {By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850-near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: How does the Earth system respond to forcing What are the origins and consequences of systematic model biases? How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.},
-author = {Eyring, Veronika and Bony, Sandrine and Meehl, Gerald A. and Senior, Catherine A. and Stevens, Bjorn and Stouffer, Ronald J. and Taylor, Karl E.},
-doi = {10.5194/gmd-9-1937-2016},
-issn = {19919603},
-journal = {Geoscientific Model Development},
-number = {5},
-pages = {1937--1958},
-title = {{Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization}},
-volume = {9},
-year = {2016}
-}
-
-@article{Almazroui2020,
-author = {Almazroui, Mansour and Saeed, Fahad and Saeed, Sajjad and {Nazrul Islam}, M. and Ismail, Muhammad and Klutse, Nana Ama Browne and Siddiqui, Muhammad Haroon},
-doi = {10.1007/s41748-020-00161-x},
-isbn = {0123456789},
-issn = {25099434},
-journal = {Earth Systems and Environment},
-keywords = {Africa,CMIP6,Future climate projections,Precipitation,Temperature},
-number = {3},
-pages = {455--475},
-publisher = {Springer International Publishing},
-title = {{Projected Change in Temperature and Precipitation Over Africa from CMIP6}},
-url = {https://doi.org/10.1007/s41748-020-00161-x},
-volume = {4},
-year = {2020}
-}
-
-@article{Pendergrass2018,
-author = {Pendergrass, Angeline G. and Knutti, Reto},
-doi = {10.1029/2018GL080298},
-issn = {19448007},
-journal = {Geophysical Research Letters},
-keywords = {climate change,climate models,distribution,extremes,observations,precipitation},
-number = {21},
-pages = {11,980--11,988},
-title = {{The Uneven Nature of Daily Precipitation and Its Change}},
-volume = {45},
-year = {2018}
-}
-
-@article{Nerem2018,
-author = {Nerem, R S and Beckley, B D and Fasullo, J T and Hamlington, B D and Masters, D and Mitchum, G T},
-doi = {10.1073/pnas.1717312115},
-issn = {0027-8424},
-journal = {Proceedings of the National Academy of Sciences},
-month = {feb},
-number = {9},
-pages = {2022--2025},
-title = {{Climate-change–driven accelerated sea-level rise detected in the altimeter era}},
-url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1717312115},
-volume = {115},
-year = {2018}
-}
-
-@article{Hirabayashi2013,
-author = {Hirabayashi, Yukiko and Mahendran, Roobavannan and Koirala, Sujan and Konoshima, Lisako and Yamazaki, Dai and Watanabe, Satoshi and Kim, Hyungjun and Kanae, Shinjiro},
-doi = {10.1038/nclimate1911},
-isbn = {doi:10.1038/nclimate1911},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {9},
-pages = {816--821},
-pmid = {2054449},
-publisher = {Nature Publishing Group},
-title = {{Global flood risk under climate change}},
-url = {http://dx.doi.org/10.1038/nclimate1911},
-volume = {3},
-year = {2013}
-}
-
-@article{Knutson2020,
-author = {Knutson, Thomas and Camargo, Suzana J. and Chan, Johnny C.L. and Emanuel, Kerry and Ho, Chang Hoi and Kossin, James and Mohapatra, Mrutyunjay and Satoh, Masaki and Sugi, Masato and Walsh, Kevin and Wu, Liguang},
-doi = {10.1175/BAMS-D-18-0194.1},
-issn = {00030007},
-journal = {Bulletin of the American Meteorological Society},
-number = {3},
-pages = {E303--E322},
-title = {{Tropical cyclones and climate change assessment part II: Projected response to anthropogenic warming}},
-volume = {101},
-year = {2020}
-}
-
-@article{Dai2013,
-author = {Dai, Aiguo},
-doi = {10.1038/nclimate1633},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {1},
-pages = {52--58},
-pmid = {5860533},
-publisher = {Nature Publishing Group},
-title = {{Increasing drought under global warming in observations and models}},
-url = {http://dx.doi.org/10.1038/nclimate1633},
-volume = {3},
-year = {2013}
-}
-
-@article{Kulp2019,
-author = {Kulp, Scott A. and Strauss, Benjamin H.},
-doi = {10.1038/s41467-019-12808-z},
-issn = {20411723},
-journal = {Nature Communications},
-number = {1},
-pmid = {31664024},
-publisher = {Springer US},
-title = {{New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding}},
-volume = {10},
-year = {2019}
-}
-
-@article{Christodoulou2018,
-author = {Christodoulou, Aris and Christidis, Panayotis and Demirel, Hande},
-doi = {10.1057/s41278-018-0114-z},
-journal = {Maritime Economics {\&} Logistics},
-keywords = {Climate change,Foreland,Hinterland,Maritime transport,Ports,Sea-level rise},
-month = {dec},
-number = {4},
-pages = {482--496},
-title = {{Sea-level rise in ports: a wider focus on impacts}},
-url = {http://link.springer.com/10.1057/s41278-018-0114-z},
-volume = {21},
-year = {2019}
-}
-
-@article{Yesudian2021,
-author = {Yesudian, Aaron N. and Dawson, Richard J.},
-doi = {10.1016/j.crm.2020.100266},
-issn = {22120963},
-journal = {Climate Risk Management},
-number = {November 2020},
-pages = {100266},
-publisher = {Elsevier B.V.},
-title = {{Global analysis of sea level rise risk to airports}},
-url = {https://doi.org/10.1016/j.crm.2020.100266},
-volume = {31},
-year = {2021}
-}
-
-@article{Forzieri2018,
-author = {Forzieri, Giovanni and Bianchi, Alessandra and Silva, Filipe Batista e. and {Marin Herrera}, Mario A. and Leblois, Antoine and Lavalle, Carlo and Aerts, Jeroen C.J.H. and Feyen, Luc},
-doi = {10.1016/j.gloenvcha.2017.11.007},
-journal = {Global Environmental Change},
-keywords = {Climate change impact,Critical infrastructures,Loss and damage,Multiple climate hazards},
-number = {April 2017},
-pages = {97--107},
-pmid = {29606806},
-publisher = {Elsevier Ltd},
-title = {{Escalating impacts of climate extremes on critical infrastructures in Europe}},
-volume = {48},
-year = {2018}
-}
-
-@article{Wong2017,
-author = {Wong, Tony E and Bakker, Alexander M R and Keller, Klaus},
-doi = {10.1007/s10584-017-2039-4},
-journal = {Climatic Change},
-month = {sep},
-number = {2},
-pages = {347--364},
-title = {{Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense}},
-url = {http://link.springer.com/10.1007/s10584-017-2039-4},
-volume = {144},
-year = {2017}
-}
-
-
-@techreport{Pant2019,
-address = {Oxford, United Kingdom},
-author = {Pant, Raghav and Koks, Elco E. and Paltan, Homero and Russell, Tom and Hall, Jim W.},
-institution = {Oxford Infrastructure Analytics},
-number = {August},
-pages = {1--154},
-title = {{Argentina – Transport risk analysis}},
-year = {2019}
-}
-
-@phdthesis{White1942,
-address = {Chicago},
-archivePrefix = {arXiv},
-arxivId = {arXiv:1011.1669v3},
-author = {White, Gilbert Fowler},
-booktitle = {Department of Geography Research Papers},
-eprint = {arXiv:1011.1669v3},
-isbn = {9788578110796},
-issn = {1098-6596},
-pages = {11--238},
-pmid = {25246403},
-school = {University of Chicago},
-title = {{Human Ajustment to floods: A Geographical aproach to the flood problem in the United States}},
-type = {PhD},
-year = {1942}
-}
-
-@misc{EuropeanEnvironmentalAgence2020,
-author = {{European Environmental Agence}},
-title = {{Global average near surface temperature since the pre-industrial period}},
-url = {https://www.eea.europa.eu/data-and-maps/figures/global-average-near-surface-temperature},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-@misc{GFDL,
-author = {GFDL},
-booktitle = {GFDL Model Development},
-title = {{Climate Modeling}},
-url = {https://www.gfdl.noaa.gov/climate-modeling/},
-urldate = {18-03-2021}
-}
-
-@misc{CarbonBrief2019,
-author = {CarbonBrief},
-booktitle = {Climate Modelling},
-title = {{CMIP6: the next generation of climate models explained}},
-url = {https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained},
-urldate = {18-03-2021},
-year = {2019}
-}
-
-@misc{TheIrishTimes2017,
-author = {{The Irish Times}},
-booktitle = {Climate Change},
-title = {{Climate change link to the timing of European floods}},
-url = {https://www.irishtimes.com/news/environment/climate-change-link-to-the-timing-of-european-floods-1.3182865},
-urldate = {18-03-2021},
-year = {2017}
-}
-
-@misc{NASA2020,
-author = {NASA},
-title = {{Climate Change Could Trigger More Landslides in High Mountain Asia}},
-url = {https://www.nasa.gov/feature/goddard/2020/climate-change-could-trigger-more-landslides-in-high-mountain-asia},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-
-@article{Hirabayashi2021,
-author = {Hirabayashi, Yukiko and Tanoue, Masahiro and Sasaki, Orie and Zhou, Xudong and Yamazaki, Dai},
-doi = {10.1038/s41598-021-83279-w},
-isbn = {0123456789},
-issn = {20452322},
-journal = {Scientific Reports},
-number = {1},
-pages = {1--7},
-publisher = {Nature Publishing Group UK},
-title = {{Global exposure to flooding from the new CMIP6 climate model projections}},
-url = {https://doi.org/10.1038/s41598-021-83279-w},
-volume = {11},
-year = {2021}
-}
-
-@article{owidnaturaldisasters,
- author = {Hannah Ritchie and Max Roser},
- title = {Natural Disasters},
- journal = {Our World in Data},
- year = {2014},
- note = {https://ourworldindata.org/natural-disasters}
-}
-
-@misc{EspaceMondial,
-author = {{Espace Mondial}},
-booktitle = {Maps and charrts},
-title = {{Extension of urban sprawl in selected cities, 1975-2015}},
-url = {https://espace-mondial-atlas.sciencespo.fr/en/topic-mobility/map-2C14-EN-extension-of-urban-sprawl-in-selected-cities-1975-2015.html},
-urldate = {2021-03-24}
-}
-@book{EuropeanCommissionExpertGrouponFAIRData2018,
-abstract = {In addressing the remit assigned, the FAIR Data Expert Group chose to take a holistic and systemic approach to describe the broad range of changes required to “turn FAIR data into reality”. The notions of findability, accessibility, interoperability and reusability - and the actions needed to enable them - are so deeply intertwined that it does not make sense to address them individually. Instead, this report focuses on actions needed in terms of research culture and technology to ensure data, code and other research outputs are made FAIR. Research culture and technology are two sides of one whole. Coordinated, simultaneous interventions are needed in each to enable FAIR in this broad sense. The implementation of FAIR will be supported through the European Open Science Cloud (EOSC). The federation of data infrastructure and application of standards will enable the discovery and interoperability of data. Member States should support this movement by aligning their policies and investments in relation to FAIR data and Open Science. In a wider global context, parallel initiatives such as the NIH Data Commons, the Australian Research Data Commons and also the proposed African Open Science Platform are important for the implementation of FAIR. Developments in the EOSC should align with these international movements and ensure that data are FAIR across disciplines and geographic boundaries beyond Europe. The central sections of this Report focus on existing practice in certain fields to ascertain what can be learned from those research areas that have already developed standards, international agreements and infrastructure to enable FAIR. These examples have helped to define models for FAIR Digital Objects and the essential components of a FAIR ecosystem. Naturally the main building blocks in the ecosystem are technology-based services. However, the social aspects that drive the system and enable culture change – namely skills, metrics, incentives and sustainable investment – are also addressed. The report makes a number of detailed recommendations and specifies actions for different stakeholder groups to enable the changes required. Implementing FAIR is a significant undertaking and requires changes in terms of research culture and infrastructure provision. These changes are important in the context of the European Open Science Cloud and the direction for European Commission and Member State policy, but go beyond that: FAIR requires global agreements to ensure the broadest interoperability and reusability of data - beyond disciplinary and geographic boundaries. Twenty-seven recommendations are made, which are grouped into ‘Priority' and ‘Supporting' Recommendations. The fifteen priority recommendations should be considered the initial set of changes or steps to take in order to implement FAIR. The Supporting Recommendations may be considered as following on from the Priority Recommendations, adding specifics or further detail for implementation. Each individual Recommendation is followed by a set of Actions. Each Recommendation and each Action is numbered for unambiguous referencing. The full set of Recommendations and Actions are presented in the FAIR Action Plan at the end of this report.},
-author = {{European Commission Expert Group on FAIR Data}},
-booktitle = {Final Report and Action Plan from the European Commission Expert Group on FAIR Data},
-doi = {10.2777/1524},
-file = {:Users/wusher/Documents/Mendeley Desktop/European Commission Expert Group on FAIR Data - 2018 - Turning FAIR into reality.pdf:pdf},
-isbn = {9789279965470},
-keywords = {DMP (Data Management Plan),EOSC (European Open Science Cloud),European politics,FAIR principle (Findable Accessible Interoperable,PGD (Plan de Gestion des Donn{\'{e}}es),Standardised data,identifiants p{\'{e}}rennes,m{\'{e}}tadonn{\'{e}}es,politique europ{\'{e}}enne},
-pages = {78},
-title = {{Turning FAIR into reality}},
-year = {2018}
-}
-@article{Ramos2020,
-abstract = {蚯蚓仿生机器人},
-author = {Ramos, Eunice Pereira and Howells, Mark and Sridharan, Vignesh and Engstr{\"{o}}m, Rebecka Ericsdotter and Taliotis, Constantinos and Mentis, Dimitris and Gardumi, Francesco and de Strasser, Lucia and Pappis, Ioannis and {Pe{\~{n}}a Balderrama}, Gabriela and Almulla, Youssef and Beltramo, Agnese and {Ramirez Gomez}, Camilo and Sundin, Caroline and Alfstad, Thomas and Lipponen, Annukka and Zepeda, Eduardo and Niet, Taco and Quir{\'{o}}s-Tort{\'{o}}s, Jairo and Angulo-Paniagua, Jam and Shivakumar, Abhishek and Ulloa, Silvia and Rogner, Holger},
-doi = {10.1088/1748-9326/abd34f},
-file = {:Users/wusher/Documents/Mendeley Desktop/Ramos et al. - 2020 - The Climate, Land, Energy, and Water systems (CLEWs) framework a retrospective of activities and advances to 2019.pdf:pdf},
-issn = {1748-9326},
-journal = {Environmental Research Letters},
-month = {dec},
-pages = {0--31},
-title = {{The Climate, Land, Energy, and Water systems (CLEWs) framework: a retrospective of activities and advances to 2019}},
-url = {https://iopscience.iop.org/article/10.1088/1748-9326/abd34f},
-volume = {2},
-year = {2020}
-}
-@article{Flyvbjerg2021,
-abstract = {Scale-up is the process of growing a venture in size. The paper identifies modularity and speed as keys to successful scale-up. On that basis four types of scale-up are identified: Smart, dumb, forced, and fumbled. Smart scale-up combines modularity and speed. Dumb scale-up is bespoke and slow, and very common. The paper presents examples of each type of scale-up, explaining why they were successful or not. Whether you are a small startup or Elon Musk trying to grow Tesla and SpaceX or Jeff Bezos scaling up Amazon – or you are the US, UK, Chinese, or other government trying to increase power production, expand your infrastructure, or make your health, education, and social services work better – modularity and speed are the answer to effective delivery, or so the paper argues. How well you deal with modularity and speed decides whether your efforts succeed or fail. Most ventures, existing or planned, are neither fully smart nor fully dumb, but have elements of both. Successful organizations work to tip the balance towards smart by (a) introducing elements of smart scale-up into existing ventures and (b) starting new, fully smart-scaled ventures, to make themselves less dumb and ever smarter},
-author = {Flyvbjerg, By Bent},
-file = {:Users/wusher/Documents/Mendeley Desktop/Flyvbjerg - 2021 - Four Ways to Scale Up Smart , Dumb , Forced , and Fumbled.pdf:pdf},
-number = {January},
-pages = {1--37},
-title = {{Four Ways to Scale Up : Smart , Dumb , Forced , and Fumbled}},
-year = {2021}
-}
-
-@article{Hallegatte2012,
-abstract = {The impact of climate change on economic losses from tropical cyclones is a major concern. New research shows that — like changes in population and assets — climate change may double global losses from hurricanes.},
-author = {Hallegatte, St{\'{e}}phane},
-doi = {10.1038/nclimate1427},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {3},
-pages = {148--149},
-publisher = {Nature Publishing Group},
-title = {{Economics: The rising costs of hurricanes}},
-url = {http://dx.doi.org/10.1038/nclimate1427},
-volume = {2},
-year = {2012}
-}
-
-@article{Vitousek2017,
-author = {Vitousek, S and Barnard, P L and Fletcher, C H and Frazer, N and Erikson, L and Storlazzi, C D},
-journal = {Scientific Reports},
-number = {1399},
-pages = {1--9},
-title = {{Doubling of coastal flooding frequency within decades due to sea-level rise}},
-volume = {7},
-year = {2017}
-}
-
-@article{Elsner2008,
-abstract = {Atlantic tropical cyclones are getting stronger on average, with a 30-year trend that has been related to an increase in ocean temperatures over the Atlantic Ocean and elsewhere. Over the rest of the tropics, however, possible trends in tropical cyclone intensity are less obvious, owing to the unreliability and incompleteness of the observational record and to a restricted focus, in previous trend analyses, on changes in average intensity. Here we overcome these two limitations by examining trends in the upper quantiles of per-cyclone maximum wind speeds (that is, the maximum intensities that cyclones achieve during their lifetimes), estimated from homogeneous data derived from an archive of satellite records. We find significant upward trends for wind speed quantiles above the 70th percentile, with trends as high as 0.3 +/- 0.09 m s(-1) yr(-1) (s.e.) for the strongest cyclones. We note separate upward trends in the estimated lifetime-maximum wind speeds of the very strongest tropical cyclones (99th percentile) over each ocean basin, with the largest increase at this quantile occurring over the North Atlantic, although not all basins show statistically significant increases. Our results are qualitatively consistent with the hypothesis that as the seas warm, the ocean has more energy to convert to tropical cyclone wind.},
-author = {Elsner, James B and Kossin, James P and Jagger, Thomas H},
-doi = {10.1038/nature07234},
-isbn = {1476-4687 (Electronic){\$}\backslash{\$}n0028-0836 (Linking)},
-issn = {14764687},
-journal = {Nature},
-number = {7209},
-pages = {92--95},
-pmid = {18769438},
-title = {{The increasing intensity of the strongest tropical cyclones}},
-volume = {455},
-year = {2008}
-}
-
-
-@article{Bloschl2019,
-abstract = {Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.},
-author = {Bl{\"{o}}schl, G{\"{u}}nter and Hall, Julia and Viglione, Alberto and Perdig{\~{a}}o, Rui A.P. and Parajka, Juraj and Merz, Bruno and Lun, David and Arheimer, Berit and Aronica, Giuseppe T. and Bilibashi, Ardian and Boh{\'{a}}{\v{c}}, Miloň and Bonacci, Ognjen and Borga, Marco and {\v{C}}anjevac, Ivan and Castellarin, Attilio and Chirico, Giovanni B. and Claps, Pierluigi and Frolova, Natalia and Ganora, Daniele and Gorbachova, Liudmyla and G{\"{u}}l, Ali and Hannaford, Jamie and Harrigan, Shaun and Kireeva, Maria and Kiss, Andrea and Kjeldsen, Thomas R. and Kohnov{\'{a}}, Silvia and Koskela, Jarkko J. and Ledvinka, Ondrej and Macdonald, Neil and Mavrova-Guirguinova, Maria and Mediero, Luis and Merz, Ralf and Molnar, Peter and Montanari, Alberto and Murphy, Conor and Osuch, Marzena and Ovcharuk, Valeryia and Radevski, Ivan and Salinas, Jos{\'{e}} L. and Sauquet, Eric and {\v{S}}raj, Mojca and Szolgay, Jan and Volpi, Elena and Wilson, Donna and Zaimi, Klodian and {\v{Z}}ivkovi{\'{c}}, Nenad},
-doi = {10.1038/s41586-019-1495-6},
-issn = {14764687},
-journal = {Nature},
-number = {7772},
-pages = {108--111},
-pmid = {31462777},
-publisher = {Springer US},
-title = {{Changing climate both increases and decreases European river floods}},
-url = {http://dx.doi.org/10.1038/s41586-019-1495-6},
-volume = {573},
-year = {2019}
-}
-@article{Lehmann2019,
-abstract = {Deforestation of steep slopes may temporarily reduce evapotranspiration and lessen root reinforcement thus potentially enhancing landslide susceptibility. Quantifying the effects of deforestation and associated perturbations on landslide characteristics remains a challenge, especially for predictions in remote areas with limited information. We applied the STEP-TRAMM model that uses publicly available climatic and landscape information to assess effects of forest alteration on hydro-mechanical processes. The model considers two types of forest alterations: (i) removal of root reinforcement following permanent forest conversion, and (ii) time dependent root decay and regrowth following clear-cut timber harvesting. The model was applied to four study areas in different climatic regions (New Zealand, Oregon, Sumatra and Cambodia). We compared model predictions of landslide metrics with satellite-imaging of landslides following deforestation. Although we observe a higher propensity and larger landslides in deforested areas, effects were sensitive to deforestation practices and patterns. The largest increase in landslide area was associated with large and interconnected deforested tracts within a few years after deforestation as determined by competition between root decay and forest regrowth. For patchy small-scale forest conversion, the landslide areas were smaller but could occur many years after deforestation ({\textgreater} 10 years). The modeling framework offers ability to evaluate forest alteration scenarios through their potential impact on landslide hazard in specific regions of the landscape.},
-author = {Lehmann, Peter and von Ruette, Jonas and Or, Dani},
-doi = {10.1029/2019WR025233},
-issn = {19447973},
-journal = {Water Resources Research},
-keywords = {Landslide,Modeling,Rainfall,Remote Sensing},
-number = {11},
-pages = {9962--9976},
-title = {{Deforestation Effects on Rainfall-Induced Shallow Landslides: Remote Sensing and Physically-Based Modelling}},
-volume = {55},
-year = {2019}
-}
-@article{Du2015,
-abstract = {To date, limited attention has been paid to the role of impervious surface (IS) location in influencing flood processes. However, this topic is of tremendous significance for developing guidelines for urban planning and flood management. This study uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to investigate the impact of land-use change on flood processes and proposes a new index to quantify the impact of IS location on basin peak discharge. The results indicate that rapid urban expansion in the Longhua Basin, China, has increased peak discharge and flood volume by 140 and 162 {\%} over the past 30 years, respectively. The new index, named the Impervious Surface Impact Index, describes the spatially varying effects of IS increase in individual sub-basins on a basin's peak discharge. For the Longhua Basin, the index varies from 0.43 in downstream sub-basins to 5.91 in upstream sub-basins. An increase in upstream IS increases peak discharge nearly 14 times more than the same increase in downstream IS. Accordingly, the location of newly created IS can influence flood processes significantly. These findings can help to find suitable locations for urban development while mitigating the impact of land development on flood risks.},
-author = {Du, Shiqiang and Shi, Peijun and {Van Rompaey}, Anton and Wen, Jiahong},
-doi = {10.1007/s11069-014-1463-2},
-issn = {15730840},
-journal = {Natural Hazards},
-keywords = {Flood mitigation,Flood risk,Impervious surface,Low-impact development,Peak flow},
-number = {3},
-pages = {1457--1471},
-title = {{Quantifying the impact of impervious surface location on flood peak discharge in urban areas}},
-volume = {76},
-year = {2015}
-}
-@article{DiBaldassarre2018,
-abstract = {The expansion of reservoirs to cope with droughts and water shortages is hotly debated in many places around the world. We argue that there are two counterintuitive dynamics that should be considered in this debate: supply–demand cycles and reservoir effects. Supply–demand cycles describe instances where increasing water supply enables higher water demand, which can quickly offset the initial benefits of reservoirs. Reservoir effects refer to cases where over-reliance on reservoirs increases vulnerability, and therefore increases the potential damage caused by droughts. Here we illustrate these counterintuitive dynamics with global and local examples, and discuss policy and research implications.},
-author = {{Di Baldassarre}, Giuliano and Wanders, Niko and AghaKouchak, Amir and Kuil, Linda and Rangecroft, Sally and Veldkamp, Ted I.E. and Garcia, Margaret and van Oel, Pieter R. and Breinl, Korbinian and {Van Loon}, Anne F.},
-doi = {10.1038/s41893-018-0159-0},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {11},
-pages = {617--622},
-publisher = {Springer US},
-title = {{Water shortages worsened by reservoir effects}},
-url = {http://dx.doi.org/10.1038/s41893-018-0159-0},
-volume = {1},
-year = {2018}
-}
-@article{Tanoue2016,
-abstract = {The impacts of flooding are expected to rise due to population$\backslash$nincreases, economic growth and climate change. Hence, understanding the$\backslash$nphysical and spatiotemporal characteristics of risk drivers (hazard,$\backslash$nexposure and vulnerability) is required to develop effective flood$\backslash$nmitigation measures. Here, the long-term trend in flood vulnerability$\backslash$nwas analysed globally, calculated from the ratio of the reported flood$\backslash$nloss or damage to the modelled flood exposure using a global river and$\backslash$ninundation model. A previous study showed decreasing global flood$\backslash$nvulnerability over a shorter period using different disaster data. The$\backslash$nlong-term analysis demonstrated for the first time that flood$\backslash$nvulnerability to economic losses in upper-middle, lower-middle and$\backslash$nlow-income countries shows an inverted U-shape, as a result of the$\backslash$nbalance between economic growth and various historical socioeconomic$\backslash$nefforts to reduce damage, leading to non-significant upward or downward$\backslash$ntrends. We also show that the flood-exposed population is affected by$\backslash$nhistorical changes in population distribution, with changes in flood$\backslash$nvulnerability of up to 48.9{\%}. Both increasing and decreasing trends in$\backslash$nflood vulnerability were observed in different countries, implying that$\backslash$npopulation growth scenarios considering spatial distribution changes$\backslash$ncould affect flood risk projections.},
-author = {Tanoue, Masahiro and Hirabayashi, Yukiko and Ikeuchi, Hiroaki},
-doi = {10.1038/srep36021},
-issn = {20452322},
-journal = {Scientific Reports},
-pages = {1--9},
-publisher = {Nature Publishing Group},
-title = {{Global-scale river flood vulnerability in the last 50 years}},
-url = {http://dx.doi.org/10.1038/srep36021},
-volume = {6},
-year = {2016}
-}
-@article{Curtis2018,
-abstract = {Global maps of forest loss depict the scale and magnitude of forest disturbance,yet companies,governments,and nongovernmental organizations need to distinguish permanent conversion (i.e.,deforestation) from temporary loss from forestry or wildfire.Using satellite imagery,we developed a forest loss classification model to determine a spatial attribution of forest disturbance to the dominant drivers of land cover and land use change over the period 2001 to 2015.Our results indicate that 27{\%} of global forest loss can be attributed to deforestation through permanent land use change for commodity production.The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26{\%}),shifting agriculture (24{\%}),and wildfire (23{\%}).Despite corporate commitments,the rate of commodity-driven deforestation has not declined.To end deforestation,companies must eliminate 5 million hectares of conversion from supply chains each year.},
-author = {Curtis, Philip G. and Slay, Christy M. and Harris, Nancy L. and Tyukavina, Alexandra and Hansen, Matthew C.},
-doi = {10.1126/science.aau3445},
-
-issn = {10959203},
-journal = {Science},
-number = {6407},
-pages = {1108--1111},
-pmid = {30213911},
-title = {{Classifying drivers of global forest loss}},
-volume = {361},
-year = {2018}
-}
-
-@article{Liu2020,
-abstract = {High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones.},
-author = {Liu, Xiaoping and Huang, Yinghuai and Xu, Xiaocong and Li, Xuecao and Li, Xia and Ciais, Philippe and Lin, Peirong and Gong, Kai and Ziegler, Alan D. and Chen, Anping and Gong, Peng and Chen, Jun and Hu, Guohua and Chen, Yimin and Wang, Shaojian and Wu, Qiusheng and Huang, Kangning and Estes, Lyndon and Zeng, Zhenzhong},
-doi = {10.1038/s41893-020-0521-x},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {7},
-pages = {564--570},
-publisher = {Springer US},
-title = {{High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015}},
-url = {http://dx.doi.org/10.1038/s41893-020-0521-x},
-volume = {3},
-year = {2020}
-}
-
-
-@article{Vuuren2011,
-author = {van Vuuren, D P and Edmonds, J and Kainuma, M and Riahi, K and Thomson, A and Hibbard, K and Hurtt, G C and Kram, T and Krey, V and Lamarque, J F and Masui, T and Meinshausen, M and Nakicenovic, N and Smith, S J and Rose, S K},
-journal = {Climatic Change},
-pages = {5--31},
-title = {{The representative concentration pathways: an overview}},
-volume = {109},
-year = {2011}
-}
-
-
-@article{Eyring2016,
-abstract = {By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850-near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: How does the Earth system respond to forcing What are the origins and consequences of systematic model biases? How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.},
-author = {Eyring, Veronika and Bony, Sandrine and Meehl, Gerald A. and Senior, Catherine A. and Stevens, Bjorn and Stouffer, Ronald J. and Taylor, Karl E.},
-doi = {10.5194/gmd-9-1937-2016},
-issn = {19919603},
-journal = {Geoscientific Model Development},
-number = {5},
-pages = {1937--1958},
-title = {{Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization}},
-volume = {9},
-year = {2016}
-}
-
-@article{Almazroui2020,
-author = {Almazroui, Mansour and Saeed, Fahad and Saeed, Sajjad and {Nazrul Islam}, M. and Ismail, Muhammad and Klutse, Nana Ama Browne and Siddiqui, Muhammad Haroon},
-doi = {10.1007/s41748-020-00161-x},
-isbn = {0123456789},
-issn = {25099434},
-journal = {Earth Systems and Environment},
-keywords = {Africa,CMIP6,Future climate projections,Precipitation,Temperature},
-number = {3},
-pages = {455--475},
-publisher = {Springer International Publishing},
-title = {{Projected Change in Temperature and Precipitation Over Africa from CMIP6}},
-url = {https://doi.org/10.1007/s41748-020-00161-x},
-volume = {4},
-year = {2020}
-}
-
-@article{Pendergrass2018,
-author = {Pendergrass, Angeline G. and Knutti, Reto},
-doi = {10.1029/2018GL080298},
-issn = {19448007},
-journal = {Geophysical Research Letters},
-keywords = {climate change,climate models,distribution,extremes,observations,precipitation},
-number = {21},
-pages = {11,980--11,988},
-title = {{The Uneven Nature of Daily Precipitation and Its Change}},
-volume = {45},
-year = {2018}
-}
-
-@article{Nerem2018,
-author = {Nerem, R S and Beckley, B D and Fasullo, J T and Hamlington, B D and Masters, D and Mitchum, G T},
-doi = {10.1073/pnas.1717312115},
-issn = {0027-8424},
-journal = {Proceedings of the National Academy of Sciences},
-month = {feb},
-number = {9},
-pages = {2022--2025},
-title = {{Climate-change–driven accelerated sea-level rise detected in the altimeter era}},
-url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1717312115},
-volume = {115},
-year = {2018}
-}
-
-@article{Hirabayashi2013,
-author = {Hirabayashi, Yukiko and Mahendran, Roobavannan and Koirala, Sujan and Konoshima, Lisako and Yamazaki, Dai and Watanabe, Satoshi and Kim, Hyungjun and Kanae, Shinjiro},
-doi = {10.1038/nclimate1911},
-isbn = {doi:10.1038/nclimate1911},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {9},
-pages = {816--821},
-pmid = {2054449},
-publisher = {Nature Publishing Group},
-title = {{Global flood risk under climate change}},
-url = {http://dx.doi.org/10.1038/nclimate1911},
-volume = {3},
-year = {2013}
-}
-
-@article{Knutson2020,
-author = {Knutson, Thomas and Camargo, Suzana J. and Chan, Johnny C.L. and Emanuel, Kerry and Ho, Chang Hoi and Kossin, James and Mohapatra, Mrutyunjay and Satoh, Masaki and Sugi, Masato and Walsh, Kevin and Wu, Liguang},
-doi = {10.1175/BAMS-D-18-0194.1},
-issn = {00030007},
-journal = {Bulletin of the American Meteorological Society},
-number = {3},
-pages = {E303--E322},
-title = {{Tropical cyclones and climate change assessment part II: Projected response to anthropogenic warming}},
-volume = {101},
-year = {2020}
-}
-
-@article{Dai2013,
-author = {Dai, Aiguo},
-doi = {10.1038/nclimate1633},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {1},
-pages = {52--58},
-pmid = {5860533},
-publisher = {Nature Publishing Group},
-title = {{Increasing drought under global warming in observations and models}},
-url = {http://dx.doi.org/10.1038/nclimate1633},
-volume = {3},
-year = {2013}
-}
-
-@article{Kulp2019,
-author = {Kulp, Scott A. and Strauss, Benjamin H.},
-doi = {10.1038/s41467-019-12808-z},
-issn = {20411723},
-journal = {Nature Communications},
-number = {1},
-pmid = {31664024},
-publisher = {Springer US},
-title = {{New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding}},
-volume = {10},
-year = {2019}
-}
-
-@article{Christodoulou2018,
-author = {Christodoulou, Aris and Christidis, Panayotis and Demirel, Hande},
-doi = {10.1057/s41278-018-0114-z},
-journal = {Maritime Economics {\&} Logistics},
-keywords = {Climate change,Foreland,Hinterland,Maritime transport,Ports,Sea-level rise},
-month = {dec},
-number = {4},
-pages = {482--496},
-title = {{Sea-level rise in ports: a wider focus on impacts}},
-url = {http://link.springer.com/10.1057/s41278-018-0114-z},
-volume = {21},
-year = {2019}
-}
-
-@article{Yesudian2021,
-author = {Yesudian, Aaron N. and Dawson, Richard J.},
-doi = {10.1016/j.crm.2020.100266},
-issn = {22120963},
-journal = {Climate Risk Management},
-number = {November 2020},
-pages = {100266},
-publisher = {Elsevier B.V.},
-title = {{Global analysis of sea level rise risk to airports}},
-url = {https://doi.org/10.1016/j.crm.2020.100266},
-volume = {31},
-year = {2021}
-}
-
-@article{Forzieri2018,
-author = {Forzieri, Giovanni and Bianchi, Alessandra and Silva, Filipe Batista e. and {Marin Herrera}, Mario A. and Leblois, Antoine and Lavalle, Carlo and Aerts, Jeroen C.J.H. and Feyen, Luc},
-doi = {10.1016/j.gloenvcha.2017.11.007},
-journal = {Global Environmental Change},
-keywords = {Climate change impact,Critical infrastructures,Loss and damage,Multiple climate hazards},
-number = {April 2017},
-pages = {97--107},
-pmid = {29606806},
-publisher = {Elsevier Ltd},
-title = {{Escalating impacts of climate extremes on critical infrastructures in Europe}},
-volume = {48},
-year = {2018}
-}
-
-@article{Wong2017,
-author = {Wong, Tony E. and Keller, Klaus},
-doi = {10.1002/2017EF000607},
-issn = {23284277},
-journal = {Earth's Future},
-keywords = {10.1002/2017EF000607 and sea-level rise,coastal flooding,deep uncertainty,scenarios},
-month = {oct},
-number = {10},
-pages = {1015--1026},
-title = {{Deep Uncertainty Surrounding Coastal Flood Risk Projections: A Case Study for New Orleans}},
-url = {http://doi.wiley.com/10.1002/2017EF000607},
-volume = {5},
-year = {2017}
-}
-
-
-@techreport{Pant2019,
-address = {Oxford, United Kingdom},
-author = {Pant, Raghav and Koks, Elco E. and Paltan, Homero and Russell, Tom and Hall, Jim W.},
-institution = {Oxford Infrastructure Analytics},
-number = {August},
-pages = {1--154},
-title = {{Argentina – Transport risk analysis}},
-year = {2019}
-}
-
-@phdthesis{White1942,
-address = {Chicago},
-archivePrefix = {arXiv},
-arxivId = {arXiv:1011.1669v3},
-author = {White, Gilbert Fowler},
-booktitle = {Department of Geography Research Papers},
-eprint = {arXiv:1011.1669v3},
-isbn = {9788578110796},
-issn = {1098-6596},
-pages = {11--238},
-pmid = {25246403},
-school = {University of Chicago},
-title = {{Human Ajustment to floods: A Geographical aproach to the flood problem in the United States}},
-type = {PhD},
-year = {1942}
-}
-
-@misc{EuropeanEnvironmentalAgence2020,
-author = {{European Environmental Agence}},
-title = {{Global average near surface temperature since the pre-industrial period}},
-url = {https://www.eea.europa.eu/data-and-maps/figures/global-average-near-surface-temperature},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-@misc{GFDL,
-author = {GFDL},
-booktitle = {GFDL Model Development},
-title = {{Climate Modeling}},
-url = {https://www.gfdl.noaa.gov/climate-modeling/},
-urldate = {18-03-2021}
-}
-
-@misc{CarbonBrief2019,
-author = {CarbonBrief},
-booktitle = {Climate Modelling},
-title = {{CMIP6: the next generation of climate models explained}},
-url = {https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained},
-urldate = {18-03-2021},
-year = {2019}
-}
-
-@misc{TheIrishTimes2017,
-author = {{The Irish Times}},
-booktitle = {Climate Change},
-title = {{Climate change link to the timing of European floods}},
-url = {https://www.irishtimes.com/news/environment/climate-change-link-to-the-timing-of-european-floods-1.3182865},
-urldate = {18-03-2021},
-year = {2017}
-}
-
-@misc{NASA2020,
-author = {NASA},
-title = {{Climate Change Could Trigger More Landslides in High Mountain Asia}},
-url = {https://www.nasa.gov/feature/goddard/2020/climate-change-could-trigger-more-landslides-in-high-mountain-asia},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-
-@article{Hirabayashi2021,
-author = {Hirabayashi, Yukiko and Tanoue, Masahiro and Sasaki, Orie and Zhou, Xudong and Yamazaki, Dai},
-doi = {10.1038/s41598-021-83279-w},
-isbn = {0123456789},
-issn = {20452322},
-journal = {Scientific Reports},
-number = {1},
-pages = {1--7},
-publisher = {Nature Publishing Group UK},
-title = {{Global exposure to flooding from the new CMIP6 climate model projections}},
-url = {https://doi.org/10.1038/s41598-021-83279-w},
-volume = {11},
-year = {2021}
-}
-
-@article{owidnaturaldisasters,
- author = {Hannah Ritchie and Max Roser},
- title = {Natural Disasters},
- journal = {Our World in Data},
- year = {2014},
- note = {https://ourworldindata.org/natural-disasters}
-}
-
-@misc{EspaceMondial,
-author = {{Espace Mondial}},
-booktitle = {Maps and charrts},
-title = {{Extension of urban sprawl in selected cities, 1975-2015}},
-url = {https://espace-mondial-atlas.sciencespo.fr/en/topic-mobility/map-2C14-EN-extension-of-urban-sprawl-in-selected-cities-1975-2015.html},
-urldate = {2021-03-24}
-}
-@book{EuropeanCommissionExpertGrouponFAIRData2018,
-abstract = {In addressing the remit assigned, the FAIR Data Expert Group chose to take a holistic and systemic approach to describe the broad range of changes required to “turn FAIR data into reality”. The notions of findability, accessibility, interoperability and reusability - and the actions needed to enable them - are so deeply intertwined that it does not make sense to address them individually. Instead, this report focuses on actions needed in terms of research culture and technology to ensure data, code and other research outputs are made FAIR. Research culture and technology are two sides of one whole. Coordinated, simultaneous interventions are needed in each to enable FAIR in this broad sense. The implementation of FAIR will be supported through the European Open Science Cloud (EOSC). The federation of data infrastructure and application of standards will enable the discovery and interoperability of data. Member States should support this movement by aligning their policies and investments in relation to FAIR data and Open Science. In a wider global context, parallel initiatives such as the NIH Data Commons, the Australian Research Data Commons and also the proposed African Open Science Platform are important for the implementation of FAIR. Developments in the EOSC should align with these international movements and ensure that data are FAIR across disciplines and geographic boundaries beyond Europe. The central sections of this Report focus on existing practice in certain fields to ascertain what can be learned from those research areas that have already developed standards, international agreements and infrastructure to enable FAIR. These examples have helped to define models for FAIR Digital Objects and the essential components of a FAIR ecosystem. Naturally the main building blocks in the ecosystem are technology-based services. However, the social aspects that drive the system and enable culture change – namely skills, metrics, incentives and sustainable investment – are also addressed. The report makes a number of detailed recommendations and specifies actions for different stakeholder groups to enable the changes required. Implementing FAIR is a significant undertaking and requires changes in terms of research culture and infrastructure provision. These changes are important in the context of the European Open Science Cloud and the direction for European Commission and Member State policy, but go beyond that: FAIR requires global agreements to ensure the broadest interoperability and reusability of data - beyond disciplinary and geographic boundaries. Twenty-seven recommendations are made, which are grouped into ‘Priority' and ‘Supporting' Recommendations. The fifteen priority recommendations should be considered the initial set of changes or steps to take in order to implement FAIR. The Supporting Recommendations may be considered as following on from the Priority Recommendations, adding specifics or further detail for implementation. Each individual Recommendation is followed by a set of Actions. Each Recommendation and each Action is numbered for unambiguous referencing. The full set of Recommendations and Actions are presented in the FAIR Action Plan at the end of this report.},
-author = {{European Commission Expert Group on FAIR Data}},
-booktitle = {Final Report and Action Plan from the European Commission Expert Group on FAIR Data},
-doi = {10.2777/1524},
-file = {:Users/wusher/Documents/Mendeley Desktop/European Commission Expert Group on FAIR Data - 2018 - Turning FAIR into reality.pdf:pdf},
-isbn = {9789279965470},
-keywords = {DMP (Data Management Plan),EOSC (European Open Science Cloud),European politics,FAIR principle (Findable Accessible Interoperable,PGD (Plan de Gestion des Donn{\'{e}}es),Standardised data,identifiants p{\'{e}}rennes,m{\'{e}}tadonn{\'{e}}es,politique europ{\'{e}}enne},
-pages = {78},
-title = {{Turning FAIR into reality}},
-year = {2018}
-}
-@article{Ramos2020,
-abstract = {蚯蚓仿生机器人},
-author = {Ramos, Eunice Pereira and Howells, Mark and Sridharan, Vignesh and Engstr{\"{o}}m, Rebecka Ericsdotter and Taliotis, Constantinos and Mentis, Dimitris and Gardumi, Francesco and de Strasser, Lucia and Pappis, Ioannis and {Pe{\~{n}}a Balderrama}, Gabriela and Almulla, Youssef and Beltramo, Agnese and {Ramirez Gomez}, Camilo and Sundin, Caroline and Alfstad, Thomas and Lipponen, Annukka and Zepeda, Eduardo and Niet, Taco and Quir{\'{o}}s-Tort{\'{o}}s, Jairo and Angulo-Paniagua, Jam and Shivakumar, Abhishek and Ulloa, Silvia and Rogner, Holger},
-doi = {10.1088/1748-9326/abd34f},
-file = {:Users/wusher/Documents/Mendeley Desktop/Ramos et al. - 2020 - The Climate, Land, Energy, and Water systems (CLEWs) framework a retrospective of activities and advances to 2019.pdf:pdf},
-issn = {1748-9326},
-journal = {Environmental Research Letters},
-month = {dec},
-pages = {0--31},
-title = {{The Climate, Land, Energy, and Water systems (CLEWs) framework: a retrospective of activities and advances to 2019}},
-url = {https://iopscience.iop.org/article/10.1088/1748-9326/abd34f},
-volume = {2},
-year = {2020}
-}
-@article{Flyvbjerg2021,
-abstract = {Scale-up is the process of growing a venture in size. The paper identifies modularity and speed as keys to successful scale-up. On that basis four types of scale-up are identified: Smart, dumb, forced, and fumbled. Smart scale-up combines modularity and speed. Dumb scale-up is bespoke and slow, and very common. The paper presents examples of each type of scale-up, explaining why they were successful or not. Whether you are a small startup or Elon Musk trying to grow Tesla and SpaceX or Jeff Bezos scaling up Amazon – or you are the US, UK, Chinese, or other government trying to increase power production, expand your infrastructure, or make your health, education, and social services work better – modularity and speed are the answer to effective delivery, or so the paper argues. How well you deal with modularity and speed decides whether your efforts succeed or fail. Most ventures, existing or planned, are neither fully smart nor fully dumb, but have elements of both. Successful organizations work to tip the balance towards smart by (a) introducing elements of smart scale-up into existing ventures and (b) starting new, fully smart-scaled ventures, to make themselves less dumb and ever smarter},
-author = {Flyvbjerg, By Bent},
-file = {:Users/wusher/Documents/Mendeley Desktop/Flyvbjerg - 2021 - Four Ways to Scale Up Smart , Dumb , Forced , and Fumbled.pdf:pdf},
-number = {January},
-pages = {1--37},
-title = {{Four Ways to Scale Up : Smart , Dumb , Forced , and Fumbled}},
-year = {2021}
-}
-
-@article{Hallegatte2012,
-abstract = {The impact of climate change on economic losses from tropical cyclones is a major concern. New research shows that — like changes in population and assets — climate change may double global losses from hurricanes.},
-author = {Hallegatte, St{\'{e}}phane},
-doi = {10.1038/nclimate1427},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {3},
-pages = {148--149},
-publisher = {Nature Publishing Group},
-title = {{Economics: The rising costs of hurricanes}},
-url = {http://dx.doi.org/10.1038/nclimate1427},
-volume = {2},
-year = {2012}
-}
-
-@article{Vitousek2017,
-author = {Vitousek, S and Barnard, P L and Fletcher, C H and Frazer, N and Erikson, L and Storlazzi, C D},
-journal = {Scientific Reports},
-number = {1399},
-pages = {1--9},
-title = {{Doubling of coastal flooding frequency within decades due to sea-level rise}},
-volume = {7},
-year = {2017}
-}
-
-@article{Elsner2008,
-abstract = {Atlantic tropical cyclones are getting stronger on average, with a 30-year trend that has been related to an increase in ocean temperatures over the Atlantic Ocean and elsewhere. Over the rest of the tropics, however, possible trends in tropical cyclone intensity are less obvious, owing to the unreliability and incompleteness of the observational record and to a restricted focus, in previous trend analyses, on changes in average intensity. Here we overcome these two limitations by examining trends in the upper quantiles of per-cyclone maximum wind speeds (that is, the maximum intensities that cyclones achieve during their lifetimes), estimated from homogeneous data derived from an archive of satellite records. We find significant upward trends for wind speed quantiles above the 70th percentile, with trends as high as 0.3 +/- 0.09 m s(-1) yr(-1) (s.e.) for the strongest cyclones. We note separate upward trends in the estimated lifetime-maximum wind speeds of the very strongest tropical cyclones (99th percentile) over each ocean basin, with the largest increase at this quantile occurring over the North Atlantic, although not all basins show statistically significant increases. Our results are qualitatively consistent with the hypothesis that as the seas warm, the ocean has more energy to convert to tropical cyclone wind.},
-author = {Elsner, James B and Kossin, James P and Jagger, Thomas H},
-doi = {10.1038/nature07234},
-isbn = {1476-4687 (Electronic){\$}\backslash{\$}n0028-0836 (Linking)},
-issn = {14764687},
-journal = {Nature},
-number = {7209},
-pages = {92--95},
-pmid = {18769438},
-title = {{The increasing intensity of the strongest tropical cyclones}},
-volume = {455},
-year = {2008}
-}
-
-
-@article{Bloschl2019,
-abstract = {Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management.},
-author = {Bl{\"{o}}schl, G{\"{u}}nter and Hall, Julia and Viglione, Alberto and Perdig{\~{a}}o, Rui A.P. and Parajka, Juraj and Merz, Bruno and Lun, David and Arheimer, Berit and Aronica, Giuseppe T. and Bilibashi, Ardian and Boh{\'{a}}{\v{c}}, Miloň and Bonacci, Ognjen and Borga, Marco and {\v{C}}anjevac, Ivan and Castellarin, Attilio and Chirico, Giovanni B. and Claps, Pierluigi and Frolova, Natalia and Ganora, Daniele and Gorbachova, Liudmyla and G{\"{u}}l, Ali and Hannaford, Jamie and Harrigan, Shaun and Kireeva, Maria and Kiss, Andrea and Kjeldsen, Thomas R. and Kohnov{\'{a}}, Silvia and Koskela, Jarkko J. and Ledvinka, Ondrej and Macdonald, Neil and Mavrova-Guirguinova, Maria and Mediero, Luis and Merz, Ralf and Molnar, Peter and Montanari, Alberto and Murphy, Conor and Osuch, Marzena and Ovcharuk, Valeryia and Radevski, Ivan and Salinas, Jos{\'{e}} L. and Sauquet, Eric and {\v{S}}raj, Mojca and Szolgay, Jan and Volpi, Elena and Wilson, Donna and Zaimi, Klodian and {\v{Z}}ivkovi{\'{c}}, Nenad},
-doi = {10.1038/s41586-019-1495-6},
-issn = {14764687},
-journal = {Nature},
-number = {7772},
-pages = {108--111},
-pmid = {31462777},
-publisher = {Springer US},
-title = {{Changing climate both increases and decreases European river floods}},
-url = {http://dx.doi.org/10.1038/s41586-019-1495-6},
-volume = {573},
-year = {2019}
-}
-@article{Lehmann2019,
-abstract = {Deforestation of steep slopes may temporarily reduce evapotranspiration and lessen root reinforcement thus potentially enhancing landslide susceptibility. Quantifying the effects of deforestation and associated perturbations on landslide characteristics remains a challenge, especially for predictions in remote areas with limited information. We applied the STEP-TRAMM model that uses publicly available climatic and landscape information to assess effects of forest alteration on hydro-mechanical processes. The model considers two types of forest alterations: (i) removal of root reinforcement following permanent forest conversion, and (ii) time dependent root decay and regrowth following clear-cut timber harvesting. The model was applied to four study areas in different climatic regions (New Zealand, Oregon, Sumatra and Cambodia). We compared model predictions of landslide metrics with satellite-imaging of landslides following deforestation. Although we observe a higher propensity and larger landslides in deforested areas, effects were sensitive to deforestation practices and patterns. The largest increase in landslide area was associated with large and interconnected deforested tracts within a few years after deforestation as determined by competition between root decay and forest regrowth. For patchy small-scale forest conversion, the landslide areas were smaller but could occur many years after deforestation ({\textgreater} 10 years). The modeling framework offers ability to evaluate forest alteration scenarios through their potential impact on landslide hazard in specific regions of the landscape.},
-author = {Lehmann, Peter and von Ruette, Jonas and Or, Dani},
-doi = {10.1029/2019WR025233},
-issn = {19447973},
-journal = {Water Resources Research},
-keywords = {Landslide,Modeling,Rainfall,Remote Sensing},
-number = {11},
-pages = {9962--9976},
-title = {{Deforestation Effects on Rainfall-Induced Shallow Landslides: Remote Sensing and Physically-Based Modelling}},
-volume = {55},
-year = {2019}
-}
-@article{Du2015,
-abstract = {To date, limited attention has been paid to the role of impervious surface (IS) location in influencing flood processes. However, this topic is of tremendous significance for developing guidelines for urban planning and flood management. This study uses the Hydrologic Engineering Center's Hydrologic Modeling System (HEC-HMS) to investigate the impact of land-use change on flood processes and proposes a new index to quantify the impact of IS location on basin peak discharge. The results indicate that rapid urban expansion in the Longhua Basin, China, has increased peak discharge and flood volume by 140 and 162 {\%} over the past 30 years, respectively. The new index, named the Impervious Surface Impact Index, describes the spatially varying effects of IS increase in individual sub-basins on a basin's peak discharge. For the Longhua Basin, the index varies from 0.43 in downstream sub-basins to 5.91 in upstream sub-basins. An increase in upstream IS increases peak discharge nearly 14 times more than the same increase in downstream IS. Accordingly, the location of newly created IS can influence flood processes significantly. These findings can help to find suitable locations for urban development while mitigating the impact of land development on flood risks.},
-author = {Du, Shiqiang and Shi, Peijun and {Van Rompaey}, Anton and Wen, Jiahong},
-doi = {10.1007/s11069-014-1463-2},
-issn = {15730840},
-journal = {Natural Hazards},
-keywords = {Flood mitigation,Flood risk,Impervious surface,Low-impact development,Peak flow},
-number = {3},
-pages = {1457--1471},
-title = {{Quantifying the impact of impervious surface location on flood peak discharge in urban areas}},
-volume = {76},
-year = {2015}
-}
-@article{DiBaldassarre2018,
-abstract = {The expansion of reservoirs to cope with droughts and water shortages is hotly debated in many places around the world. We argue that there are two counterintuitive dynamics that should be considered in this debate: supply–demand cycles and reservoir effects. Supply–demand cycles describe instances where increasing water supply enables higher water demand, which can quickly offset the initial benefits of reservoirs. Reservoir effects refer to cases where over-reliance on reservoirs increases vulnerability, and therefore increases the potential damage caused by droughts. Here we illustrate these counterintuitive dynamics with global and local examples, and discuss policy and research implications.},
-author = {{Di Baldassarre}, Giuliano and Wanders, Niko and AghaKouchak, Amir and Kuil, Linda and Rangecroft, Sally and Veldkamp, Ted I.E. and Garcia, Margaret and van Oel, Pieter R. and Breinl, Korbinian and {Van Loon}, Anne F.},
-doi = {10.1038/s41893-018-0159-0},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {11},
-pages = {617--622},
-publisher = {Springer US},
-title = {{Water shortages worsened by reservoir effects}},
-url = {http://dx.doi.org/10.1038/s41893-018-0159-0},
-volume = {1},
-year = {2018}
-}
-@article{Tanoue2016,
-abstract = {The impacts of flooding are expected to rise due to population$\backslash$nincreases, economic growth and climate change. Hence, understanding the$\backslash$nphysical and spatiotemporal characteristics of risk drivers (hazard,$\backslash$nexposure and vulnerability) is required to develop effective flood$\backslash$nmitigation measures. Here, the long-term trend in flood vulnerability$\backslash$nwas analysed globally, calculated from the ratio of the reported flood$\backslash$nloss or damage to the modelled flood exposure using a global river and$\backslash$ninundation model. A previous study showed decreasing global flood$\backslash$nvulnerability over a shorter period using different disaster data. The$\backslash$nlong-term analysis demonstrated for the first time that flood$\backslash$nvulnerability to economic losses in upper-middle, lower-middle and$\backslash$nlow-income countries shows an inverted U-shape, as a result of the$\backslash$nbalance between economic growth and various historical socioeconomic$\backslash$nefforts to reduce damage, leading to non-significant upward or downward$\backslash$ntrends. We also show that the flood-exposed population is affected by$\backslash$nhistorical changes in population distribution, with changes in flood$\backslash$nvulnerability of up to 48.9{\%}. Both increasing and decreasing trends in$\backslash$nflood vulnerability were observed in different countries, implying that$\backslash$npopulation growth scenarios considering spatial distribution changes$\backslash$ncould affect flood risk projections.},
-author = {Tanoue, Masahiro and Hirabayashi, Yukiko and Ikeuchi, Hiroaki},
-doi = {10.1038/srep36021},
-issn = {20452322},
-journal = {Scientific Reports},
-pages = {1--9},
-publisher = {Nature Publishing Group},
-title = {{Global-scale river flood vulnerability in the last 50 years}},
-url = {http://dx.doi.org/10.1038/srep36021},
-volume = {6},
-year = {2016}
-}
-@article{Curtis2018,
-abstract = {Global maps of forest loss depict the scale and magnitude of forest disturbance,yet companies,governments,and nongovernmental organizations need to distinguish permanent conversion (i.e.,deforestation) from temporary loss from forestry or wildfire.Using satellite imagery,we developed a forest loss classification model to determine a spatial attribution of forest disturbance to the dominant drivers of land cover and land use change over the period 2001 to 2015.Our results indicate that 27{\%} of global forest loss can be attributed to deforestation through permanent land use change for commodity production.The remaining areas maintained the same land use over 15 years; in those areas, loss was attributed to forestry (26{\%}),shifting agriculture (24{\%}),and wildfire (23{\%}).Despite corporate commitments,the rate of commodity-driven deforestation has not declined.To end deforestation,companies must eliminate 5 million hectares of conversion from supply chains each year.},
-author = {Curtis, Philip G. and Slay, Christy M. and Harris, Nancy L. and Tyukavina, Alexandra and Hansen, Matthew C.},
-doi = {10.1126/science.aau3445},
-
-issn = {10959203},
-journal = {Science},
-number = {6407},
-pages = {1108--1111},
-pmid = {30213911},
-title = {{Classifying drivers of global forest loss}},
-volume = {361},
-year = {2018}
-}
-
-@article{Liu2020,
-abstract = {High-resolution global maps of annual urban land coverage provide fundamental information of global environmental change and contribute to applications related to climate mitigation and urban planning for sustainable development. Here we map global annual urban dynamics from 1985 to 2015 at a 30 m resolution using numerous surface reflectance data from Landsat satellites. We find that global urban extent has expanded by 9,687 km2 per year. This rate is four times greater than previous reputable estimates from worldwide individual cities, suggesting an unprecedented rate of global urbanization. The rate of urban expansion is notably faster than that of population growth, indicating that the urban land area already exceeds what is needed to sustain population growth. Looking ahead, using these maps in conjunction with integrated assessment models can facilitate greater understanding of the complex environmental impacts of urbanization and help urban planners avoid natural hazards; for example, by limiting new development in flood risk zones.},
-author = {Liu, Xiaoping and Huang, Yinghuai and Xu, Xiaocong and Li, Xuecao and Li, Xia and Ciais, Philippe and Lin, Peirong and Gong, Kai and Ziegler, Alan D. and Chen, Anping and Gong, Peng and Chen, Jun and Hu, Guohua and Chen, Yimin and Wang, Shaojian and Wu, Qiusheng and Huang, Kangning and Estes, Lyndon and Zeng, Zhenzhong},
-doi = {10.1038/s41893-020-0521-x},
-issn = {23989629},
-journal = {Nature Sustainability},
-number = {7},
-pages = {564--570},
-publisher = {Springer US},
-title = {{High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015}},
-url = {http://dx.doi.org/10.1038/s41893-020-0521-x},
-volume = {3},
-year = {2020}
-}
-
-
-@article{Vuuren2011,
-author = {van Vuuren, D P and Edmonds, J and Kainuma, M and Riahi, K and Thomson, A and Hibbard, K and Hurtt, G C and Kram, T and Krey, V and Lamarque, J F and Masui, T and Meinshausen, M and Nakicenovic, N and Smith, S J and Rose, S K},
-journal = {Climatic Change},
-pages = {5--31},
-title = {{The representative concentration pathways: an overview}},
-volume = {109},
-year = {2011}
-}
-
-
-@article{Eyring2016,
-abstract = {By coordinating the design and distribution of global climate model simulations of the past, current, and future climate, the Coupled Model Intercomparison Project (CMIP) has become one of the foundational elements of climate science. However, the need to address an ever-expanding range of scientific questions arising from more and more research communities has made it necessary to revise the organization of CMIP. After a long and wide community consultation, a new and more federated structure has been put in place. It consists of three major elements: (1) a handful of common experiments, the DECK (Diagnostic, Evaluation and Characterization of Klima) and CMIP historical simulations (1850-near present) that will maintain continuity and help document basic characteristics of models across different phases of CMIP; (2) common standards, coordination, infrastructure, and documentation that will facilitate the distribution of model outputs and the characterization of the model ensemble; and (3) an ensemble of CMIP-Endorsed Model Intercomparison Projects (MIPs) that will be specific to a particular phase of CMIP (now CMIP6) and that will build on the DECK and CMIP historical simulations to address a large range of specific questions and fill the scientific gaps of the previous CMIP phases. The DECK and CMIP historical simulations, together with the use of CMIP data standards, will be the entry cards for models participating in CMIP. Participation in CMIP6-Endorsed MIPs by individual modelling groups will be at their own discretion and will depend on their scientific interests and priorities. With the Grand Science Challenges of the World Climate Research Programme (WCRP) as its scientific backdrop, CMIP6 will address three broad questions: How does the Earth system respond to forcing What are the origins and consequences of systematic model biases? How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? This CMIP6 overview paper presents the background and rationale for the new structure of CMIP, provides a detailed description of the DECK and CMIP6 historical simulations, and includes a brief introduction to the 21 CMIP6-Endorsed MIPs.},
-author = {Eyring, Veronika and Bony, Sandrine and Meehl, Gerald A. and Senior, Catherine A. and Stevens, Bjorn and Stouffer, Ronald J. and Taylor, Karl E.},
-doi = {10.5194/gmd-9-1937-2016},
-issn = {19919603},
-journal = {Geoscientific Model Development},
-number = {5},
-pages = {1937--1958},
-title = {{Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization}},
-volume = {9},
-year = {2016}
-}
-
-@article{Almazroui2020,
-author = {Almazroui, Mansour and Saeed, Fahad and Saeed, Sajjad and {Nazrul Islam}, M. and Ismail, Muhammad and Klutse, Nana Ama Browne and Siddiqui, Muhammad Haroon},
-doi = {10.1007/s41748-020-00161-x},
-isbn = {0123456789},
-issn = {25099434},
-journal = {Earth Systems and Environment},
-keywords = {Africa,CMIP6,Future climate projections,Precipitation,Temperature},
-number = {3},
-pages = {455--475},
-publisher = {Springer International Publishing},
-title = {{Projected Change in Temperature and Precipitation Over Africa from CMIP6}},
-url = {https://doi.org/10.1007/s41748-020-00161-x},
-volume = {4},
-year = {2020}
-}
-
-@article{Pendergrass2018,
-author = {Pendergrass, Angeline G. and Knutti, Reto},
-doi = {10.1029/2018GL080298},
-issn = {19448007},
-journal = {Geophysical Research Letters},
-keywords = {climate change,climate models,distribution,extremes,observations,precipitation},
-number = {21},
-pages = {11,980--11,988},
-title = {{The Uneven Nature of Daily Precipitation and Its Change}},
-volume = {45},
-year = {2018}
-}
-
-@article{Nerem2018,
-author = {Nerem, R S and Beckley, B D and Fasullo, J T and Hamlington, B D and Masters, D and Mitchum, G T},
-doi = {10.1073/pnas.1717312115},
-issn = {0027-8424},
-journal = {Proceedings of the National Academy of Sciences},
-month = {feb},
-number = {9},
-pages = {2022--2025},
-title = {{Climate-change–driven accelerated sea-level rise detected in the altimeter era}},
-url = {http://www.pnas.org/lookup/doi/10.1073/pnas.1717312115},
-volume = {115},
-year = {2018}
-}
-
-@article{Hirabayashi2013,
-author = {Hirabayashi, Yukiko and Mahendran, Roobavannan and Koirala, Sujan and Konoshima, Lisako and Yamazaki, Dai and Watanabe, Satoshi and Kim, Hyungjun and Kanae, Shinjiro},
-doi = {10.1038/nclimate1911},
-isbn = {doi:10.1038/nclimate1911},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {9},
-pages = {816--821},
-pmid = {2054449},
-publisher = {Nature Publishing Group},
-title = {{Global flood risk under climate change}},
-url = {http://dx.doi.org/10.1038/nclimate1911},
-volume = {3},
-year = {2013}
-}
-
-@article{Knutson2020,
-author = {Knutson, Thomas and Camargo, Suzana J. and Chan, Johnny C.L. and Emanuel, Kerry and Ho, Chang Hoi and Kossin, James and Mohapatra, Mrutyunjay and Satoh, Masaki and Sugi, Masato and Walsh, Kevin and Wu, Liguang},
-doi = {10.1175/BAMS-D-18-0194.1},
-issn = {00030007},
-journal = {Bulletin of the American Meteorological Society},
-number = {3},
-pages = {E303--E322},
-title = {{Tropical cyclones and climate change assessment part II: Projected response to anthropogenic warming}},
-volume = {101},
-year = {2020}
-}
-
-@article{Dai2013,
-author = {Dai, Aiguo},
-doi = {10.1038/nclimate1633},
-isbn = {1758-678X},
-issn = {1758678X},
-journal = {Nature Climate Change},
-number = {1},
-pages = {52--58},
-pmid = {5860533},
-publisher = {Nature Publishing Group},
-title = {{Increasing drought under global warming in observations and models}},
-url = {http://dx.doi.org/10.1038/nclimate1633},
-volume = {3},
-year = {2013}
-}
-
-@article{Kulp2019,
-author = {Kulp, Scott A. and Strauss, Benjamin H.},
-doi = {10.1038/s41467-019-12808-z},
-issn = {20411723},
-journal = {Nature Communications},
-number = {1},
-pmid = {31664024},
-publisher = {Springer US},
-title = {{New elevation data triple estimates of global vulnerability to sea-level rise and coastal flooding}},
-volume = {10},
-year = {2019}
-}
-
-@article{Christodoulou2018,
-author = {Christodoulou, Aris and Christidis, Panayotis and Demirel, Hande},
-doi = {10.1057/s41278-018-0114-z},
-journal = {Maritime Economics {\&} Logistics},
-keywords = {Climate change,Foreland,Hinterland,Maritime transport,Ports,Sea-level rise},
-month = {dec},
-number = {4},
-pages = {482--496},
-title = {{Sea-level rise in ports: a wider focus on impacts}},
-url = {http://link.springer.com/10.1057/s41278-018-0114-z},
-volume = {21},
-year = {2019}
-}
-
-@article{Yesudian2021,
-author = {Yesudian, Aaron N. and Dawson, Richard J.},
-doi = {10.1016/j.crm.2020.100266},
-issn = {22120963},
-journal = {Climate Risk Management},
-number = {November 2020},
-pages = {100266},
-publisher = {Elsevier B.V.},
-title = {{Global analysis of sea level rise risk to airports}},
-url = {https://doi.org/10.1016/j.crm.2020.100266},
-volume = {31},
-year = {2021}
-}
-
-@article{Forzieri2018,
-author = {Forzieri, Giovanni and Bianchi, Alessandra and Silva, Filipe Batista e. and {Marin Herrera}, Mario A. and Leblois, Antoine and Lavalle, Carlo and Aerts, Jeroen C.J.H. and Feyen, Luc},
-doi = {10.1016/j.gloenvcha.2017.11.007},
-journal = {Global Environmental Change},
-keywords = {Climate change impact,Critical infrastructures,Loss and damage,Multiple climate hazards},
-number = {April 2017},
-pages = {97--107},
-pmid = {29606806},
-publisher = {Elsevier Ltd},
-title = {{Escalating impacts of climate extremes on critical infrastructures in Europe}},
-volume = {48},
-year = {2018}
-}
-
-@article{Wong2017,
-author = {Wong, Tony E and Bakker, Alexander M R and Keller, Klaus},
-doi = {10.1007/s10584-017-2039-4},
-journal = {Climatic Change},
-month = {sep},
-number = {2},
-pages = {347--364},
-title = {{Impacts of Antarctic fast dynamics on sea-level projections and coastal flood defense}},
-url = {http://link.springer.com/10.1007/s10584-017-2039-4},
-volume = {144},
-year = {2017}
-}
-
-
-@techreport{Pant2019,
-address = {Oxford, United Kingdom},
-author = {Pant, Raghav and Koks, Elco E. and Paltan, Homero and Russell, Tom and Hall, Jim W.},
-institution = {Oxford Infrastructure Analytics},
-number = {August},
-pages = {1--154},
-title = {{Argentina – Transport risk analysis}},
-year = {2019}
-}
-
-@phdthesis{White1942,
-address = {Chicago},
-archivePrefix = {arXiv},
-arxivId = {arXiv:1011.1669v3},
-author = {White, Gilbert Fowler},
-booktitle = {Department of Geography Research Papers},
-eprint = {arXiv:1011.1669v3},
-isbn = {9788578110796},
-issn = {1098-6596},
-pages = {11--238},
-pmid = {25246403},
-school = {University of Chicago},
-title = {{Human Ajustment to floods: A Geographical aproach to the flood problem in the United States}},
-type = {PhD},
-year = {1942}
-}
-
-@misc{EuropeanEnvironmentalAgence2020,
-author = {{European Environmental Agence}},
-title = {{Global average near surface temperature since the pre-industrial period}},
-url = {https://www.eea.europa.eu/data-and-maps/figures/global-average-near-surface-temperature},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-@misc{GFDL,
-author = {GFDL},
-booktitle = {GFDL Model Development},
-title = {{Climate Modeling}},
-url = {https://www.gfdl.noaa.gov/climate-modeling/},
-urldate = {18-03-2021}
-}
-
-@misc{CarbonBrief2019,
-author = {CarbonBrief},
-booktitle = {Climate Modelling},
-title = {{CMIP6: the next generation of climate models explained}},
-url = {https://www.carbonbrief.org/cmip6-the-next-generation-of-climate-models-explained},
-urldate = {18-03-2021},
-year = {2019}
-}
-
-@misc{TheIrishTimes2017,
-author = {{The Irish Times}},
-booktitle = {Climate Change},
-title = {{Climate change link to the timing of European floods}},
-url = {https://www.irishtimes.com/news/environment/climate-change-link-to-the-timing-of-european-floods-1.3182865},
-urldate = {18-03-2021},
-year = {2017}
-}
-
-@misc{NASA2020,
-author = {NASA},
-title = {{Climate Change Could Trigger More Landslides in High Mountain Asia}},
-url = {https://www.nasa.gov/feature/goddard/2020/climate-change-could-trigger-more-landslides-in-high-mountain-asia},
-urldate = {2021-03-18},
-year = {2020}
-}
-
-
-@article{Hirabayashi2021,
-author = {Hirabayashi, Yukiko and Tanoue, Masahiro and Sasaki, Orie and Zhou, Xudong and Yamazaki, Dai},
-doi = {10.1038/s41598-021-83279-w},
-isbn = {0123456789},
-issn = {20452322},
-journal = {Scientific Reports},
-number = {1},
-pages = {1--7},
-publisher = {Nature Publishing Group UK},
-title = {{Global exposure to flooding from the new CMIP6 climate model projections}},
-url = {https://doi.org/10.1038/s41598-021-83279-w},
-volume = {11},
-year = {2021}
-}
-
-@article{owidnaturaldisasters,
- author = {Hannah Ritchie and Max Roser},
- title = {Natural Disasters},
- journal = {Our World in Data},
- year = {2014},
- note = {https://ourworldindata.org/natural-disasters}
-}
-
-@misc{EspaceMondial,
-author = {{Espace Mondial}},
-booktitle = {Maps and charrts},
-title = {{Extension of urban sprawl in selected cities, 1975-2015}},
-url = {https://espace-mondial-atlas.sciencespo.fr/en/topic-mobility/map-2C14-EN-extension-of-urban-sprawl-in-selected-cities-1975-2015.html},
-urldate = {2021-03-24}
-}
diff --git a/docs/hands_on_03/hands_on_3.md b/docs/hands_on_03/hands_on_3.md
deleted file mode 100644
index 8815d72..0000000
--- a/docs/hands_on_03/hands_on_3.md
+++ /dev/null
@@ -1,77 +0,0 @@
----
-title: "Hands On Exercise 3: Productions constraints by timeslice"
-keywords:
-- Constraints by timeslice
-- MUSE
-- Intermittent renewable energy sources
-authors:
-- Alexander J. M. Kell
----
-
-In this hands-on we explain how to add constraints to outputs of technologies at certain timeslices. This could either by a maximum constraint, for instance with the solar PV example mentioned in the previous lecture (lecture 2). Or, this could be a minimum constraint, where we expect a minimum amount of output by a nuclear power plant at all times.
-
-# Learning objectives
-
-- Learn how to add min/max production constraints in MUSE
-
-# Minimum timeslice
-
-Hands-on accompanying video:
-[https://youtu.be/cC00jjSQBuQ](https://youtu.be/cC00jjSQBuQ)
-
-In this tutorial we will be amending the same default example (`default.zip`) as in hands-on 2, which you can find in the following zenodo link:
-[https://zenodo.org/record/6346284#.YisfUS-l1pQ](https://zenodo.org/record/6346284#.YisfUS-l1pQ)
-
-
-Firstly, we will be imposing a minimum service factor for gasCCGT (combined cycle gas turbine) in the power sector. This is the minimum that a technology can output per timeslice.
-
-To do this, we will need to create a new csv file that specifies the minimum service factor per timeslice.
-
-An example of the file, which also contains values for `windturnine` can be seen below and in the zenodo link.
-
-
-{width=100%}
-
-**Figure 3.1:** TechnodataTimeslices.csv file for the power sector.
-
-Notice that we have to specify the following columns: `ProcessName`, `RegionName`, `Time`, `month`, `day`, `hour`, `UtilizationFactor`, `MinimumServiceFactor`.
-
-The majority of these columns are self explanatory, and correspond to the columns in other csv files - for instance, `ProcessName`, `RegionName` and `Time`. The timeslice based columns, however, are dynamic and will match the levels as defined in the toml file.
-
-The majority of these columns are self explanatory, and correspond to the columns in other csv files - for instance, ProcessName, RegionName and Time. The timeslice based columns, however, are dynamic and will match the levels as defined in the `settings.toml` file in the main `default` folder.
-
-We need to link the `TechnodataTimeslices.csv` file to the MUSE model. So to do this, we must enter into the `settings.toml` file and under the `[sectors.power]` add the line `technodata_timeslices = '{path}/technodata/power/TechnodataTimeslices.csv'` as shown below. Although we must ensure that the `TechnodataTimeslices.csv` is in the `/technodata/power/` folder of the default example.
-
-```
-[sectors.power]
-type = 'default'
-priority = 2
-dispatch_production = 'costed'
-
-technodata = '{path}/technodata/power/Technodata.csv'
-commodities_in = '{path}/technodata/power/CommIn.csv'
-commodities_out = '{path}/technodata/power/CommOut.csv'
-technodata_timeslices = '{path}/technodata/power/TechnodataTimeslices.csv'
-```
-
-Once this has been completed, we are able to run MUSE as before, with the following command:
-
-```
-python -m muse settings.toml
-```
-
-We can then view the results as before using Excel.
-
-# Maximum timeslice constraint
-
-Next, we want to ensure that the supply of windturbine does not exceed a certain value during the day. This may be because, for example, there is reduced wind during the day. We will, therefore, modify the `TechnodataTimeslices.csv` file by changing the values of UtilizationFactor. This is shown in the figure below, where we change the morning and afternoon timeslices to be 0.5, as an example.
-
-{width=100%}
-
-**Figure 3.2:** Edited TechnodataTimeslices file opened in Excel.
-
-Once this has been saved, we can run the model again (`python -m muse settings.toml`). We can then visualise our results as before.
-
-# Summary
-
-In this hands-on we have introduced the `TechnodataTimeslices.csv` file, and linked it to the `settings.toml` file. This has allowed us to vary the output of various energy technologies by their characteristics.
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+++ /dev/null
@@ -1,378 +0,0 @@
-@article{Thacker2019,
- abstract = {Infrastructure systems form the backbone of every society, providing essential services that include energy, water, waste management, transport and telecommunications. Infrastructure can also create harmful social and environmental impacts, increase vulnerability to natural disasters and leave an unsustainable burden of debt. Investment in infrastructure is at an all-time high globally, thus an ever-increasing number of decisions are being made now that will lock-in patterns of development for future generations. Although for the most part these investments are motivated by the desire to increase economic productivity and employment, we find that infrastructure either directly or indirectly influences the attainment of all of the Sustainable Development Goals (SDGs), including 72{\%} of the targets. We categorize the positive and negative effects of infrastructure and the interdependencies between infrastructure sectors. To ensure that the right infrastructure is built, policymakers need to establish long-term visions for sustainable national infrastructure systems, informed by the SDGs, and develop adaptable plans that can demonstrably deliver their vision.},
- author = {Thacker, Scott and Adshead, Daniel and Fay, Marianne and Hallegatte, St{\'{e}}phane and Harvey, Mark and Meller, Hendrik and O'Regan, Nicholas and Rozenberg, Julie and Watkins, Graham and Hall, Jim W.},
- doi = {10.1038/s41893-019-0256-8},
- isbn = {4189301902568},
- issn = {23989629},
- journal = {Nature Sustainability},
- number = {4},
- pages = {324--331},
- publisher = {Springer US},
- title = {{Infrastructure for sustainable development}},
- url = {http://dx.doi.org/10.1038/s41893-019-0256-8},
- volume = {2},
- year = {2019},
- Bdsk-Url-1 = {http://dx.doi.org/10.1038/s41893-019-0256-8}}
-
-@techreport{Dulac2013,
- abstract = {Estimating road and railway infrastructure capacity and costs to 2050 INTERNATIONAL ENERGY AGENCY The International Energy Agency (IEA), an autonomous agency, was established in November 1974. Its primary mandate was -- and is -- two-fold: to promote energy security amongst its member countries through collective response to physical disruptions in oil supply, and provide authoritative research and analysis on ways to ensure reliable, affordable and clean energy for its 28 member countries and beyond. The IEA carries out a comprehensive programme of energy co-operation among its member countries, each of which is obliged to hold oil stocks equivalent to 90 days of its net imports. The Agency's aims include the following objectives: n Secure member countries' access to reliable and ample supplies of all forms of energy; in particular, through maintaining effective emergency response capabilities in case of oil supply disruptions.},
- address = {Paris, France},
- author = {Dulac, John},
- booktitle = {International Energy Agency},
- institution = {International Energy Agency},
- pages = {54},
- title = {{Global land transport infrastructure requirements - Estimating road and railway infrastructure capacity and costs to 2050}},
- year = {2013}}
-
-@book{Rozenberg2019,
- author = {Rozenberg, Julie and Fay, Marianne},
- doi = {10.1596/978-1-4648-1363-4},
- isbn = {978-1-4648-1363-4},
- month = {feb},
- publisher = {Washington, DC: World Bank},
- title = {{Beyond the Gap: How Countries Can Afford the Infrastructure They Need while Protecting the Planet}},
- url = {http://hdl.handle.net/10986/31291},
- year = {2019},
- Bdsk-Url-1 = {http://hdl.handle.net/10986/31291},
- Bdsk-Url-2 = {https://doi.org/10.1596/978-1-4648-1363-4}}
-
-@article{ONeill2014,
- abstract = {The new scenario framework for climate change research envisions combining pathways of future radiative forcing and their associated climate changes with alternative pathways of socioeconomic development in order to carry out research on climate change impacts, adaptation, and mitigation. Here we propose a conceptual framework for how to define and develop a set of Shared Socioeconomic Pathways (SSPs) for use within the scenario framework. We define SSPs as reference pathways describing plausible alternative trends in the evolution of society and ecosystems over a century timescale, in the absence of climate change or climate policies. We introduce the concept of a space of challenges to adaptation and to mitigation that should be spanned by the SSPs, and discuss how particular trends in social, economic, and environmental development could be combined to produce such outcomes. A comparison to the narratives from the scenarios developed in the Special Report on Emissions Scenarios (SRES) illustrates how a starting point for developing SSPs can be defined. We suggest initial development of a set of basic SSPs that could then be extended to meet more specific purposes, and envision a process of application of basic and extended SSPs that would be iterative and potentially lead to modification of the original SSPs themselves. {\textcopyright} The Author(s) 2013.},
- author = {O'Neill, Brian C. and Kriegler, Elmar and Riahi, Keywan and Ebi, Kristie L. and Hallegatte, Stephane and Carter, Timothy R. and Mathur, Ritu and van Vuuren, Detlef P.},
- doi = {10.1007/s10584-013-0905-2},
- issn = {01650009},
- journal = {Climatic Change},
- number = {3},
- pages = {387--400},
- title = {{A new scenario framework for climate change research: The concept of shared socioeconomic pathways}},
- volume = {122},
- year = {2014},
- Bdsk-Url-1 = {https://doi.org/10.1007/s10584-013-0905-2}}
-
-@article{Bauer2017,
- abstract = {Energy is crucial for supporting basic human needs, development and well-being. The future evolution of the scale and character of the energy system will be fundamentally shaped by socioeconomic conditions and drivers, available energy resources, technologies of energy supply and transformation, and end-use energy demand. However, because energy-related activities are significant sources of greenhouse gas (GHG) emissions and other environmental and social externalities, energy system development will also be influenced by social acceptance and strategic policy choices. All of these uncertainties have important implications for many aspects of economic and environmental sustainability, and climate change in particular. In the Shared-Socioeconomic Pathway (SSP) framework these uncertainties are structured into five narratives, arranged according to the challenges to climate change mitigation and adaptation. In this study we explore future energy sector developments across the five SSPs using Integrated Assessment Models (IAMs), and we also provide summary output and analysis for selected scenarios of global emissions mitigation policies. The mitigation challenge strongly corresponds with global baseline energy sector growth over the 21st century, which varies between 40{\%} and 230{\%} depending on final energy consumer behavior, technological improvements, resource availability and policies. The future baseline CO2-emission range is even larger, as the most energy-intensive SSP also incorporates a comparatively high share of carbon-intensive fossil fuels, and vice versa. Inter-regional disparities in the SSPs are consistent with the underlying socioeconomic assumptions; these differences are particularly strong in the SSPs with large adaptation challenges, which have little inter-regional convergence in long-term income and final energy demand levels. The scenarios presented do not include feedbacks of climate change on energy sector development. The energy sector SSPs with and without emissions mitigation policies are introduced and analyzed here in order to contribute to future research in climate sciences, mitigation analysis, and studies on impacts, adaptation and vulnerability.},
- author = {Bauer, Nico and Calvin, Katherine and Emmerling, Johannes and Fricko, Oliver and Fujimori, Shinichiro and Hilaire, J{\'{e}}r{\^{o}}me and Eom, Jiyong and Krey, Volker and Kriegler, Elmar and Mouratiadou, Ioanna and {Sytze de Boer}, Harmen and van den Berg, Maarten and Carrara, Samuel and Daioglou, Vassilis and Drouet, Laurent and Edmonds, James E. and Gernaat, David and Havlik, Petr and Johnson, Nils and Klein, David and Kyle, Page and Marangoni, Giacomo and Masui, Toshihiko and Pietzcker, Robert C. and Strubegger, Manfred and Wise, Marshall and Riahi, Keywan and van Vuuren, Detlef P.},
- doi = {10.1016/j.gloenvcha.2016.07.006},
- issn = {09593780},
- journal = {Global Environmental Change},
- keywords = {Energy demand,Energy resources,Energy supply,Energy system,Integrated Assessment Models (IAMs),Shared Socio-economic Pathways (SSPs)},
- pages = {316--330},
- publisher = {Elsevier Ltd},
- title = {{Shared Socio-Economic Pathways of the Energy Sector -- Quantifying the Narratives}},
- url = {http://dx.doi.org/10.1016/j.gloenvcha.2016.07.006},
- volume = {42},
- year = {2017},
- Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.gloenvcha.2016.07.006}}
-
-@article{ONeill2017,
- abstract = {Long-term scenarios play an important role in research on global environmental change. The climate change research community is developing new scenarios integrating future changes in climate and society to investigate climate impacts as well as options for mitigation and adaptation. One component of these new scenarios is a set of alternative futures of societal development known as the shared socioeconomic pathways (SSPs). The conceptual framework for the design and use of the SSPs calls for the development of global pathways describing the future evolution of key aspects of society that would together imply a range of challenges for mitigating and adapting to climate change. Here we present one component of these pathways: the SSP narratives, a set of five qualitative descriptions of future changes in demographics, human development, economy and lifestyle, policies and institutions, technology, and environment and natural resources. We describe the methods used to develop the narratives as well as how these pathways are hypothesized to produce particular combinations of challenges to mitigation and adaptation. Development of the narratives drew on expert opinion to (1) identify key determinants of these challenges that were essential to incorporate in the narratives and (2) combine these elements in the narratives in a manner consistent with scholarship on their inter-relationships. The narratives are intended as a description of plausible future conditions at the level of large world regions that can serve as a basis for integrated scenarios of emissions and land use, as well as climate impact, adaptation and vulnerability analyses.},
- author = {O'Neill, Brian C. and Kriegler, Elmar and Ebi, Kristie L. and Kemp-Benedict, Eric and Riahi, Keywan and Rothman, Dale S. and van Ruijven, Bas J. and van Vuuren, Detlef P. and Birkmann, Joern and Kok, Kasper and Levy, Marc and Solecki, William},
- doi = {10.1016/j.gloenvcha.2015.01.004},
- file = {:Users/Jasper/Documenten/DPhil{\_}literature/Climate{\_}economic{\_}projections/1-s2.0-S0959378015000060-main.pdf:pdf},
- issn = {09593780},
- journal = {Global Environmental Change},
- keywords = {Adaptation,Climate change,Mitigation,Narratives,Scenarios,Shared socioeconomic pathways},
- pages = {169--180},
- publisher = {Elsevier Ltd},
- title = {{The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century}},
- url = {http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004},
- volume = {42},
- year = {2017},
- Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.gloenvcha.2015.01.004}}
-
-@article{Gao2020,
- abstract = {Urban land expansion is one of the most visible, irreversible, and rapid types of land cover/land use change in contemporary human history, and is a key driver for many environmental and societal changes across scales. Yet spatial projections of how much and where it may occur are often limited to short-term futures and small geographic areas. Here we produce a first empirically-grounded set of global, spatial urban land projections over the 21st century. We use a data-science approach exploiting 15 diverse datasets, including a newly available 40-year global time series of fine-spatial-resolution remote sensing observations. We find the global total amount of urban land could increase by a factor of 1.8--5.9, and the per capita amount by a factor of 1.1--4.9, across different socioeconomic scenarios over the century. Though the fastest urban land expansion occurs in Africa and Asia, the developed world experiences a similarly large amount of new development.},
- author = {Gao, Jing and O'Neill, Brian C.},
- doi = {10.1038/s41467-020-15788-7},
- issn = {20411723},
- journal = {Nature Communications},
- number = {1},
- pages = {1--12},
- pmid = {32385275},
- publisher = {Springer US},
- title = {{Mapping global urban land for the 21st century with data-driven simulations and Shared Socioeconomic Pathways}},
- volume = {11},
- year = {2020},
- Bdsk-Url-1 = {https://doi.org/10.1038/s41467-020-15788-7}}
-
-@article{Winsemius2016,
- author = {Winsemius, Hessel C. and Aerts, Jeroen C.J.H. and {Van Beek}, Ludovicus P.H. and Bierkens, Marc F.P. and Bouwman, Arno and Jongman, Brenden and Kwadijk, Jaap C.J. and Ligtvoet, Willem and Lucas, Paul L. and {Van Vuuren}, Detlef P. and Ward, Philip J.},
- doi = {10.1038/nclimate2893},
- isbn = {1758-678X$\backslash$r1758-6798},
- issn = {17586798},
- journal = {Nature Climate Change},
- mendeley-groups = {Flood risk},
- number = {4},
- pages = {381--385},
- pmid = {9404965},
- title = {{Global drivers of future river flood risk}},
- volume = {6},
- year = {2016},
- Bdsk-Url-1 = {https://doi.org/10.1038/nclimate2893}}
-
-@article{Zarfl2015,
- author = {Zarfl, Christiane and Lumsdon, Alexander E. and Berlekamp, J{\"{u}}rgen and Tydecks, Laura and Tockner, Klement},
- doi = {10.1007/s00027-014-0377-0},
- issn = {14209055},
- journal = {Aquatic Sciences},
- keywords = {Biodiversity,Climate change,Energy,River management,Sustainability},
- number = {1},
- pages = {161--170},
- title = {{A global boom in hydropower dam construction}},
- volume = {77},
- year = {2015},
- Bdsk-Url-1 = {https://doi.org/10.1007/s00027-014-0377-0}}
-
-@article{Conway2017,
- author = {Conway, Declan and Dalin, Carole and Landman, Willem A. and Osborn, Timothy J.},
- doi = {10.1038/s41560-017-0037-4},
- isbn = {4156001700374},
- issn = {20587546},
- journal = {Nature Energy},
- number = {12},
- pages = {946--953},
- publisher = {Springer US},
- title = {{Hydropower plans in eastern and southern Africa increase risk of concurrent climate-related electricity supply disruption}},
- url = {http://dx.doi.org/10.1038/s41560-017-0037-4},
- volume = {2},
- year = {2017},
- Bdsk-Url-1 = {http://dx.doi.org/10.1038/s41560-017-0037-4}}
-
-@article{Hanson2020,
- author = {Hanson, Susan E. and Nicholls, Robert J.},
- doi = {10.1029/2020EF001543},
- issn = {2328-4277},
- journal = {Earth's Future},
- month = {aug},
- number = {8},
- title = {{Demand for Ports to 2050: Climate Policy, Growing Trade and the Impacts of Sea‐Level Rise}},
- url = {https://onlinelibrary.wiley.com/doi/10.1029/2020EF001543},
- volume = {8},
- year = {2020},
- Bdsk-Url-1 = {https://onlinelibrary.wiley.com/doi/10.1029/2020EF001543},
- Bdsk-Url-2 = {https://doi.org/10.1029/2020EF001543}}
-
-@book{ITF2019,
- author = {ITF},
- doi = {10.1787/transp_outlook-en-2019-en},
- isbn = {9789282103883},
- month = {may},
- publisher = {OECD},
- series = {ITF Transport Outlook},
- title = {{ITF Transport Outlook 2019}},
- url = {https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019{\_}transp{\_}outlook-en-2019-en},
- year = {2019},
- Bdsk-Url-1 = {https://www.oecd-ilibrary.org/transport/itf-transport-outlook-2019%7B%5C_%7Dtransp%7B%5C_%7Doutlook-en-2019-en},
- Bdsk-Url-2 = {https://doi.org/10.1787/transp_outlook-en-2019-en}}
-
-@article{Meijer2018,
- author = {Meijer, Johan R. and Huijbregts, Mark A.J. and Schotten, Kees C.G.J. and Schipper, Aafke M.},
- doi = {10.1088/1748-9326/aabd42},
- file = {:Users/Jasper/Documenten/DPhil{\_}literature/Infrastructure/Meijer{\_}etal(2018)-Global{\_}patterns{\_}road{\_}infrastructure.pdf:pdf},
- isbn = {0000000337},
- issn = {17489326},
- journal = {Environmental Research Letters},
- keywords = {SSP scenarios,global,global roads inventory project (GRIP),infrastructure,road map},
- number = {6},
- title = {{Global patterns of current and future road infrastructure}},
- volume = {13},
- year = {2018},
- Bdsk-Url-1 = {https://doi.org/10.1088/1748-9326/aabd42}}
-
-@techreport{Pant2018,
- author = {Pant, R. and Koks, E. E. and Russell, T. and Hall, J.W.},
- institution = {Oxford Infrastructure Analytics},
- number = {April},
- title = {{Transport Risk Analysis for The United Republic of Tanzania}},
- year = {2018}}
-
-@misc{Oughton2021,
- archiveprefix = {arXiv},
- author = {Edward Oughton},
- eprint = {2102.03561},
- primaryclass = {cs.CY},
- title = {Policy options for digital infrastructure strategies: A simulation model for broadband universal service in Africa},
- year = {2021}}
-
-@article{DiBaldassarre2015,
- author = {{Di Baldassarre}, Giuliano and Viglione, Alberto and Carr, Gemma and Kuil, Linda and Yan, Kun and Brandimarte, Luigia and Bl{\"{o}}schl, G{\"{u}}nter},
- doi = {10.1002/2014WR016416},
- issn = {00431397},
- journal = {Water Resources Research},
- month = {jun},
- number = {6},
- pages = {4770--4781},
- title = {{Debates-Perspectives on socio-hydrology: Capturing feedbacks between physical and social processes}},
- url = {http://doi.wiley.com/10.1002/2014WR016416},
- volume = {51},
- year = {2015},
- Bdsk-Url-1 = {http://doi.wiley.com/10.1002/2014WR016416},
- Bdsk-Url-2 = {https://doi.org/10.1002/2014WR016416}}
-
-@phdthesis{White1942,
- address = {Chicago},
- archiveprefix = {arXiv},
- arxivid = {arXiv:1011.1669v3},
- author = {White, Gilbert Fowler},
- booktitle = {Department of Geography Research Papers},
- eprint = {arXiv:1011.1669v3},
- isbn = {9788578110796},
- issn = {1098-6596},
- pages = {11--238},
- pmid = {25246403},
- school = {University of Chicago},
- title = {{Human Ajustment to floods: A Geographical aproach to the flood problem in the United States}},
- type = {PhD},
- year = {1942}}
-
-@misc{McKinsey&Company2020,
- author = {{McKinsey {\&} Company}},
- title = {{How COVID-19 has pushed companies over the technology tipping point---and transformed business forever}},
- url = {https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever},
- urldate = {2021-03-20},
- year = {2020},
- Bdsk-Url-1 = {https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/how-covid-19-has-pushed-companies-over-the-technology-tipping-point-and-transformed-business-forever}}
-
-@misc{Brookings2019,
- author = {Brookings},
- booktitle = {Brookings now},
- title = {{Charts of the Week: China's Belt and Road Initiative}},
- url = {https://www.brookings.edu/blog/brookings-now/2019/05/17/charts-of-the-week-chinas-belt-and-road-initiative/},
- urldate = {20-03-2021},
- year = {2019},
- Bdsk-Url-1 = {https://www.brookings.edu/blog/brookings-now/2019/05/17/charts-of-the-week-chinas-belt-and-road-initiative/}}
-
-@book{Alam2019,
- abstract = {Page 1. Policy Research Working Paper 9057 Wider Economic Benefits of Transport Corridors Evidence from International Development Organizations Muneeza Alam Matias Herrera Dappe Martin Melecky Ran Goldblatt South Asia Region, Office of the Chief Economist {\ldots}},
- author = {Alam, Muneeza and {Herrera Dappe}, Matias and Melecky, Martin and Goldblatt, Ran},
- booktitle = {Wider Economic Benefits of Transport Corridors: Evidence from International Development Organizations},
- doi = {10.1596/1813-9450-9057},
- file = {:Users/Jasper/Documenten/Risk{\_}resilience{\_}metric/Literature/Evaluating {\_}infrastructure/Wider-Economic-Benefits-of-Transport-Corridors-Evidence-from-International-Development-Organizations.pdf:pdf},
- month = {nov},
- number = {November},
- publisher = {World Bank, Washington, DC},
- title = {{Wider Economic Benefits of Transport Corridors: Evidence from International Development Organizations}},
- url = {http://hdl.handle.net/10986/32666},
- year = {2019},
- Bdsk-Url-1 = {http://hdl.handle.net/10986/32666},
- Bdsk-Url-2 = {https://doi.org/10.1596/1813-9450-9057}}
-
-@article{WorldBank2012,
- author = {{World Bank}},
- journal = {Urban Development Series-Knowledge Papers},
- number = {1},
- pages = {8--12},
- title = {{Waste Generation}},
- volume = {3},
- year = {2012}}
-
-@techreport{GOSL2017,
- address = {Castries, Saint Lucia},
- author = {{Government of Saint Lucia}},
- file = {:Users/lena/Library/Application Support/Mendeley Desktop/Downloaded/The Government of Saint Lucia - 2017 - Third National Communication on Climate Change for Saint Lucia.pdf:pdf},
- title = {{Third National Communication on Climate Change for Saint Lucia}},
- year = {2017}}
-
-@book{Hall2016,
- address = {Cambridge},
- author = {Hall, J.W. and Tran, M. and Hickford, A. and Nicholls, R.},
- publisher = {Cambridge University Press},
- title = {{The future of national infrastructure: A systems-of-systems approach}},
- year = {2016}}
-
-@techreport{Ives2017,
- address = {Oxford, UK},
- author = {Ives, M. and Thacker, S. and Adshead, D. and Hall, J.W. and Hickford, A. and Nicholls, R.},
- institution = {Environmental Change Institute},
- title = {{A Fast Track Analysis of Infrastructure Provision in Palestine}},
- url = {https://www.itrc.org.uk/wp-content/PDFs/PalestineFTA{\_}online. pdf},
- year = {2017},
- Bdsk-Url-1 = {https://www.itrc.org.uk/wp-content/PDFs/PalestineFTA%7B%5C_%7Donline.%20pdf}}
-
-@techreport{UNFCCC2017,
- address = {Bonn},
- author = {UNFCCC},
- institution = {UNFCCC},
- title = {{C40 Infrastructure Interdependencies and Climate Risks Report}},
- url = {https://unfccc.int/sites/default/files/report{\_}c40{\_}interdependencies{\_}.pdf},
- year = {2017},
- Bdsk-Url-1 = {https://unfccc.int/sites/default/files/report%7B%5C_%7Dc40%7B%5C_%7Dinterdependencies%7B%5C_%7D.pdf}}
-
-@article{Mohee2015,
- abstract = {This article reviews the current status of waste management in Small Island Developing States (SIDS) and the challenges that are faced in solid waste management. The waste generation rates of SIDS were compared within the three geographic regions namely Caribbean SIDS, Pacific SIDS and Atlantic, Indian Ocean, Mediterranean and South China (AIMS) SIDS and with countries of the Organisation for Economic Co-Operation and Development (OECD). Only Pacific SIDS had a waste generation rate less than 1. kg/capita/day. The waste generation rates for the three SIDS regions averaged 1.29. kg/capita/day while that for OECD countries was at a mean value of 1.35. kg/capita/day. The waste compositions in the different SIDS regions were almost similar owing to comparable consumption patterns while these differed to a large extent with wastes generated in OECD countries. In SIDS, the major fraction of MSW comprised of organics (44{\%}) followed by recyclables namely paper, plastics, glass and metals (total: 43{\%}). In contrast, MSW in OECD countries consisted mainly of recyclables (43{\%}) followed by organics (37{\%}). This article also reviewed the other functional elements of the waste management systems in SIDS. Several shortcomings were noted in the process of waste collection, transfer and transport namely the fact of having outdated collection vehicles and narrow roads which are inaccessible. Among the waste management practices in SIDS, waste disposal via landfilling, illegal dumping and backyard burning were favoured most of the time at the expense of sustainable waste treatment technologies such as composting, anaerobic digestion and recycling.},
- annote = {NULL},
- author = {Mohee, R. and Mauthoor, S. and Bundhoo, Z. and Somaroo, G. and Soobhany, N. and Gunasee, S.},
- doi = {10.1016/j.wasman.2015.06.012},
- isbn = {0956-053X},
- issn = {18792456},
- journal = {Waste Management},
- keywords = {Anaerobic digestion,Composting,Recycling,Small islands developing states,Thermochemical processes,Waste management},
- pages = {539--549},
- pmid = {26116009},
- publisher = {Elsevier Ltd},
- title = {{Current status of solid waste management in small island developing states: A review}},
- volume = {43},
- year = {2015},
- Bdsk-Url-1 = {https://doi.org/10.1016/j.wasman.2015.06.012}}
-
-@article{Fuldauer2019,
- author = {Lena Fuldauer and Ives, Matthew C. and Adshead, Daniel and Thacker, Scott and Hall, Jim W.},
- doi = {10.1016/j.jclepro.2019.02.269},
- issn = {09596526},
- journal = {Journal of Cleaner Production},
- month = {jun},
- pages = {147--162},
- title = {{Participatory planning of the future of waste management in small island developing states to deliver on the Sustainable Development Goals}},
- url = {https://linkinghub.elsevier.com/retrieve/pii/S095965261930678X},
- volume = {223},
- year = {2019},
- Bdsk-Url-1 = {https://linkinghub.elsevier.com/retrieve/pii/S095965261930678X},
- Bdsk-Url-2 = {https://doi.org/10.1016/j.jclepro.2019.02.269}}
-
-@techreport{Adshead2020,
- address = {Oxford, UK},
- author = {Adshead, Daniel and Roman, O. and Thacker, S. and Felix, F. and Fuldauer, L.I.},
- institution = {University of Oxford and United Nations Office for Project Services},
- title = {{Long-term strategic infrastructure planning model for Saint Lucia}},
- year = {2020}}
-
-@techreport{terBals2014,
- address = {Willemstad, Curacao},
- annote = {Relevant information
-Population: 150,563 people 2011
-- geographic distribution: mainly Willelmstad, on SouthEast of island
-- migration: influx migrants due to demand from refinery
-- foreign born pop. increasing
-
-Abstract:
-population growth of almost 20,000 persons between 2001 and 2011 averaged 1.4 percent per year and was similar in magnitude to the population growth of the 1960s in Cura{\c{c}}ao. The mainly migration- induced population growth has had a significant impact on the composition of the population. The sex ratio, that was already low in 2001, decreased further, mainly as a result of female- dominated immigration from regional countries such as Colombia, the Dominican Republic and Jamaica, to reach a level of 84 men per 100 women in 2011. At the same time, the pace of the ageing process of the population has increased as a result of the rapidly growing number of persons aged 60 years or older. Besides, the decline in the size of the population aged 0-14 years, which has been ongoing since the 1970s, continued between 2001 and 2011.},
- author = {ter Bals, M.},
- institution = {Central Bureau of Statistics Curacao},
- isbn = {9789990419474},
- keywords = {birth rate,lemurs,population structure},
- title = {{Demography of Cura{\c{c}}ao Census 2011}},
- url = {http://www.cbs.cw/website/2011-census{\_}3226/item/demography-of- curacao-publication-series-census-2011{\_}757.html},
- year = {2014},
- Bdsk-Url-1 = {http://www.cbs.cw/website/2011-census%7B%5C_%7D3226/item/demography-of-%20curacao-publication-series-census-2011%7B%5C_%7D757.html}}
-
-@article{Fuldauer2021b,
- author = {Lena Fuldauer and Thacker, S. and Hall, J.},
- journal = {In preparation},
- title = {{Spatially assessing the impacts of climate change hazards on sustainable development to inform national adaptation}},
- year = {2021}}
-
-@article{Otto2016,
- abstract = {National Infrastructure (NI) systems (i.e. Energy, Transport, Water, Waste, ICT) provide essential services to the economy and contribute to human wellbeing. These systems have evolved over centuries, being planned and implemented piecewise, and mostly managed in isolation from one-another. The growing interconnection between these systems and the convergent challenges ahead (i.e. demographic, technological, and climate change) call for an integrated ``systems-of-systems'' approach to managing national infrastructure. Here we propose a modeling framework for the long-term (to 2100) simulation of national infrastructure (NI) system performance in a highly uncertain future. The approach is based upon the assessment of performance of infrastructure services in a wide range of possible future conditions, and a robust optimization to identify cross- sectoral strategies that ensure satisfactory infrastructure performance. We demonstrate the framework using Great Britain's NI as a case study.},
- author = {Otto, A. and Hall, J.W. and Hickford, A. and Nicholls, R. and Alderson, D. and Barr, S.},
- doi = {10.1109/JSYST.2014.2361157},
- file = {:Users/lena/Library/Application Support/Mendeley Desktop/Downloaded/Otto et al. - 2016 - A quantified system-of systems modeling framework for robust national infrastructure planning.pdf:pdf},
- isbn = {1932-8184},
- issn = {19379234},
- journal = {IEEE Systems Journal},
- keywords = {Robust Decision making,infrastructure systems,system-of-systems},
- number = {2},
- pages = {11},
- title = {{A quantified system-of systems modeling framework for robust national infrastructure planning}},
- volume = {10},
- year = {2016},
- Bdsk-Url-1 = {https://doi.org/10.1109/JSYST.2014.2361157}}
diff --git a/docs/hands_on_04/hands_on_4.md b/docs/hands_on_04/hands_on_4.md
deleted file mode 100644
index e8a42e8..0000000
--- a/docs/hands_on_04/hands_on_4.md
+++ /dev/null
@@ -1,147 +0,0 @@
----
-title: "Hands On Exercise 4: Adding a technology"
-keywords:
-- Adding a technology
-- MUSE
-authors:
-- Alexander J. M. Kell
----
-
-
-
-# Learning objectives
-
-- Learn how to add a new technology in MUSE
-
-## Addition of solar PV
-
-Hands-on accompanying video:
-[https://youtu.be/d_KlS4QL5mw](https://youtu.be/d_KlS4QL5mw)
-
-In this section, we will add solar photovoltaics to the default model. We will be starting from scratch and not continuing with the examples from hands-on 2 and 3. Therefore, to achieve this, we must modify the input files in the default example (default.zip) which can be found in the zenodo link provided below.
-
-[https://zenodo.org/record/6092287#.YgvOEy-l1pQ](https://zenodo.org/record/6092287#.YgvOEy-l1pQ)
-
-## Technodata Input
-
-We must note, before starting, that we require consistency in input and output units. For example, if capacity is in PJ, the same basis would be needed for the output files `CommIn.csv` and `CommOut.csv`. In addition, across sectors a commodity needs to maintain the same unit. In these examples, we use the unit petajoule (PJ).
-
-Next, we will edit the `CommIn.csv` file in the power sector, which specifies the commodities consumed by solar photovoltaics.
-
-The table below shows the original `CommIn.csv` version in normal text, and the added column and row in bold.
-
-{width=100%}
-
-**Figure 4.1:** Modified CommIn.csv file for the power sector
-
-We must first add a new row at the bottom of the file, to indicate the new solar photovoltaic technology:
-
-- we call this technology `solarPV`
-- place it in region `R1`
-- the data in this row is associated to the year 2020
-- the input type is fixed
-- `solarPV` consumes solar
-
-As the solar commodity has not been previously defined, we must define it by adding a column, which we will call `solar`. We fill out the entries in the solar column, ie. that neither `gasCCGT` nor `windturbine` consume solar.
-
-We repeat this process for the file: `CommOut.csv`. This file specifies the output of the technology. In our case, solar photovoltaics only output electricity. This is unlike `gasCCGT` which also outputs `CO2f`, or carbon dioxide.
-
-{width=100%}
-
-**Figure 4.2:** Modified CommOut.csv file for the power sector
-
-Similar to the the `CommIn.csv`, we create a new row, and add in the solar commodity. We must ensure that we call our new commodity and technologies the same as the previous file for MUSE to successfully run, i.e. `solar` and `solarPV`. Please note that the commodity names are case-sensitive.
-
-Please note that we use flat forward extension of the values when only one value is defined. For example, in the `CommOut.csv` we only provide data for the year 2020. Therefore for the benchmark years, 2025, 2030, 2035… we assume the data remains unchanged from 2020.
-
-The next file to modify is the `ExistingCapacity.csv` file. This file details the existing capacity of each technology, per benchmark year. For this example, we will set the existing capacity to be 0.5 for all technologies in the base year and 0 for the remaining years. Please note, that the model interpolates between benchmark years linearly.
-
-{width=100%}
-
-**Figure 4.3:** Modified ExistingCapacity.csv file for the power sector
-
-Finally, the technodata.csv contains parametrisation data for the technology, such as the cost, growth constraints, lifetime of the power plant and fuel used. The technodata file is too long for it all to be displayed here, so we will truncate the full version.
-
-Here, we will only define the parameters: `processName`, `RegionName`, `Time`, `Level`,`cap_par`, `Fuel`, `EndUse`, `Agent2` and `Agent1`
-
-We shall copy the existing parameters from the windturbine technology for the remaining parameters that can be seen in the `technodata.csv` file for brevity. You can see the full file at the zenodo link, below:
-
-[https://zenodo.org/record/6092287#.YgvOEy-l1pQ](https://zenodo.org/record/6092287#.YgvOEy-l1pQ)
-
-Again, flat forward extension is used here. Therefore, as in this example we only provide data for the benchmark year 2020, 2025 and the following benchmark years will keep the same characteristics, e.g. costs, for each benchmark year of the simulation.
-
-
-{width=100%}
-
-**Figure 4.4:** Modified Technodata.csv file for the power sector
-
-Notice that we have hidden the cells between F and T. These are the same as the `windturbine` technology, but we've changed the `cap_par` input to 30 and the `Fuel` technology to `solar`.
-
-## Global inputs
-
-Next, navigate to the input folder, found at:
-
-```
-{muse_installation_location}/src/muse/data/example/default/input
-```
-
-We must now edit each of the files found here to add the new solar commodity. The edited files can be viewed in the zenodo link below:
-
-[https://zenodo.org/record/6092287#.YgvOEy-l1pQ](https://zenodo.org/record/6092287#.YgvOEy-l1pQ)
-
-The `BaseYearExport.csv` file defines the exogenous exports for commodities. For our example we add a column to indicate that there is no export for solar. However, it is important that a column exists for our new commodity.
-
-It is noted, however, that the `BaseYearImport.csv` as well as the `BaseYearExport.csv` files are optional files to define exogenous imports and exports; all values are set to zero if they are not used.
-
-{width=100%}
-
-**Figure 4.5:** Modified BaseYearExport.csv file for the power sector
-
-The `BaseYearImport.csv` file defines the imports in the base year. Similarly to `BaseYearExport.csv`, we add a column for solar in the `BaseYearImport.csv` file. Again, we indicate that solar has no imports.
-
-{width=100%}
-
-**Figure 4.6:** Modified BaseYearImport.csv file for the power sector
-
-The `GlobalCommodities.csv` file is the file which defines the commodities. Here we give the commodities a commodity type, CO2 emissions factor and heat rate. For this file, we will add the `solar` commodity, with zero CO2 emissions factor and a heat rate of 1.
-
-
-{width=100%}
-
-**Figure 4.7:** Modified GlobalCommodities.csv file for the power sector
-
-The `projections.csv` file details the initial market prices for the commodities. The market clearing algorithm will update these throughout the simulation; however, an initial estimate is required to start the simulation. As solar irradiance as a fuel is free, we will indicate this by adding a final column.
-
-Please note that the unit row is not read by MUSE, but used as a reference for the user. The units should be consistent across all input files for MUSE; MUSE does not carry out any unit conversion.
-
-
-{width=100%}
-
-**Figure 4.8:** Modified projections.csv file for the power sector
-
-## Running our customised simulation
-
-Now we are able to run our simulation with the new solar power technology.
-
-To do this we run the same run command as previously in the anaconda command prompt:
-
-```
-python -m muse settings.toml
-```
-
-If the simulation has run successfully, you should now have a folder in the same location as your `settings.toml` file called `Results`. It must be noted, however, that if you update a value and re-run the model, the results folder will be overwritten.
-
-The next step is to visualise the results using Excel.
-
-We will use the PivotChart, similar to that shown in hands-on 1. The file to be used is the MCACapacity.csv file. For our visualisation we have selected a stacked area chart, but you are free to choose the type you like.
-
-{width=100%}
-
-**Figure 4.9:** Visualisation with new technology.
-
-The power sector now shows us the new `solarPV` technology.
-
-
-
-
-
diff --git a/docs/hands_on_05/assets/Figure_5.1.png b/docs/hands_on_05/assets/Figure_5.1.png
deleted file mode 100644
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diff --git a/docs/hands_on_05/bibliography.bib b/docs/hands_on_05/bibliography.bib
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index e69de29..0000000
diff --git a/docs/hands_on_05/hands_on_5.md b/docs/hands_on_05/hands_on_5.md
deleted file mode 100644
index 4286503..0000000
--- a/docs/hands_on_05/hands_on_5.md
+++ /dev/null
@@ -1,87 +0,0 @@
----
-title: "Hands On Exercise 5: Adding a service demand by correlation"
-keywords:
-- Service demand
-- Correlation
-- Regression functions
-authors:
-- Alexander J. M. Kell
----
-
-In hands-on 2, we added an exogenous service demand. That is, we explicitly specified what the demand would be per year.
-
-However, we may not know what the electricity demand is for each year into the future. Instead, we may conclude that our electricity demand is a function of the GDP and population of a particular region.
-
-To accommodate such scenarios, MUSE enables us to choose a regression function that estimates service demands from GDP and population, which may be more certain in your case. In this hands-on we find out how this can be done.
-
-# Learning objectives
-
-- How to add a service demand by correlation
-
-# Introduction
-
-Hands-on accompanying video:
-[https://youtu.be/_KMHRMd2QoM](https://youtu.be/_KMHRMd2QoM)
-
-For this work, we will use the default example, as before, from the MUSE repository.
-
-The full scenario files for the default example can be found at the zenodo link below.
-[https://zenodo.org/record/6092720#.YgvcMy-l1pQ](https://zenodo.org/record/6092720#.YgvcMy-l1pQ)
-
-We recommend that you download these files and save them to a location convenient to you, as we will be amending these throughout this tutorial.
-
-Similarly to before, we must amend the preset folder for this. However, we no longer require the `Residential2020Consumption.csv` and `Residential2050Consumption.csv` files. These files set the exogenous service demand for the residential sector.
-
-We must replace these files, with the following files:
-
-- A macrodrivers file. This contains the drivers of the service demand that we want to model. For this example, these will include GDP based on purchasing power parity (GDP PPP) and the population that we expect from 2010 to 2110.
-- A regression parameters file. This file will set the function type we would like to use to predict the service demand and the respective parameters of this regression file per region. We will not go into detail about the different functions that you can choose, but for more information, please refer to the documentation: [https://muse-docs.readthedocs.io/en/latest/](https://muse-docs.readthedocs.io/en/latest/)
-- A timeslice share file. This file sets how the demand is shared between timeslices.
-
-The example files for each of those just mentioned can be found in the zenodo link below.
-[https://zenodo.org/record/6092720#.YgvcMy-l1pQ](https://zenodo.org/record/6092720#.YgvcMy-l1pQ)
-
-Download these files and save them within the preset folder.
-
-Next, we must amend our TOML file to include our new way of calculating the preset service demand.
-
-## TOML file
-
-Editing the TOML file to include this can be done relatively quickly if we know the variable names. This just requires opening the TOML file in a text editor of your choice.
-
-In the second bottom section of the TOML file, you will see the following section:
-
-```
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-consumption_path= "{path}/technodata/preset/*Consumption.csv"
-```
-
-This enables us to run the model in exogenous mode, but now we would like to run the model from the files previously mentioned. This can be done by linking new variables to the new files, as follows:
-
-```
-[sectors.residential_presets]
-type = 'presets'
-priority = 0
-
-timeslice_shares_path = '{path}/technodata/preset/TimesliceSharepreset.csv'
-macrodrivers_path = '{path}/technodata/preset/Macrodrivers.csv'
-regression_path = '{path}/technodata/preset/regressionparameters.csv'
-```
-
-We've just linked the new files to MUSE.
-
-## Running and visualising our new results
-
-
-Figure 5.1, below, shows the power sector over the future horizon. We can see a significantly higher installed capacity, as the demand has increased due to the correlation of GDP PPP and population.
-
-{width=100%}
-
-**Figure 5.1:** Visualisation of the power sector
-
-# Summary
-
-In this hands-on we added a service demand by correlation. Specifically, GDP purchasing power parity and population. We saw that we could make inferences on how the demand will grow based on these using seperate files in MUSE.
-
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diff --git a/docs/hands_on_06/bibliography.bib b/docs/hands_on_06/bibliography.bib
deleted file mode 100644
index e69de29..0000000
diff --git a/docs/hands_on_06/hands_on_6.md b/docs/hands_on_06/hands_on_6.md
deleted file mode 100644
index 2fb6b43..0000000
--- a/docs/hands_on_06/hands_on_6.md
+++ /dev/null
@@ -1,70 +0,0 @@
----
-title: "Hands On Exercise 6: Adding an agent"
-keywords:
-- Agent
-- MUSE
-- Modelling
-authors:
-- Alexander J. M. Kell
----
-
-
-Now we will learn how to add a new agent to our example.
-
-# Learning objectives
-
-- How to add a new agent
-
-# Introduction
-
-Hands-on accompanying video:
-[https://youtu.be/dhZfrZ9YtuU](https://youtu.be/dhZfrZ9YtuU)
-
-In this hands-on, we will add a new agent called `A2`. This agent will be slightly different to the other agents in the `default` example, in that it will make investments based upon a mixture of levelised cost of electricity (LCOE) and equivalent annual cost (EAC). Where EAC is the is the annual cost of owning, operating, and maintaining an asset over its entire life. These two objectives will be combined by calculating a weighted sum of the two when comparing potential investment options. We will give the LCOE a relative weight value of 0.5 and the EAC a relative weight value of 0.5.
-
-We will edit the default example to add a new agent, which can be found from the following zenodo link:
-[https://zenodo.org/record/6323453#.Yh-QWi-l1pQ]( https://zenodo.org/record/6323453#.Yh-QWi-l1pQ)
-
-To achieve this, first, we must modify the Agents.csv file in the directory:
-```
-{muse_install_location}/src/muse/data/example/default/technodata/Agents.csv
-```
-
-To do this, we will add two new rows to the file. To simplify the process, we copy the data from the first two rows of agent A1, changing only the rows: `AgentShare`, `Name`, `Objective1`, `Objective2`, `ObjData1`, `ObjData2`, `DecisionMethod` and `Quantity`. The values we changed can be seen below. Notice how we edit the `AgentShare` column. This variable allows us to split the existing capacity between the two different agents. There is a set list of objectives that can be chosen from, with more information provided at the documentation: [https://muse-docs.readthedocs.io/en/latest/]( https://muse-docs.readthedocs.io/en/latest/)
-We will also need to edit the Agents.csv file to define these new AgentShares.
-
-{width=100%}
-
-**Figure 6.1:** Updated Agents.csv.
-
-Also notice that we amend the Quantity column. The reason for this is that we want to specify that Agent `A1` makes up 50% of the population, and `A2` makes up the remaining 50% of the population.
-
-We then edit all of the technodata files to split the existing capacity between the two agents by the proportions we like. As we now have two agents which take up 50% of the population each, we will split the existing capacity by 50% for each of the agents. Notice that we only require the columns `Agent2` and `Agent4` to define the retrofit agents.
-
-The new technodata file for the power sector will look like the following (we have hidden the middle columns as they remain the same):
-
-{width=100%}
-
-**Figure 6.2:** Edited power technodata file.
-
-However, remember you will have to make the same changes for the residential and gas sectors!
-
-We will now save these files and run the new simulation model using the following command in Anaconda prompt:
-
-```
-python -m muse settings.toml
-```
-
-Figure 6.3 shows us the results of these two agents. We can see a divergence between technologies invested in by the agents dependent on their objectives
-
-{width=100%}
-
-**Figure 6.3:** Visualisation of the two different agents - a) agent = A1, b) agent = A2.
-
-For all the files explored in this hands-on, please refer to the following link:
-https://zenodo.org/record/6323453#.Yh-QWi-l1pQ
-
-# Summary
-
-In this hands-on we added a new agent which had different characteristics to the original agent and saw that this led to a dramatic change in the technologies invested in.
-
diff --git a/docs/hands_on_07/assets/Figure_7.1.png b/docs/hands_on_07/assets/Figure_7.1.png
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diff --git a/docs/hands_on_07/assets/Figure_7.3.png b/docs/hands_on_07/assets/Figure_7.3.png
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diff --git a/docs/hands_on_07/assets/Figure_7.4.png b/docs/hands_on_07/assets/Figure_7.4.png
deleted file mode 100644
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diff --git a/docs/hands_on_07/hands_on_7.md b/docs/hands_on_07/hands_on_7.md
deleted file mode 100644
index 54dddce..0000000
--- a/docs/hands_on_07/hands_on_7.md
+++ /dev/null
@@ -1,73 +0,0 @@
----
-title: "Hands On Exercise 7: Adding a region"
-keywords:
-- Region
-- MUSE
-- Modelling
-authors:
-- Alexander J. M. Kell
----
-
-
-Now we will learn how to add a new region to our example.
-
-# Learning objectives
-
-- Learn how to add a new region
-
-# Introduction
-
-Hands-on accompanying video:
-[https://youtu.be/Ybj-zLH1mmg](https://youtu.be/Ybj-zLH1mmg)
-
-The next step is to add a region which we will call `R2`, however, this could equally be called `USA` or `India`. These regions do not have any energy trade. This requires us to undertake a similar process as in the previous hands-on when modifying the input simulation data. However, this time we will also have to change the `settings.toml` file to achieve this.
-
-The process to change the `settings.toml` file is relatively simple. We just have to add our new region to the regions variable, in the 4th line of the `settings.toml` file, like so:
-
-```
-regions = ["R1", "R2"]
-```
-
-The process to change the input files, however, takes a bit more time. To achieve this, there must be data for each of the sectors for the new region. This, therefore, requires the modification of every input file.
-
-Due to space constraints, we will not show you how to edit all of the files. However, you can access the modified files at the zenodo link below:
-[https://zenodo.org/record/6327789#.YiI1ri-l1pQ](https://zenodo.org/record/6327789#.YiI1ri-l1pQ)
-
-Effectively, for this example, we will copy and paste the results for each of the input files from region `R1`, and change the name of the region for the new rows to `R2`.
-
-However, as we are increasing the demand by adding a region, as well as modifying the costs of technologies, it may be the case that a higher growth in technology is required. For example, there may be no possible solution to meet demand without increasing the `windturbine` maximum allowed limit. We will therefore increase the allowed limits for `windturbine` in region `R2`.
-
-We have placed two examples as to how to edit the power sector below. Again, the edited data are highlighted in **bold**, with the original data in normal text.
-
-The following file is the modified `/technodata/power/CommIn.csv` file:
-
-{width=100%}
-
-**Figure 7.1:** Updated CommIn.csv.
-
-Whereas the following file is the modified `/technodata/power/ExistingCapacity.csv` file:
-
-{width=100%}
-
-**Figure 7.2:** Updated ExistingCapacity.csv.
-
-Below is the reduced `/technodata/power/technodata.csv` file, showing the new `windturbine` in `R2`. For this, we highlight only the elements we changed from the rows in `R1`. The rest of the elements are the same for `R1` as they are for `R2`.
-
-{width=100%}
-
-**Figure 7.3:** Updated Technodata.csv.
-
-Now, go ahead and amend all of the other input files for each of the sectors, the Agents file and the input files `BaseYearExport`, `BaseYearImport` and `Projections.csv` by copying and pasting the rows from `R1` and replacing the `RegionName` with `R2` for the new rows. All of the edited input files can be seen at the zenodo link:
-[https://zenodo.org/record/6327789#.YiI1ri-l1pQ](https://zenodo.org/record/6327789#.YiI1ri-l1pQ)
-
-Again, we will run the results using the `python -m pip muse settings.toml` in anaconda prompt, and analyse the data using excel as follows:
-
-{width=100%}
-
-**Figure 7.4:** Capacity visualisation for both regions in the power sector - a) Region = R1, b) Region = R2.
-
-
-# Summary
-
-In this hands-on we added a new fictional region with the same characteristics for both of these regions. We see that the output of the two regions in the power sector are the same. This is because the characteristics in both regions are identical.
-
diff --git a/docs/installation.ipynb b/docs/installation.ipynb
new file mode 100644
index 0000000..5594068
--- /dev/null
+++ b/docs/installation.ipynb
@@ -0,0 +1,69 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "# Installing MUSE\n",
+ "\n",
+ "This hands-on exercise will show you how to install MUSE on your computer.\n",
+ "\n",
+ "If at any point you get stuck with any of these exercises, feel free to post a question in the [MUSE Google group](https://groups.google.com/g/muse-model), or raise an issue on [Github](https://github.com/EnergySystemsModellingLab/MUSE_OS). You can also consult the full MUSE documentation [here](https://muse-os.readthedocs.io/en/v1.2.1/).\n",
+ "\n",
+ "## Creating a virtual environment\n",
+ "\n",
+ "MUSE is built as a Python package (called `muse_os`), so installing MUSE requires Python to be installed on your machine. This version of the course is based on `muse_os` version 1.2.1, which requires a Python version between 3.9 and 3.12. In this tutorial, we will show you how to set up an environment with Python 3.12 and install `muse_os` into that environment. If you're already comfortable creating Python environments and installing packages, feel free to do this however you choose. Otherwise, please read on.\n",
+ "\n",
+ "The simplest method to install Python is by downloading the [Anaconda distribution](https://www.anaconda.com/). Once installed on your machine, this will allow you to create isolated environments containing all the dependencies needed for a project, with a specific Python version.\n",
+ "\n",
+ "If you're using Anaconda, you can create and activate an environment by opening up your terminal and running the following commands:\n",
+ "\n",
+ " conda create -n muse_env python=3.12\n",
+ " conda activate muse_env\n",
+ "\n",
+ "Once you've activated the environment, run the following command to install MUSE version 1.2.1:\n",
+ "\n",
+ " python -m pip install muse_os==1.2.1\n",
+ "\n",
+ "Later (for example, when tackling future hands-on exercises in this course), you can return to this environment by simply running:\n",
+ "\n",
+ " conda activate muse_env\n",
+ "\n",
+ "\n",
+ "## Confirming the installation\n",
+ "\n",
+ "To confirm that the installation has worked, run the following in your command line:\n",
+ "\n",
+ " python -m muse --model default\n",
+ "\n",
+ "This command runs the default model that comes with MUSE. If the installation has worked correctly, you should see output text similar to [this](https://muse-os.readthedocs.io/en/v1.2.1/example-output.html).\n",
+ "\n",
+ "You should also see that a folder called `Results` has been created in your working directory. Don't worry about the contents of this folder for now, as we will be analysing these results in the next exercise.\n",
+ "\n",
+ "## Summary\n",
+ "\n",
+ "Hopefully you've now managed to install MUSE on your machine, and are ready to tackle the next hands-on exercises.\n",
+ "Interested readers can also see [here](https://muse-os.readthedocs.io/en/v1.2.1/installation/index.html) for more detailed installation instructions, including a number of alternative methods for installing MUSE.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "muse_env",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python",
+ "version": "3.12.5"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/lecture_01/Lecture_1.1.md b/docs/lecture_01/Lecture_1.1.md
index e593b4b..0eca5e4 100644
--- a/docs/lecture_01/Lecture_1.1.md
+++ b/docs/lecture_01/Lecture_1.1.md
@@ -14,14 +14,14 @@ This mini-lecture will provide an overview of how the course is structured.
# Learning objectives
-- To present the structure of the course for both the lecture content
+- To present the structure of the course for both the lecture content
and the hands-on sessions
-- To obtain an introductory insight into the content delivered within
+- To obtain an introductory insight into the content delivered within
this course.
# Introduction and structure of the course
-This course is made up of 8 lectures and 7 hands-on exercises. All the lectures have an accompanying hands-on exercise, apart from this lecture. Each of the lectures are made up of four, related mini-lectures. Whilst the lectures will give you a background to energy modelling, you will also learn how to practically build a case study using the MUSE model.
+This course is made up of 9 lectures and 9 hands-on exercises. Whilst the lectures will give you a background to energy modelling, you will also learn how to practically build a case study using the MUSE model.
To build up a good understanding of MUSE, you will be required to understand why we use energy systems modelling and the main features that make up a good energy systems model. You will learn of the trade-offs between different decisions, such as increasing data granularity versus overall model run time.
@@ -29,25 +29,26 @@ The overall lecture structure is presented below:
1. Energy systems modelling
2. Introduction to MUSE
-3. Energy demands
-4. Timeslices
-5. Technologies
-6. Sectors
-7. Agents
-8. Regions
+3. Sectors
+4. Technologies
+5. Agents
+6. Regions
+7. Timeslices
+8. Energy demands
+9. Communicating research
-First, you will have an overview of energy systems modelling. Then we will introduce you to the agent-based model, MUSE. The next lectures detail the key components that make up MUSE. Some of these, such as energy demands and timeslices are common to all energy systems models, and some are unique to MUSE's agent-based modelling type, such as regions.
+First, you will have an overview of energy systems modelling. Then we will introduce you to the agent-based model MUSE. The next lectures detail the key components that make up MUSE. Some of these, such as energy demands and timeslices are common to all energy systems models, and some are unique to MUSE's agent-based modelling type, such as regions.
The accompanying hands-on exercises are:
-1. Installing and running MUSE
-2. Adding a service demand
-3. Production constraints by timeslice
-4. Adding a new technology
-5. Adding a service demand by correlation
-6. Adding an agent
-7. Adding a region
-
-As you can see, through the hands-on exercises you will pick up the key skills needed to design an energy systems model with MUSE.
-
-
+1. Installing MUSE
+2. Running MUSE
+3. Adding a new technology
+4. Adding an agent
+5. Adding a region
+6. Modifying timeslicing
+7. Production constraints by timeslice
+8. Adding a service demand
+9. Adding a service demand by correlation
+
+Through these hands-on exercises, you will pick up the key skills needed to design your own energy systems models with MUSE.
diff --git a/docs/lecture_01/Lecture_1.2.md b/docs/lecture_01/Lecture_1.2.md
index 06785d3..934b724 100644
--- a/docs/lecture_01/Lecture_1.2.md
+++ b/docs/lecture_01/Lecture_1.2.md
@@ -1,5 +1,5 @@
---
-title: Mini-Lecture 1.2 -- Sustainable Development Goals and the global climate agenda.
+title: Mini-Lecture 1.2 -- Sustainable Development Goals and the global climate agenda.
keywords:
- Sustainable development
- Sustainable Development Goals (SDGs)
@@ -14,15 +14,15 @@ This mini-lecture will provide a background to sustainable development, the glob
## Learning objectives
-- Learn the importance of sustainable development and how it frames
+- Learn the importance of sustainable development and how it frames
the major global development agendas
-- Identify the general principles of the Sustainable Development Goals
+- Identify the general principles of the Sustainable Development Goals
# Lecture content
## Introduction
-The Sustainable Development Goals (SDGs) are a set of universal goals for every country. The SDGs are focused on ending poverty, improving quality of life and protecting the environment. These goals were agreed on in 2015, with 2030 set as the target year for their achievement. The Sustainable Development Goals aim to tackle multiple issues. Some of the goals are based around poverty, environmental protection, climate action, justice and more. The SDGs can be seen in Figure 1.2.1. These goals are designed to be thought of together, rather than in isolation. There are, therefore, many links between different goals.
+The Sustainable Development Goals (SDGs) are a set of universal goals for every country. The SDGs are focused on ending poverty, improving quality of life and protecting the environment. These goals were agreed on in 2015, with 2030 set as the target year for their achievement. The Sustainable Development Goals aim to tackle multiple issues. Some of the goals are based around poverty, environmental protection, climate action, justice and more. The SDGs can be seen in Figure 1.2.1. These goals are designed to be thought of together, rather than in isolation. There are, therefore, many links between different goals.
{width=100%}
@@ -42,13 +42,13 @@ Sustainable Development Goal 7 (SDG7) specifically focuses on the energy sector.
## Progress on SDG7: Access to Electricity and Clean Cooking
-According to the tracking SDG7 report, there has been progress in improving access to both electricity and clean cooking in recent years. This report states that electricity access increased from 83% in 2010 to 90% in 2018, whilst clean cooking increased from 56% to 63%. The report finds, however, that the current rates of progress are insufficient to meet the targets set by SDG7 by 2030. There are also key regions where progress has been substantially slower. For example, the population without access to electricity is concentrated in Sub-Saharan Africa -- with an overall access rate of 47%.
+According to the tracking SDG7 report, there has been progress in improving access to both electricity and clean cooking in recent years. This report states that electricity access increased from 83% in 2010 to 90% in 2018, whilst clean cooking increased from 56% to 63%. The report finds, however, that the current rates of progress are insufficient to meet the targets set by SDG7 by 2030. There are also key regions where progress has been substantially slower. For example, the population without access to electricity is concentrated in Sub-Saharan Africa -- with an overall access rate of 47%.
There are several ways in which energy modelling can be used to aid with these goals. For instance, geospatial electrification modelling can be used to assess which access solutions are the most economical for different regions or sub-regions. This also includes capacity expansion planning to assess how supply can be increased whilst minimising economic and environmental impacts.
## Progress on SDG7: Renewable Energy and Energy Efficiency
-The graph below shows the share of electricity, heat and transport demands met by renewables (such as solar, wind, hydro and geothermal energy) globally. We can see that there has been some progress in increasing the share of renewables in the electricity and transport sectors as well as in the heat sector when traditional biomass use is excluded. According to the SDG7 Tracking Report, the share of renewable energy in Total Final Energy Consumption reached 17.3% in 2017, up from 16.3% in 2010.
+The graph below shows the share of electricity, heat and transport demands met by renewables (such as solar, wind, hydro and geothermal energy) globally. We can see that there has been some progress in increasing the share of renewables in the electricity and transport sectors as well as in the heat sector when traditional biomass use is excluded. According to the SDG7 Tracking Report, the share of renewable energy in Total Final Energy Consumption reached 17.3% in 2017, up from 16.3% in 2010.
The electricity sector has observed the most progress. This is largely due to the growth in solar photovoltaic (PV) and wind energy. However, more progress is required to achieve SDG7, with most scenarios requiring the decarbonisation of end-use sectors: for example, the electrification of transport and heat, sectors which have observed relatively slow progress.
@@ -57,14 +57,13 @@ The electricity sector has observed the most progress. This is largely due to th
**Figure 1.2.4:** Renewable energy and energy efficiency progress
[@UnitedNations2015]
+For energy efficiency, global reductions in primary energy intensity have slowed in recent years. Where energy intensity is the quantity of energy required per unit output or activity, so that using less energy to produce a product reduces the intensity. This is despite progress still being greater than in the period before 2010. The SDG7 Tracking Report analysis shows that the transport sector has seen an increase in energy intensity improvement since 2010, while other sectors have seen a decrease. Differences between regions are observed, with Sub-Saharan Africa having the highest energy intensity and Latin America and the Caribbean having the lowest.
-For energy efficiency, global reductions in primary energy intensity have slowed in recent years. Where energy intensity is the quantity of energy required per unit output or activity, so that using less energy to produce a product reduces the intensity. This is despite progress still being greater than in the period before 2010. The SDG7 Tracking Report analysis shows that the transport sector has seen an increase in energy intensity improvement since 2010, while other sectors have seen a decrease. Differences between regions are observed, with Sub-Saharan Africa having the highest energy intensity and Latin America and the Caribbean having the lowest.
-
-If the SDG7 target of doubling the rate of global improvement in energy efficiency by 2030 is to be met, energy efficiency measures must be prioritised in policy making and investment planning.
+If the SDG7 target of doubling the rate of global improvement in energy efficiency by 2030 is to be met, energy efficiency measures must be prioritised in policy making and investment planning.
## Links between SDGs
-The SDGs are highly interlinked, with synergies and trade-offs between these targets. It is important that these interactions are understood so that policymakers can make plans to maximise synergies and minimise trade-offs. Modelling can help in the making of informed decisions by providing a better understanding of these interactions. This is because models can be used to develop cross-sectoral scenarios and can help project the impacts of decisions.
+The SDGs are highly interlinked, with synergies and trade-offs between these targets. It is important that these interactions are understood so that policymakers can make plans to maximise synergies and minimise trade-offs. Modelling can help in the making of informed decisions by providing a better understanding of these interactions. This is because models can be used to develop cross-sectoral scenarios and can help project the impacts of decisions.
As an example, let us explore the links between the energy system and the SDG targets. One study [@nerini2018mapping] found that at least 113 of the 169 SDG targets require changes in energy systems. Examples of this include targets in SDG13, which focus on climate change action. This requires decarbonisation of the energy system. In addition SDG1, which focuses on ending poverty, requires improved energy infrastructure to increase modern energy access. Nerini et al. (2018) highlight that we cannot think in silos and must use integrated planning approaches with a long-term perspective. Energy modelling tools are a key enabler of such approaches.
diff --git a/docs/lecture_01/Lecture_1.3.md b/docs/lecture_01/Lecture_1.3.md
index 7aacae7..0f87a36 100644
--- a/docs/lecture_01/Lecture_1.3.md
+++ b/docs/lecture_01/Lecture_1.3.md
@@ -9,47 +9,45 @@ authors:
## Short description
-In this mini-lecture we will explore the benefits of energy planning and why energy modelling is needed. We will discover why different countries require different solutions and how a continuous, iterative process is required to perform analyses.
+In this mini-lecture we will explore the benefits of energy planning and why energy modelling is needed. We will discover why different countries require different solutions and how a continuous, iterative process is required to perform analyses.
## Learning objectives
-- Learn the principles of energy planning
-
-- Learn why energy modelling is needed
+- Learn the principles of energy planning
+- Learn why energy modelling is needed
# Lecture content
## Introduction
-Energy has become a fundamental commodity over the last 100 years. It has allowed society to make significant progress in some of the Sustainable Development Goals. For instance, it has brought millions out of poverty across the world. However, it has required the development of other development goals such as SDG13, which focuses on climate action. In addition, economic and sustainable development have grown in an inequitable manner, with certain regions prospering more than others.
+Energy has become a fundamental commodity over the last 100 years. It has allowed society to make significant progress in some of the Sustainable Development Goals. For instance, it has brought millions out of poverty across the world. However, it has required the development of other development goals such as SDG13, which focuses on climate action. In addition, economic and sustainable development have grown in an inequitable manner, with certain regions prospering more than others.
-This mini-lecture will introduce the concepts of energy planning in the context of the Sustainable Development Goals. In particular, we will explore how energy planning can be used to tackle some of the most difficult issues humanity faces.
+This mini-lecture will introduce the concepts of energy planning in the context of the Sustainable Development Goals. In particular, we will explore how energy planning can be used to tackle some of the most difficult issues humanity faces.
## Why plan an energy system?
The traditional methods of supplying energy come with some major pitfalls. One of the most significant issues is the emissions of carbon emissions, leading to climate change. However, we require energy to sustain the current global population; no energy would lead to mass starvation and population decline.
-Thus, a fundamental transformation of the energy system is required. Energy systems, however, are highly complex and capital intensive. In addition, these systems are constantly interacting with many other critical systems, such as the environment, natural resource systems and infrastructure. Thus, comprehensive, systematic analyses are required to avoid expensive stop-gap measures and long-term "lock-in" [@rodriguez2017energy].
+Thus, a fundamental transformation of the energy system is required. Energy systems, however, are highly complex and capital intensive. In addition, these systems are constantly interacting with many other critical systems, such as the environment, natural resource systems and infrastructure. Thus, comprehensive, systematic analyses are required to avoid expensive stop-gap measures and long-term "lock-in" [@rodriguez2017energy].
## Importance of energy planning
-An additional complication is that there is no single solution that can be applied to all energy systems. Different geographies have differing needs and resources. For instance, the UK has access to lots of offshore wind, whilst Kenya has access to lots of geothermal energy. The energy demand profiles and challenges of these two countries are also very different.
+An additional complication is that there is no single solution that can be applied to all energy systems. Different geographies have differing needs and resources. For instance, the UK has access to lots of offshore wind, whilst Kenya has access to lots of geothermal energy. The energy demand profiles and challenges of these two countries are also very different.
-In developing countries, access to affordable energy services is important to combat energy poverty. This is especially true for rural areas, but it is becoming increasingly true for large metropolitan areas as urbanisation accelerates.
+In developing countries, access to affordable energy services is important to combat energy poverty. This is especially true for rural areas, but it is becoming increasingly true for large metropolitan areas as urbanisation accelerates.
In contrast, developed countries, such as those in Europe, North America and Asia, struggle with the replacement of ageing plants and equipment. It is estimated that 40% of existing capacity stock is scheduled for retirement by 2040.
Therefore, investment decisions are required to alleviate these different issues. However, due to the long-term nature of these investments, these private sector dominated energy markets rely on clear and consistent government energy-environment policy to reduce uncertainty.
-
## What is energy planning?
Energy planning is the act of assessing the ability of a local, national or regional system to provide dependable energy services under constantly changing conditions. For example, variables such as the cost of materials and fuels, investment costs in technologies, demand levels and distribution requirements may all change.
-Drawing on the field of operations research, electricity planning applies advanced analytical methods and tools to make better decisions when faced with complex decisions. This process, however, must be done iteratively due to the fast transformations that can take place over a very limited time period.
+Drawing on the field of operations research, electricity planning applies advanced analytical methods and tools to make better decisions when faced with complex decisions. This process, however, must be done iteratively due to the fast transformations that can take place over a very limited time period.
-Energy planning models help discern the most cost-effective way of delivering energy to the final consumer. Of course, the most effective way of providing energy is different in different parts of the world. However, quantitative energy modelling offers a promising tool to make better decisions under uncertain conditions.
+Energy planning models help discern the most cost-effective way of delivering energy to the final consumer. Of course, the most effective way of providing energy is different in different parts of the world. However, quantitative energy modelling offers a promising tool to make better decisions under uncertain conditions.
There is a requirement in developing countries for energy planning
due to growing populations and a lack of access to electricity. However, the main barriers to energy models in developing countries are the lack of adequate data and a shortage of skilled human resources to perform the analyses. As a result, investment decisions are often based on ill-informed policy targets and the need for ad-hoc stop-gap measures. These measures, therefore, tend to focus on cheap and quick-to-build technologies. This can result in higher environmental and operating costs. It is often the case that such actions serve the supply shortfalls of already-connected consumers, and so, increasing access to energy is rarely part of the strategy.
@@ -72,4 +70,4 @@ For developing countries, the impact of even minor system improvements often can
## Summary
-In this mini-lecture we have covered the reasons that energy planning is important, how it can help with the Sustainable Development Goals and the various pitfalls that could occur without energy planning. We discovered that with sound energy planning, uncertainty can be reduced and greater stability can be provided to private sector investments. This can lead to sustainable economic growth if done in an optimal manner.
+In this mini-lecture we have covered the reasons that energy planning is important, how it can help with the Sustainable Development Goals and the various pitfalls that could occur without energy planning. We discovered that with sound energy planning, uncertainty can be reduced and greater stability can be provided to private sector investments. This can lead to sustainable economic growth if done in an optimal manner.
diff --git a/docs/lecture_01/Lecture_1.4.md b/docs/lecture_01/Lecture_1.4.md
index a39e534..2c4b418 100644
--- a/docs/lecture_01/Lecture_1.4.md
+++ b/docs/lecture_01/Lecture_1.4.md
@@ -10,34 +10,34 @@ authors:
## Short description
-Energy systems models can take many different forms, as they can be designed for global long-term energy markets, short-term energy dispatch markets or local markets. In this mini-lecture we will explore the different types of models that fit into different classifications.
+Energy systems models can take many different forms, as they can be designed for global long-term energy markets, short-term energy dispatch markets or local markets. In this mini-lecture we will explore the different types of models that fit into different classifications.
## Learning objectives
-- Classify different types of energy systems models
+- Classify different types of energy systems models
-- Identify the pros and cons of selected energy systems models
+- Identify the pros and cons of selected energy systems models
# Lecture content
## Introduction: Typical classifications
-Energy systems models can be broken down into four different categories in a typical classification. These include the time scales in which they model, the geographies in which they model, the analytical approach and the methodology [@Pfenninger2014]. The different classifications are shown below:
+Energy systems models can be broken down into four different categories in a typical classification. These include the time scales in which they model, the geographies in which they model, the analytical approach and the methodology [@Pfenninger2014]. The different classifications are shown below:
- Time Scale
- - Short
- - Medium
- - Long
+ - Short
+ - Medium
+ - Long
- Geography
- - Global
- - National
- - Sub-national
+ - Global
+ - National
+ - Sub-national
- Analytical Approach
- - Top-down
- - Bottom-up
+ - Top-down
+ - Bottom-up
- Methodology
- - Optimisation
- - Simulation
+ - Optimisation
+ - Simulation
## Time
@@ -59,10 +59,6 @@ Within energy systems models there exist at least two broad methodologies which
Simulations, on the other hand, are computer programs which describe system evolutions. These represent the behaviour of the main players in the energy system. This does not necessarily lead to a minimisation or maximisation of an objective, and it can take into account different uncertainties of what may occur as opposed to what should occur.
-
-
## Summary
This mini-lecture has covered the different classifications that energy models can fit into. We have seen that a range of different models are required to cover the whole requirements of different energy systems, with models suited for different needs.
-
-
diff --git a/docs/lecture_01/bibliography.bib b/docs/lecture_01/bibliography.bib
index cf21b0d..0dc469e 100644
--- a/docs/lecture_01/bibliography.bib
+++ b/docs/lecture_01/bibliography.bib
@@ -331,4 +331,4 @@ @article{nerini2018mapping
pages = {10--15},
year = {2018},
publisher = {Nature Publishing Group}
-}
\ No newline at end of file
+}
diff --git a/docs/lecture_02/Lecture_2.1.md b/docs/lecture_02/Lecture_2.1.md
index e2682c6..04cd5f1 100644
--- a/docs/lecture_02/Lecture_2.1.md
+++ b/docs/lecture_02/Lecture_2.1.md
@@ -10,7 +10,7 @@ authors:
# Short description
-In this mini-lecture we will give an introduction into the energy systems model, MUSE (ModUlar energy system Simulation Environment). We will cover the differences between MUSE and intertemporal optimisation models. We will also address the advantages and disadvantages of using MUSE.
+In this mini-lecture we will give an introduction into the energy systems model, MUSE (ModUlar energy system Simulation Environment). We will cover the differences between MUSE and intertemporal optimisation models. We will also address the advantages and disadvantages of using MUSE.
# Learning objectives
@@ -30,20 +30,16 @@ Therefore, MUSE is mainly designed to understand how long-term energy markets ma
## What are MUSE's unique features?
-MUSE is a generalisable agent-based modelling environment that simulates energy transitions from the point of view of the investor and consumer agents [@Sachs2019a]. This means that users can define their own agents based upon their needs and data. In addition, each of these agents can have different objectives. For instance, a proportion of the population may have higher disposable incomes, which allows them to spend more on heating and cooling rather than requiring cost minimisation. Another proportion may prefer to spend less on heating and cooling while still having high disposable incomes. This features differs from the optimisation-based approaches which can, for instance, minimise costs or maximise welfare from a central perspective.
+MUSE is a generalisable agent-based modelling environment that simulates energy transitions from the point of view of the investor and consumer agents [@Sachs2019a]. This means that users can define their own agents based upon their needs and data. In addition, each of these agents can have different objectives. For instance, a proportion of the population may have higher disposable incomes, which allows them to spend more on heating and cooling rather than requiring cost minimisation. Another proportion may prefer to spend less on heating and cooling while still having high disposable incomes. This feature differs from the optimisation-based approaches which can, for instance, minimise costs or maximise welfare from a central perspective.
-Another aspect that differs from optimisation models is the ability to model imperfect information and limited foresight. Optimisation models require full knowledge of the system at the beginning of the simulation. For example, such a model needs to know what the demand will be in 2050 at the beginning of the simulation in 2020. MUSE does not give this information to the investing agents at the beginning of the simulation, and therefore they must makes their investments under uncertainty. This adds a level of realism to MUSE and is a unique feature of agent-based models when compared to intertemporal optimisation models.
+Another aspect that differs from optimisation models is the ability to model imperfect information and limited foresight. Optimisation models require full knowledge of the system at the beginning of the simulation. For example, such a model needs to know what the demand will be in 2050 at the beginning of the simulation in 2020. MUSE does not give this information to the investing agents at the beginning of the simulation, and therefore they must make their investments under uncertainty. This adds a level of realism to MUSE, and is a unique feature of agent-based models when compared to intertemporal optimisation models.
## Benefits and disadvantages of MUSE
MUSE comes with a number of advantages and disadvantages when compared to other models. The benefits include, as discussed, the ability to model heterogeneous and diverse agents as well as to model limited foresight and imperfect information. Another one of the benefits of MUSE is its flexibility in designing a case study. Users can model anything from a single region to the global scale with trade occurring between regions. In addition, MUSE is able to model a single sector (such as the transport sector) to a whole energy systems approach. This flexibility allows for many different applications to be devised for interesting research and applications.
-However, this flexibility and simulation approach comes with a number of disadvantages when compared to other models. The first disadvantage is the complexity of the model. While building a case study is similar to the process for other models, the inner workings of MUSE can be complicated. This is due to its simulation-based method which relies on rule-based behaviours, as opposed to optimisation. Another disadvantage is that the computation time of MUSE can increase with the complexity of the case study. Therefore, it becomes important to make decisions based on the sectors, timeslicing and other characteristics that are modelled. For instance, it may not be feasible to model every single sector in an energy system, and instead the model should be limited to a subset of relevant sectors.
-
+However, this flexibility and simulation approach comes with a number of disadvantages when compared to other models. The first disadvantage is the complexity of the model. While building a case study is similar to the process for other models, the inner workings of MUSE can be complicated. This is due to its simulation-based method which relies on rule-based behaviours, as opposed to optimisation. Another disadvantage is that the computation time of MUSE can increase with the complexity of the case study. Therefore, it becomes important to make decisions based on the sectors, timeslicing and other characteristics that are modelled. For instance, it may not be feasible to model every single sector in an energy system, and instead the model should be limited to a subset of relevant sectors.
# Summary
-In this mini-lecture we were introduced to the energy systems model, MUSE. We learnt of its unique features, such as heterogeneous agent behaviour, limited foresight and imperfect information. We also discovered the advantages and disadvantages of MUSE. For example, its flexible nature, which allows many different types of case-studies, can also make the model increasingly complex.
-
-
-
+In this mini-lecture we were introduced to the energy systems model, MUSE. We learnt of its unique features, such as heterogeneous agent behaviour, limited foresight and imperfect information. We also discovered the advantages and disadvantages of MUSE. For example, its flexible nature, which allows many different types of case-studies, can also make the model increasingly complex.
diff --git a/docs/lecture_02/Lecture_2.2.md b/docs/lecture_02/Lecture_2.2.md
index 8de5d22..43badce 100644
--- a/docs/lecture_02/Lecture_2.2.md
+++ b/docs/lecture_02/Lecture_2.2.md
@@ -11,7 +11,7 @@ authors:
# Short description
-In this mini-lecture we will give you an overview of how MUSE works. This will include the different sectors that make up MUSE, including primary supply sectors, conversion sectors and demand sectors. We will discover how these sectors are interlinked through a market clearing algorithm, which ultimately decides the prices of energy commodities and the final energy system, according to MUSE.
+In this mini-lecture we will give you an overview of how MUSE works. This will include the different sectors that make up MUSE, including primary supply sectors, conversion sectors and demand sectors. We will discover how these sectors are interlinked through a market clearing algorithm, which ultimately decides the prices of energy commodities and the final energy system, according to MUSE.
# Learning objectives
@@ -55,6 +55,4 @@ The MCA then sends these demands to the sectors that supply these energy commodi
# Summary
-This mini-lecture provided key information to understand the underlying mechanics of MUSE. We learnt how MUSE is made up of different sectors, which are linked by a market clearing algorithm to simulate how prices are calculated. This mechanism closely models the real-life global electricity market.
-
-
+This mini-lecture provided key information to understand the underlying mechanics of MUSE. We learnt how MUSE is made up of different sectors, which are linked by a market clearing algorithm to simulate how prices are calculated. This mechanism closely models the real-life global electricity market.
diff --git a/docs/lecture_02/Lecture_2.3.md b/docs/lecture_02/Lecture_2.3.md
index f7c6fbc..400357b 100644
--- a/docs/lecture_02/Lecture_2.3.md
+++ b/docs/lecture_02/Lecture_2.3.md
@@ -12,7 +12,7 @@ Mini-lecture 2.3 provides an overview of the benefits of using an agent-based mo
# Learning objectives
-- Understand the concept of limited foresight
+- Understand the concept of limited foresight
- Understand the concept of imperfect information
# Introduction
@@ -27,26 +27,20 @@ With some models it is necessary to make this assumption of perfect information.
## Limited foresight
-Limited foresight specifies how players within a game understand how the future may evolve. In the real-world, prediction and forecasting are difficult problems to solve, particularly within the uncertainty of energy markets. This become even more challenging when trying to make long-term predictions.
+Limited foresight specifies how players within a game understand how the future may evolve. In the real-world, prediction and forecasting are difficult problems to solve, particularly within the uncertainty of energy markets. This become even more challenging when trying to make long-term predictions.
-Within MUSE long-term predictions must be made by investor agents. For example, if a company wanted to invest in a power plant, they would need to predict the amount of money they can sell their electricity for over the lifetime of the power plant, or in other words the market price for electricity. In some cases power plants operate for 30 years or more and so electricity prices 30 years into the future are required!
+Within MUSE, long-term predictions must be made by investor agents. For example, if a company wanted to invest in a power plant, they would need to predict the amount of money they can sell their electricity for over the lifetime of the power plant, or in other words the market price for electricity. In some cases power plants operate for 30 years or more, and so electricity prices 30 years into the future are required!
MUSE makes a simplified assumption about the future prices expected by investors: they know what the price will be in the next five years. However, they assume a flat forward extension of the prices from this period. Or in other words, the energy prices over the entire lifetime of the plant are the same as the known price in the next five years. However, this assumption that the investors make will more than likely not be correct, leading to errors in their predictions, just like in the real world.
In contrast to perfect foresight, where variables such as prices, demand and technology costs in all the future time periods are known from the beginning of the simulation, using the limited foresight period, agents make investments under expectations of the market, which may be wrong.
- Figure 2.3.1, below, details how MUSE runs. Firstly, the initial capacity, price trajectory and demand trajectory are known and set by the user. These variables are exogenous to the model, which is to say that they are fixed and imposed on the model. These are used to initialise the MCA convergence algorithm. The MCA convergence algorithm finds a suitable set of investments which equilibrate supply and demand. Once equilibrium has been reached, the technologies are decided and the commodity prices are set. These commodity prices reflect the technology marginal costs, or the costs required to generate or create 1 unit of commodity, excluding capital costs. The investments balance asset retirements and the increase in demand, ensuring that supply meets demand.
-
-This whole process repeats itself at every timestep (t) until the specified number of milestone years have run.
+Figure 2.3.1, below, details how MUSE runs. Firstly, the initial capacity, price trajectory and demand trajectory are known and set by the user. These variables are exogenous to the model, which is to say that they are fixed and imposed on the model. These are used to initialise the MCA convergence algorithm. The MCA convergence algorithm finds a suitable set of investments which equilibrate supply and demand. Once equilibrium has been reached, the technologies are decided, and commodity prices are updated based on the average levelized cost of producing each commodity. The investments balance asset retirements and the increase in demand, ensuring that supply meets demand.
{width=100%}
**Figure 2.3.1:** MUSE iteration process
-
-
-# Summary
+# Summary
This mini-lecture provided an introduction to the terms limited foresight and imperfect information. We learnt how these assumptions have been integrated into the MUSE model and what this means for the modelling process. In the next mini-lecture we will explore the key components that make up MUSE.
-
-
diff --git a/docs/lecture_02/Lecture_2.4.md b/docs/lecture_02/Lecture_2.4.md
index 22b604e..cad4737 100644
--- a/docs/lecture_02/Lecture_2.4.md
+++ b/docs/lecture_02/Lecture_2.4.md
@@ -20,7 +20,7 @@ In this mini-lecture we will explore the key components which make up MUSE. Thes
The energy service demand is a user input which defines the demand that an end-use sector has. An example of this is the service demand commodity of heat or cooling that the residential sector requires. End-use, in this case, refers to the energy which is utilised at the very final stage, after both extraction and conversion.
-The estimate of the energy service demand is the first step. This estimate can be an exogenous input derived from the user, or correlations of GDP and population which reflect the socio-economic development of a region or country.
+The estimate of the energy service demand is the first step. This estimate can be an exogenous input derived from the user, or correlations of GDP and population which reflect the socioeconomic development of a region or country.
## Technologies
@@ -36,7 +36,7 @@ Each of the technologies are placed in their regions of interest, such as the US
- Utilisation factor
- Interest rate
-Technologies, and their parameters, are defined in a specific file called the Technodata file.
+Technologies, and their parameters, are defined in a specific file called the Technodata file.
## Sectors
@@ -53,7 +53,7 @@ Each of the technologies, which consume a commodity, also output a different com
## Agents
-Agents represent the investment decision makers in an energy system, for example consumers or companies. They invest in technologies that meet service demands, like heating, or produce other needed energy commodities, like electricity. These agents can be heterogenous, meaning that their investment priorities have the ability to differ.
+Agents represent the investment decision makers in an energy system, for example consumers or companies. They invest in technologies that meet service demands, like heating, or produce other needed energy commodities, like electricity. These agents can be heterogeneous, meaning that their investment priorities have the ability to differ.
As an example, a generation company could compare potential power generators based on their levelised cost of electricity, their net present value, by minimising the total capital cost, a mixture of these and/or any user-defined approach. This approach more closely matches the behaviour of real-life agents in the energy market, where companies, or people, have different priorities and constraints.
@@ -63,8 +63,7 @@ The market clearing algorithm (MCA) is the central component between the differe
For a hypothetical example, the price of electricity is set in a first iteration to $70/MWh. However, at this price, the majority of residential agents prefer to heat their homes using gas. As a result of this, residential agents consume less electricity and more gas. This reduction in demand reduces the electricity price to $50/MWh in the second iteration. However, at this lower electricity price, some agents decide to invest in electric heating as opposed to gas. Eventually, the price converges on $60/MWh, where supply and demand for both electricity and gas are equal.
-This is the principle of the MCA. It finds an equilibrium by iterating through each of the different sectors until an overall equilibrium is reached for each of the commodities. It is possible to run the MCA in a carbon budget mode, as well as an exogenous mode. The carbon budget mode ensures that an endogenous carbon price is calculated to limit the emissions of the energy system to be below a user-defined value. Whereas, the exogenous mode allows the carbon price to be set by the user.
-
+This is the principle of the MCA. It finds an equilibrium by iterating through each of the different sectors until an overall equilibrium is reached for each of the commodities. It is possible to run the MCA in a carbon budget mode, as well as an exogenous mode. The carbon budget mode ensures that an endogenous carbon price is calculated to limit the emissions of the energy system to be below a user-defined value. Whereas the exogenous mode allows the carbon price to be set by the user.
# Summary
@@ -78,6 +77,4 @@ In this mini-lecture we have explored the different components which make up MUS
All of these components interact, for example the agents in a particular sector invest in technologies to meet a certain service demand. Finally, the market clearing algorithm brings these different components together to find an ultimate price on all the different factors of the particular case study.
-We will provide more information on agents and their capabilities in lecture 7.
-
-
+We will provide more information on agents and their capabilities in lecture 6.
diff --git a/docs/lecture_03/Lecture_3.1.md b/docs/lecture_03/Lecture_3.1.md
index 088996e..637ba9b 100644
--- a/docs/lecture_03/Lecture_3.1.md
+++ b/docs/lecture_03/Lecture_3.1.md
@@ -1,95 +1,40 @@
---
-title: Mini-Lecture 3.1 -- Energy demands in energy systems modelling
+title: Mini-Lecture 3.1 –- Residential Sectors in MUSE
keywords:
-- Energy demand
-- Energy systems models
+- Residential sector
+- Sectors in MUSE
authors:
- Alexander J. M. Kell
---
-To begin lecture 3, this mini-lecture provides an overview of energy demands within an energy system. We will cover differences in energy demands by sector, time and population classes. We will also begin to explore why these differences are important within energy models. Lecture 3 will take you through the basics for modelling energy demand in MUSE, the different options available to do so, and some specific examples
+This mini-lecture introduces the concept of the residential sector
# Learning objectives
-- Learn what energy demands are in an energy modelling context
-- Understand how demands can change based on different variables
-
-# Introduction
-
-Everyone needs energy for many different purposes. The form in which this energy should be delivered is dependent on the specific application. These demands for energy come from all sectors of society such as:
-
-- The residential sector (rural and urban)
- - Cooking
- - Heating
- - Cooling
- - Lighting
- - Appliances
-- Industry
- - Chemical processes
- - Steam production
- - Heating
-- Commerce
- - Lighting
- - Heating
- - Cooling buildings
- - Keeping products at low temperatures
-- Transport
- - Cars
- - Trucks
- - Buses
- - Aviation
- - Shipping
- - Trains
-- Agriculture
- - Tractors
- - Machinery
- - Pumping water
-
-## Variations in daily energy demand
-
-These energy demands can vary on hourly, daily, weekly and monthly timescales. This mainly reflects the schedule of consumers' activities. For example, on a monthly timescale more cooling will be used in summer and more heating in winter. However, these energy demands can also vary by sector, as shown by Figure 3.1.1.
+- Understand the role of the residential sector, its technologies and the main energy and societal challenges
-{width=100%}
-
-**Figure 3.1.1:** Variations of energy demand by sector in a hypothetical example [@Taliotis2018].
-
-Figure 3.1.1 shows us that the magnitude of demand varies by sector, with agricultural demand significantly lower than residential and commercial demand, in this example. The reason that the commercial and residential sectors consume more is because their activities are more energy intensive or they are simply larger.
-
-We can also see that the daily profile of demand varies by sector. For example, in Figure 3.1.1 we can see that there is a clear evening peak in residential demand, whereas agricultural and industrial demand remains flat throughout the day. This is because agricultural and industrial demands are consistent throughout the day. This is likely because the industrial and agricultural sector operate constantly, whereas energy use in homes peaks in the evening when consumers use more electricity for cooking, lighting and appliances when they return from work or other business.
-
-## Sector specific demands
-
-The differences between sectors means that it can sometimes be important to model demands separately by each sector. This feature allows the models to consider the specific characteristics of each demand.
-
-Within each of these sectors, the energy demand varies over time and across different types of consumers. For example, within the residential sector, demands can differ between rural and urban households, as shown in Figure 3.1.2. This can also be true between grid-connected and off-grid areas. Energy planners must ensure that energy demand is always met for all types of consumers. Therefore, it is important that the key characteristics of different demands are represented in energy models.
+# Overview of the residential sector and its demands?
-{width=100%}
+Energy is used for many different reasons in the residential sector, as shown by Figure 3.1.1. This image shows the share of residential energy by service demand. We can see that energy is used for many different purposes, from heating and cooking to cleaning and ironing. This split of energy demand will vary across different countries. Figure 3.1.1 shows residential energy demand in Italy, which will differ to countries in Asia, for instance. This is largely dependent on different climates, levels of development and lifestyles.
-**Figure 3.1.2:** Variations of energy demand for the residential sector by population types [@Olaniyan2018]
-
-
-## Long-term variations in energy demands
-
-A major challenge in energy planning is that energy demands can change over time. This could be due to population growth or the creation of new industries. Figure 3.1.3 displays historical variations in energy demands. It is likely that these demands are correlated to changes in society. For example, increases in energy demand likely reflect increased industrial activity. For energy planning, we must also think about how energy demands are likely to change in the future.
-
-We can often forecast energy demand, such as with future projections as shown in Figure 3.1.3. These forecasts can be created using estimates of the key influencers of energy demand, such as population growth and economic activity. Future projections are often based on how energy demands have changed historically.
-
-{width=100%}
+{width=100%}
-**Figure 3.1.3:** Long-term energy consumption by source
+**Figure 3.1.1:** Residential sector in Italy and the different demands [@en12112055]. (Note: DHW refers to Domestic Hot Water).
+The total magnitude of energy demand varies by country as a total value, but also as energy demand per capita. This is strongly dependent on the level of electricity access and availability of other fuels in the country. Residential activities can use different forms of energy. For example, cooking can be met by burning biomass, oil products, natural gas or electricity. The fuels used vary by country.
-## Capacity expansion planning
+## Residential sector technologies
-One of the key purposes of MUSE is for capacity expansion. Figure 3.1.4 displays this key issue which MUSE can address. Essentially, if total demand increases (green line) and existing system capacities are retired (blue line), how can we invest to meet the energy capacity needed to supply demand (red line)?
+Some of the key residential technologies include lamps, cooking stoves, heating and air conditioning systems, as well as other electrical appliances. Some of these technologies can only use one fuel, such as electrical appliances and air conditioning which rely on electricity.
-{width=100%}
+However, in other cases multiple different fuels can be used for the same purpose. For example, heating. Heating can be met by burning biomass, natural gas, oil or electricity, for instance. These technologies have differing performance parameters. For example, electric stoves are usually much more efficient than biomass stoves. Different technological options also have different impacts on the environment and on human health. For example, the emissions from biomass can have detrimental impacts on human health, whereas electric stoves do not have emissions in the home.
-**Figure 3.1.4:** Capacity expansion [@Taliotis2018]
+It is possible to model these different options in MUSE, which allows us to gain insights into their environmental and cost implications. Modelling can allow us to model the entire system as a whole, understand the trade-offs between certain technologies and make decisions on which policies to implement.
-You may notice that the red line is higher than the green line at all points. This is due to losses due to lower generating efficiencies. The gap between the red and blue lines demonstrates the required capacity expansion over time. MUSE enables us to plan such a capacity expansion whilst considering technical, economic and environmental constraints.
+## Residential sector in MUSE
+Within MUSE we can model different technology options. For instance, if we are to model an electric stove and a biomass stove we would have different inputs (CommIn.csv file). However, we would have the same output (CommOut.csv file) of cooking demand. We can also model an increase in efficiency of a technology by lowering the value in the CommIn.csv file. It is possible to change the efficiency over time using interpolation or a flat-forward extension as explained in mini-lecture 4.4. We can also consider the costs of investing in more energy efficient appliances by increasing the cost of these high efficiency appliances relative to the low efficiency appliances. By doing this, we can understand where and when investments in energy efficiency might be economic.
# Summary
-In this mini-lecture we covered the differences between energy demands in different population types, sectors and timescales. We learnt why it is important to model these differences in demand in energy systems models. We also explored how energy systems models can be used to meet a changing demand profile in the future.
+In this lecture we have explored the residential sector. We considered the different demands that can reside within the residential sector and the different technologies that can be used to meet these demands. We also learnt of the difference in demands between countries and how we can model different technologies within MUSE.
diff --git a/docs/lecture_03/Lecture_3.2.md b/docs/lecture_03/Lecture_3.2.md
index c82aaac..f9678c3 100644
--- a/docs/lecture_03/Lecture_3.2.md
+++ b/docs/lecture_03/Lecture_3.2.md
@@ -1,57 +1,54 @@
---
-title: Mini-Lecture 3.2 -- Energy demands in modelling
+title: Mini-Lecture 3.2 -- The transport sector in MUSE
keywords:
-- Energy demands
-- Scenario analysis
+- Transport sector
+- Energy modelling
authors:
- Alexander J. M. Kell
---
-Mini-lecture 3.2 outlines the general requirements for defining energy demands and how modelling different scenarios can help assess potential future energy demand.
+This mini-lecture introduces the transport sector. We will explore the different demands and technologies within the transport sector and how we can model them within MUSE.
# Learning objectives
-- Understand how to define energy demands
-- Understand why we need scenario analysis
+- The main characteristics of the transport sector
+- How these can be modelled within MUSE
-# Introduction
+# Overview of the transport sector and its demands
-Within modelling we can break up the previously defined energy demands by sector. Electricity comes from the power sector and can be used to fulfil demand from each of the final service sectors. For example, the residential, commercial or industrial sector.
+The transport sector is vital in the modern age. In the last few decades, the use of transport has increased significantly. This is as more people gain access to vehicles and develop lifestyles which rely on transport.
-These sectors can have different electricity demands and needs and which can evolve over time as was seen in the last mini-lecture. We will now explore how these energy demands can be defined.
+Figure 3.2.1 shows different modes of transport. As can be seen, road transport is the most used transport mode. We can also see that over 90% of fuel used in the EU transport sector is petroleum based. This is similar across the world. However, this creates challenges due to the unsustainability of fossil fuels.
-## Defining energy demands
+{width=100%}
-When defining an energy demand for energy systems models, it is important to identify the following:
+**Figure 3.2.1:** Transport modes and fuel share in the EU [@en13020432].
-- The energy carrier which the demand arises for. For example, electricity, gasoline for transportation or biomass for cooking.
-- The sector the demand arises from. For example, residential (urban and/or rural, off- or on-grid), industrial or commercial.
-- The average variability of the demand within a year. This is usually expressed using average demand profiles, which are explained in more detail later in this lecture.
-- The current and expected future annual average demand.
+Due to the unsustainability of fossil fuels, other solutions have been taken up with support from governments around the world. For example, cars, motorbikes and buses can be fuelled by electricity. Electric vehicles have seen large reductions in cost and improvements in performance. Electric vehicles could play an important role in overcoming the sector's challenges.
-However, it is very difficult to predict future demand, and there will always be uncertainty in our predictions. Due to this it is important to model different scenarios.
+It is possible to model the different technologies in MUSE, and observe competition between technologies based upon their technoeconomic parameters.
-## Defining our own energy demand
+## Emissions
-As has just been seen, when we want to define our own energy demand, we need to identify a number of different features. Let's say, for example, that we want to define the demand for electricity in urban homes. To do this, we need to define:
+The transport sector was estimated to be responsible for around 16% of global emissions in 2016 [@owidco2andothergreenhousegasemissions]. Thus, scenarios consistent with meeting global climate targets require transport sector emissions to decline rapidly. Therefore a rapid move towards sustainable technologies, such as electric vehicles is required. It is true, however, that some modes of transport are difficult to decarbonise. For example, it is difficult to decarbonise shipping and aviation technologies. This is because the energy density of lithium ion batteries and other technologies are lower than oil-based products. It is worth mentioning, however, that decarbonising transport is only useful if the energy sector increases its low-carbon electricity sources to supply the transport sector.
-- The energy carrier for which the demand arises for. In this case it is electricity.
-- The sector the demand arises from. In this example it is the residential sector, or the urban residential sector if you would like to be more specific.
-- The average variability of the demand over the year. In this example we can look at daily and yearly electricity demand profiles for a residential urban area. This will tell us how the demand varies on a daily and seasonal scale.
-- Current and predicted future demand. For this, we can look at an energy balance (covered in more detail later) to get data for the current and historical residential electricity demand. We can use these data as a baseline, and we could combine it with an estimate of population growth to create a future projection for the demand.
+## Transport sector in MUSE
-## Scenario analysis
+Similar to the residential sector, we can define different technologies for the transport sector using technoeconomic parameters. For example, we can split road transport into three categories:
-Within energy systems modelling, we must explore different possibilities of what could happen in the future. This is known as scenario analysis. We do this as the future is uncertain, particularly over the long-term horizon. We therefore might want to consider multiple scenarios to assess how demand could vary in the future.
+- Cars
+- Motorcycles
+- Buses
-For example, for different scenarios, key predictors of energy demand, such as population growth, economic development and energy policy can be varied across the scenarios. This would mean that each scenario has a different energy demand projection.
+We can then split these three categories into their propulsion system. For instance:
-Since we can not be certain of the scenario which will be the best predictor of the future, it is useful to model several scenarios and consider the implications of each of them to give useful insights for policymaking. This allows policy makers to assess which of the different policies and mixes suit their needs based upon likelihoods and risk tolerances.
-
-# Summary
-
-Mini-lecture 3.2 provided an overview of energy demands, how we can define them and the details which make them up. We also explored how we can perform scenario analysis with energy demands, to understand what could happen in the future.
+- Electric vehicles
+- Conventional vehicles
+We can source road transport data from national energy balances such as from the IEA, and divide this between cars, motorcycles and buses based on the split of transport by mode in the country.
+We can then run a MUSE model with the different parameters and see the effect of these different parameters on agent investment decisions. These parameters could be fuel prices, technology costs or performance parameters. We can also run the model with a carbon limit, which places a tax on carbon emissions, allowing us to work out how to pick a desirable policy depending on what we are trying to achieve.
+# Summary
+In this mini-lecture we have considered the transport sector and how we can model this within MUSE. We discussed the emissions of the transport sector, and how different technologies can be used to reduce these emissions.
diff --git a/docs/lecture_03/Lecture_3.3.md b/docs/lecture_03/Lecture_3.3.md
index ff9993f..128dd65 100644
--- a/docs/lecture_03/Lecture_3.3.md
+++ b/docs/lecture_03/Lecture_3.3.md
@@ -1,62 +1,46 @@
---
-title: Mini-Lecture 3.3 -- Energy demand in MUSE
+title: Mini-Lecture 3.3 -- The industrial and commercial sectors
keywords:
-- Energy demand
-- MUSE
+- Industrial sector
+- Commercial sectors
+- MUSE modelling
authors:
- Alexander J. M. Kell
---
-## Short description
+This mini-lecture reflects on
-Following mini-lecture 3.2, this mini-lecture provides an insight into how to model service demand within MUSE. There are two possible methods to model service demand in MUSE, from user input and by correlation. In this mini-lecture we will learn what the difference is between these.
+# Learning objectives
-## Learning objectives
+- The main characteristics of the industrial and commercial sectors
+- How these can be modelled within MUSE
-- Understand how to input exogenous service demand
-- Understand what service demand by correlation is
+# Overview of the industrial and commercial sectors
-# Lecture content
+Next, we will explore the industrial and commercial sectors and their respective energy demands. Figure 3.3.1. shows the energy consumption for different sectors, including industrial, by OECD (generally high-income countries) and non-OECD countries (generally low- and middle-income countries). It is evident that the industrial sector is responsible for a large share of energy consumption across the world. The industrial sector is forecast to rise in non-OECD countries significantly. We must also consider this growing expected demand in the modelling process and during policy design.
-## Service Demand
+{width=100%}
-A service demand is a term used to describe the consumption of energy by human activity. This could be, for instance, energy for lighting or cooking in the residential sector, personal vehicles in the transportation sector or machine usage in the industrial sector. The service demand drives the entire energy system, and it influences the total amount of energy used, the location of use and the types of fuels used in the energy supply system. It also includes the characteristics of the end-use technologies that consume energy.
+**Figure 3.3.1:** Energy consumption by sector, OECD and non-OECD [@world1020007].
-## Exogenous service demand
+Energy is used in industry for a number of different purposes. For instance, heating and cooling, running machinery and chemical processes. These processes use a large variety of fuels and depend on the purpose, location and the technoeconomics.
-Within MUSE we must set the energy demand exogenously. That means that the model does not calculate how much the service demand is. Effectively, this means that the user must make an assumption on how much electricity is consumed in, for example, the residential sector for a particular region in the model.
+The commercial sector has a lower energy demand when compared to the industrial sector. This is because commercial processes, typically, are less energy intense and on smaller scales. This demand is often lighting, heating and to run office equipment and appliances.
-We can change this per scenario, but these values will not change during a simulation run, even if the price for all fuels increases significantly, for instance. We are able to define the exogenous service demand by year, sector, region and timeslice.
+## Industrial and commercial technologies
-## Service demand by correlation.
+Commercial activities use many different technologies which require energy inputs. For example, office electronics, lighting and heating systems. Many of these technologies use electricity. However, for some demands natural gas is used, for example for heating commercial buildings.
-In the previous section we learnt about the exogenous service demand. That is, we can explicitly specify what the demand would be per year, sector, region and timeslice. However, it may be the case that we do not know what the electricity demand is per year, especially in the future. We may instead conclude that our electricity demand is a function of the GDP and population of a particular region, as previously discussed.
+The industrial sector uses a wide range of technologies. This includes heavy machinery, boilers, heating and air conditioning. Again, a wide variety of fuels can be used for this. However, there exist a number of processes, such as steel manufacturing which requires very high temperatures. This is usually only done by burning fossil fuels, as it can be difficult to reach these high temperatures with electricity.
-To accommodate such a scenario, MUSE enables us to choose a regression function that estimates service demands from GDP and population projections, which may be more predictable or have more accessible data in your case. A regression function is simply a mathematical model which fits a linear model to your data to predict what may happen in the future.
+## Modelling industrial and commercial sectors in MUSE
-## Sources for energy demand data
+Similarly to the residential and transport sectors, we can use an energy balance [@iea_world_energy_balance] to estimate industry demands -- for instance, for industry heating demands. There are different technologies available for industrial heating. These can be grouped in a way that makes sense for your case study. However, as an example we can group these into high heat and low heat, which are modelled as separate demands. This is because generating very high temperatures requires different technologies and processes to generating low heat.
-We can get publicly available energy balance data and/or demand projections from the following sources:
+Again, we can group the technologies by their input fuel, such as biomass, coal, oil products or electricity with the `CommIn.csv` file. Through modelling with MUSE we can understand the emissions and economics of different technologies.
-- International Energy Agency
-- International Renewable Energy Agency
-- United Nations Statistics
-- Asia-Pacific Economic Cooperation
+In addition, the commercial sector will have a different demand load profile to the residential sector. This is because, typically, the demand will follow office times for the specific region, whereas the residential sector will follow the inverse of the office schedule.
-Energy balances tell us the amount that each energy commodity is used in a country or region in a given year. This is usually broken down by sector.
-
-
-## Summary
-
-In this mini-lecture we introduced service demands, and the way we can input these into MUSE. The two ways we can input service demands are:
-- Exogenous service demand
-- Service demand by correlation
-
-We also learned where we can get energy data from for various countries.
-
-In the hands-on we will see how we can actually do this within MUSE.
-
-
-
-
+# Summary
+In this mini-lecture we explored the industrial and commercial sectors. We learnt the difference between these two sectors in terms of demand and the different types of technologies used in these sectors. We saw that demand for the industrial sector is expected to rise significantly in non-OECD countries. Finally, we learnt how we can model different technologies in MUSE.
diff --git a/docs/lecture_03/Lecture_3.4.md b/docs/lecture_03/Lecture_3.4.md
index 7a7e2e3..c243a31 100644
--- a/docs/lecture_03/Lecture_3.4.md
+++ b/docs/lecture_03/Lecture_3.4.md
@@ -1,69 +1,33 @@
---
-title: "Mini-Lecture 3.4 -- Demand examples and units"
+title: Mini-Lecture 3.4 -- Sector coupling
keywords:
-- Infrastructure performance
+- Preset sectors
+- Service demand
authors:
- Alexander J. M. Kell
---
-# Short description
-
-Mini-lecture 3.4 explains how we can use timeslices to approximate the real-world demand profile. We will look into the difference between power and energy. Finally, we will learn how to convert units to ensure we are consistent within MUSE.
+In this mini-lecture we will investigate the role of electrification in different sectors, as well as find out what sector coupling is.
# Learning objectives
-- Understand how timeslices can be used in the context of demand
-- Understand the difference between power and energy
-- Know the units to use within MUSE and how to convert these
-
-# Demand profile
-
-Figure 3.1.5 shown an example demand profile for electricity that could be used in MUSE. In this demand profile there are 96 bars: one for each of the timeslices used in MUSE. These timeslices are split into 16 different sections – seasonal and into day and night. This is because there are four different seasons, which are split into day and night (twice). The demand profile is used to represent the proportion of demand occurring in each timeslice.
-
-{width=100%}
-
-**Figure 3.4.1:** Example demand profile for MUSE
-
-The chart shows us that electricity demand, in this example, is highest during the day in winter, while it is lowest during the night in spring. However, it is important to note that this is a simplification: in reality demand varies in the season and with each hour of the day. This simplification means that we model one representative day for each season, and we assume equal demand within days and nights of those seasons.
-
-Whilst this is a simplification, it allows us to consider the variation in demand across seasons and days without having an incredibly complex model structure. This reduces the amount of time required to run a full model relative to having timeslices for each hour and day of the year, as well as reducing the data input requirements.
-
-## Units
-
-We must ensure that during our data input process we are consistent with our units. Usually we will use the petajoules unit as this is the unit for energy for different sectors. If you were just modelling the power sector, you could use megawatt hours.
-
-## Power vs. Energy
+- Understand the importance of sector electrification
+- Understand the need for sector coupling
-When using energy modelling tools it is important to remember the difference between power and energy. Sometimes these terms are used interchangeably. However, there is an important difference between the two:
+# Sector electrification
-- Energy is the total amount of work done or the total capacity for doing work
-- Power is the rate at which this energy is supplied or used.
+Electrification is becoming increasingly important in all sectors of the economy in order to achieve decarbonisation goals. As we saw earlier, electrification can be used to decarbonise the residential, transport, industrial and commercial sectors. However, some sectors are likely to be easier to electrify than other sectors. We have seen rapid progress with electric vehicles in parts of the transport sector, but sectors such as shipping and steel, which are harder to decarbonise, still have a way to go.
-Therefore, energy and power have different units. For example, energy is often measured in Joules, while power is often measured in Joules per Second (or Watts).
+However, different options exist for the decarbonisation of steel, for example. This can be done by retrofitting blast furnaces and adding carbon capture and storage (CCS) or scaling up hydrogen-based direct reduced iron. However, this will require innovation and further research on the key technologies, such as CCS.
-For example, providing the weight stays the same, lifting a weight requires the exact same amount of energy no matter how quickly we lift it. However, if we lift the weight more quickly, the power has increased. We used the same amount of energy, but over a shorter amount of time.
+## Sector coupling
-## Units for demand
+We have seen that we must decarbonise to meet global climate targets. However, this is not a straightforward process. A large reason for this is the inflexibility of intermittent renewable resources such as solar and wind technologies. One method of mitigating this variability and inflexibility is through sector coupling. Sector coupling is where we connect energy demands and processes across differing sectors and increase the efficiency and flexibility of energy use. This would allows us to use renewable energy for all sectors.
-It is important that we convert our data from different sources to petajoules (PJ) when we include it in MUSE.
+One way this could be achieved is through power to gas conversion. When there is a high supply of renewable power, excess electricity could be used to produce hydrogen and methane. This would allow us to store this energy for later use across multiple sectors. This would enable sectors that are difficult to electrify to be based on renewable energy.
-Here are some example conversion factors:
-
-- 1 Petajoule (PJ) = 1000 Terajoules (TJ)
-- 1 Petajoule (PJ) = 1,000,000 Gigajoules (GJ)
-- 3.6 Petajoules (PJ) = 1 Terawatt hour (TWh)
-- 0.0036 Petajoules (PJ) = 1 Gigawatt hour (GWh)
-
-We must ensure that we are consistent with the units we use within MUSE.
+It is possible to model this sector coupling process within MUSE and to understand the tipping points which would make sector coupling possible. This could be based on the price and capacity of renewable energy, as well as the price of generating hydrogen or methane compared to the incumbent technologies.
# Summary
-In this lecture we have learnt the difference between power and energy. We have also learnt how to use timeslicing to speed up our model and reduce complexity. Finally, we learnt that we must use consistent units.
-
-
-
-
-
-
-
-
+In this lecture we have covered the importance of electrifying different sectors to reduce carbon emissions and meet some of the Sustainable Development Goals. We have also learnt of the importance of sector coupling to address hard to decarbonise sectors.
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diff --git a/docs/lecture_03/bibliography.bib b/docs/lecture_03/bibliography.bib
index 16a3e17..0d1365b 100644
--- a/docs/lecture_03/bibliography.bib
+++ b/docs/lecture_03/bibliography.bib
@@ -1,24 +1,62 @@
-@article{Taliotis2018,
- abstract = {Defining final energy demands },
- author = {Taliotis, Constantinos and Gardumi, Francesco and Shivakumar, Abhishek and Sridharan, Vignesh and Ramos, Eunice and Beltramo, Agnese and Rogner, Holger and Howells, Mark},
- file = {:Users/alexanderkell/Downloads/Defining final energy demands in OSeMOSYS.pdf:pdf},
- keywords = {Demand,Energy,Energy System,Modelling,Osemosys},
- number = {January},
- title = {{Defining final energy demands in OSeMOSYS}},
- year = {2018}
+
+@article{en12112055,
+ author = {Mancini, Francesco and Lo Basso, Gianluigi and De Santoli, Livio},
+ title = {Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey},
+ journal = {Energies},
+ volume = {12},
+ year = {2019},
+ number = {11},
+ article-number = {2055},
+ url = {https://www.mdpi.com/1996-1073/12/11/2055},
+ issn = {1996-1073},
+ abstract = {This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.},
+ doi = {10.3390/en12112055}
+}
+
+
+@article{en13020432,
+ author = {Arens, Stefan and Schlüters, Sunke and Hanke, Benedikt and Maydell, Karsten von and Agert, Carsten},
+ title = {Sustainable Residential Energy Supply: A Literature Review-Based Morphological Analysis},
+ journal = {Energies},
+ volume = {13},
+ year = {2020},
+ number = {2},
+ article-number = {432},
+ url = {https://www.mdpi.com/1996-1073/13/2/432},
+ issn = {1996-1073},
+ abstract = {The decarbonization of the energy system will bring substantial changes, from supranational regions to residential sites. This review investigates sustainable energy supply, applying a multi-sectoral approach from a residential site perspective, especially with focus on identifying crucial, plausible factors and their influence on the operation of the system. The traditionally separated mobility, heat, and electricity sectors are examined in more detail with regard to their decarbonization approaches. For every sector, available technologies, demand, and future perspectives are described. Furthermore, the benefits of cross-sectoral integration and technology coupling are examined, besides challenges to the electricity grid due to upcoming technologies, such as electric vehicles and heat pumps. Measures such as transport mode shift and improving building insulation can reduce the demand in their respective sector, although their impact remains uncertain. Moreover, flexibility measures such as Power to X or vehicle to grid couple the electricity sector to other sectors such as the mobility and heat sectors. Based on these findings, a morphological analysis is conducted. A morphological box is presented to summarize the major characteristics of the future residential energy system and investigate mutually incompatible pairs of factors. Lastly, the scenario space is further analyzed in terms of annual energy demand for a district.},
+ doi = {10.3390/en13020432}
+}
+
+
+@article{world1020007,
+ author = {Mendoza, Daniel L. and Bianchi, Carlo and Thomas, Jermy and Ghaemi, Zahra},
+ title = {Modeling County-Level Energy Demands for Commercial Buildings Due to Climate Variability with Prototype Building Simulations},
+ journal = {World},
+ volume = {1},
+ year = {2020},
+ number = {2},
+ pages = {67--89},
+ url = {https://www.mdpi.com/2673-4060/1/2/7},
+ issn = {2673-4060},
+ abstract = {The building sector accounts for nearly 40% of total primary energy consumption in the U.S. and E.U. and 20% of worldwide delivered energy consumption. Climate projections predict an increase of average annual temperatures between 1.1–5.4 °C by 2100. As urbanization is expected to continue increasing at a rapid pace, the energy consumption of buildings is likely to play a pivotal role in the overall energy budget. In this study, we used EnergyPlus building energy models to estimate the future energy demands of commercial buildings in Salt Lake County, Utah, USA, using locally-derived climate projections. We found significant variability in the energy demand profiles when simulating the study buildings under different climate scenarios, based on the energy standard the building was designed to meet, with reductions ranging from 10% to 60% in natural gas consumption for heating and increases ranging from 10% to 30% in electricity consumption for cooling. A case study, using projected 2040 building stock, showed a weighted average decrease in heating energy of 25% and an increase of 15% in cooling energy. We also found that building standards between ASHRAE 90.1-2004 and 90.1-2016 play a comparatively smaller role than variation in climate scenarios on the energy demand variability within building types. Our findings underscore the large range of potential future building energy consumption which depends on climatic conditions, as well as building types and standards.},
+ doi = {10.3390/world1020007}
+}
+
+
+
+@article{owidco2andothergreenhousegasemissions,
+ author = {Hannah Ritchie and Max Roser},
+ title = {CO₂ and Greenhouse Gas Emissions},
+ journal = {Our World in Data},
+ year = {2020},
+ note = {https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions}
}
-@article{Olaniyan2018,
- abstract = {Considering the challenge of accessing reliable household metering data in Nigeria, how can electricity consumption levels be determined? And how do disparities in electricity consumption patterns across the country affect the pursuit of sustainability, universal access and energy transition objectives? This study combined household-reported data on ownership of electrical appliances and energy expenditure with online sales records of household appliances to estimate current and future residential electricity demand in Nigeria, as well as the required generation capacity to achieve 100% electricity access, under various scenarios. Median residential electricity consumption was estimated at 18-27 kWh per capita but these estimates vary between the geographical zones with the North East and SouthWest representing extremes. Under a universal access scenario, the future electricity supply system would be expected to have installed generation capacity sufficient to meet the estimated residential demand of 85 TWh. To further understand the required infrastructure investment as a whole and the approaches that might be preferred in rural versus urban areas, the disaggregated, zone-by-zone and urban/rural data may offer more insight than a whole-of-country approach. The data obtained is useful for identifying specific transitions at the sub-national level that can minimize the required investment while maximizing households' energy access.},
- author = {Olaniyan, Kayode and McLellan, Benjamin C. and Ogata, Seiichi and Tezuka, Tetsuo},
- doi = {10.3390/su10051440},
- file = {:Users/alexanderkell/Downloads/sustainability-10-01440.pdf:pdf},
- isbn = {8180378314},
- issn = {20711050},
- journal = {Sustainability (Switzerland)},
- keywords = {Electricity access,Energy transition,Household survey,Nigeria,Sustainability},
- number = {5},
- title = {{Estimating residential electricity consumption in Nigeria to support energy transitions}},
- volume = {10},
- year = {2018}
+@article{iea_world_energy_balance,
+ author = {IEA},
+ title = {World Energy Balances: Overview},
+ journal = {IEA},
+ year = {2021},
+ note = {https://www.iea.org/reports/world-energy-balances-overview}
}
diff --git a/docs/lecture_04/Lecture_4.1.md b/docs/lecture_04/Lecture_4.1.md
index 156df1a..7c635f2 100644
--- a/docs/lecture_04/Lecture_4.1.md
+++ b/docs/lecture_04/Lecture_4.1.md
@@ -1,37 +1,68 @@
---
-title: Mini-Lecture 4.1 -- Timeslicing in energy systems modelling
+title: Mini-Lecture 4.1 -- Energy technologies
keywords:
-- Timeslices
-- Energy modelling
-- Energy demands
+- Energy technologies
+- Technoeconomic data
authors:
- Alexander J. M. Kell
---
-This mini-lecture provides an overview of timeslicing in energy systems modelling.
+This lecture will introduce the various technologies and how we can represent them within MUSE. We will also learn about the supply chains in which these technologies exist. Finally, we will learn about the key characteristics of the different technologies in the context of MUSE.
# Learning objectives
-- Learn why we use timeslices in energy systems models
-- Understand the importance of representative days
+- Understand the concepts of technologies and supply chains
+
+- Learn how to represent technologies in MUSE
+
+- Understand the key characteristics of technologies
# Introduction
-With energy systems models we must model how demand is met by supply. However, over the course of a year, or even over the course of 30 years we have large variations in demand and supply. For instance, the weather changes between years, seasons, and days. This all has an effect on the amount of energy that can be supplied by renewable energy sources such as solar and wind.
+A technology in MUSE represents a process, or a group of processes, that:
-It is also true that this variation in demand has a large impact on the demand. In a particularly cold year, or on a particular cold day, energy demand may significantly increase as consumers use more energy for heating. The same may be true during a particularly warm period if people need energy for cooling systems. We therefore need to model this variability.
+- Converts energy from one form into another. For example, the conversion of crude oil to oil products, oil products to electricity or electricity to heat.
+- Transfers, transmits or distributes a form of energy, for example electricity transmission technologies.
+- Supplies or produces a form of energy, for example oil imports or extraction, or a hydropower plant generating electricity.
-## Representative days
+## Technology examples
-As you can probably imagine, matching supply and demand for every 30 minutes in a year is very costly in terms of computation time. If we must match supply and demand for every 30 minutes for 30 years (or more), we may end up with a very slow model in return for some gains in accuracy.
+Now we will discuss specific technologies and their role in the energy system.
-However, it may be the case that we do not need to model a year in such high detail. In most cases, for long-term energy systems models, we can reduce the amount of detail to significantly increase the speed of the model, without losing significant accuracy [@Kell2020].
+Within the energy system there exists natural gas for the generation of electricity. However, we have to represent a technology which extracts natural gas in the system. We can call this technology "gas extraction", which outputs natural gas. This technology does not have any input fuel as it is a primary energy supply technology.
-A common approach is to model 4 days for each year. Each day corresponds to a season of the year and is split into 24 timeslices (which equates to a timeslice representing one hour). Therefore, we maintain the variability within a day, but also within seasons. We will lose some of the extremely hot or cold days, but that matters less when we're considering the long-term planning horizon.
+A coal power plant, on the other hand, has an input of coal and an output commodity of electricity. This technology is an energy conversion technology and converts the energy in coal to electricity.
-We do not always have to take into account entire days, to reduce the complexity further. For instance, we could have 8 days, but with only 2 timeslices (day and night). This will make the model run quickly, but may lose some detail. It is up to you, as the modeller, to find a sweet spot between accuracy and speed of computation. Various papers have been published to find this sweet spot, which you can look into in your own time [@Poncelet2017].
+Similarly, an oil power plant converts the energy in oil to electricity. It therefore has an input fuel of oil and an output commodity of electricity.
-# Summary
+It must be noted that some technologies can have more than one input or output fuel, such as a refinery with oil as the input fuel, producing both gasoline and heavy fuel oil as output fuels.
+
+## Parameters that define technologies
+
+There are three main groups of parameters that are used to define technologies. These can be seen in Figure 4.1.1 below. These include input commodities, which refer to the fuel supply to the technology. For instance, what is the input fuel, what is the price of this, and what is the availability? Crucially, it can also contain the greenhouse gas emissions associated with the fuel.
+
+Secondly, there is techno-economic and environmental characteristics of technologies. These include technology costs, efficiency, lifetime and availability.
+
+Finally, we need to define each technology's output commodity. This is the commodity which it produces, such as electricity from solar PV. Important data on output commodities includes their demand, impacts and when it is needed.
+
+{width=100%}
-In this mini-lecture we discovered why long-term energy models consider timeslices and representative days. Through this approach we are able to maintain high accuracy whilst also reducing computation time.
+**Figure 4.1.1:** Technology definitions by example parameters [@Taliotis2018]
+
+## Representing technologies in MUSE
+
+Since models are abstractions of reality, we can define technologies at different levels of abstraction depending on the nature of our energy model. Within MUSE, for instance, a single technology can represent a single power plant, or a group of similar power plants (for example, a technology could represent all coal power plants in a region if they had similar characteristics). The information provided can create a model with more or less granular data based upon the requirements of the user. It must be noted, that with increased granularity, an increase in computation time will be observed.
+
+It is possible within MUSE to represent all power plants as a single technology. This is appropriate when technologies do not change significantly between power plants or extraction plants.
+
+## Key characteristics of technologies
+
+There are a number of different important technology characteristics that should be considered in capacity expansion planning. MUSE allows for several of these characteristics to be included. Such as:
+
+- Variation in the availability, efficiency and costs of a technology over short and long timescales. For example, it may be the case that solar power reduces in costs over the next 30 years. If this happens, we would like to model this process and see the long-term effect on the market.
+- MUSE can consider the limits on production by technology and capacity constraints. For example, there may only be a certain amount of hydro resources in a particular country, based on the number of rivers etc. It is important that MUSE takes this into account to ensure that the results are aligned to the reality in a region or country.
+- Finally, the emissions associated with technologies can be captured. For example, we may want to reduce the carbon dioxide emissions of an entire system. This would allow us to compare scenarios and enable us to understand how we can reduce these emissions to reduce the impact of climate change. MUSE is also able to impose a limit on emissions through a constraint.
+
+# Summary
+In this mini-lecture we have learned the importance of technologies within MUSE. We learnt that a technology can refer to a single power plant, to all coal power plants, for example. This is largely based on the requirements of individual case studies. We also learnt that technologies can also be processes, such as the extraction of natural gas. All of these different technologies come together to build an entire energy system, which MUSE is able to model.
diff --git a/docs/lecture_04/Lecture_4.2.md b/docs/lecture_04/Lecture_4.2.md
index a97bf69..4822973 100644
--- a/docs/lecture_04/Lecture_4.2.md
+++ b/docs/lecture_04/Lecture_4.2.md
@@ -1,37 +1,62 @@
---
-title: Mini-Lecture 4.2 - Technologies by timeslice
+title: Mini-Lecture 4.2 -- Technoeconomic characteristics
keywords:
-- Energy technologies
-- Energy modelling
-- Timeslices
+- Technoeconomic data
+- Parametrisation
+
authors:
- Alexander J. M. Kell
---
-In this mini-lecture we describe how different technologies can have different characteristics by timeslices.
+This mini-lecture will describe the techno-economic data that defines technologies in MUSE. These technoeconomics are fundamental to the functioning of a good MUSE model. Most technologies can be characterised by their efficiencies, technoeconomics and inputs and outputs. This is because the technologies must be competitive against each other in an economic sense.
# Learning objectives
-- Understand the different characteristics of technologies by timeslice
-- Understand how to characterise technologies by timeslice
+- Understand the main technoeconomic parameters
+- Understand how these parameters can impact investment decisions
-# Introduction
+# Technology costs
-In the previous lecture we discovered the importance of timeslices. In this mini-lecture we will learn about how different technologies have different characteristics when it comes to timeslices, and how this can be modelled within MUSE.
+In this mini-lecture we will describe the different techno-economic parameters that MUSE defines, primarily in the `Technoeconomic.csv` file found in the different sector folders.
+Figure 4.2.1 displays the different cost types as defined in MUSE. The total costs are largely split into capital costs and annual costs. Capital costs, as shown by the figure, are the costs of depreciation, return on investment and other one-time fixed charges. This can include the initial costs of the technology such as construction.
-# Technologies by timeslices
+Then there are annual costs, which are split into variable and fixed costs. There is a distinction between these two types of costs, where fixed costs depend on the capacity of the power plant, whereas variable costs depend on the amount of energy output in a year. For instance, if a power plant does not output any electricity, it will not have to pay for fuel. However, it will still have to pay for salaries to look after the plant.
-Different technologies and supply sectors have different characteristics when it comes to timeslices. For instance, solar photovoltaics do not produce any energy when it is dark (for instance, at night) and produce less in the winter. Wind, on the other hand, has a completely different profile and is largely dependent on geography. Therefore, it would make sense to provide a maximum output of the technologies at different times. For instance, it would be useful if the model limited solar output at night time in the form of a maximum utilization factor. Where utilization factor is the ratio of average amount of energy output to total possible output of an energy technology if it were to run 100% of time.
+{width=100%}
-However, it can be very difficult to turn off some technologies, such as a nuclear power plant. Nuclear power plants are expensive to turn on and can be unsafe if constantly varying their power. Also, their marginal cost, or the cost to produce 1MWh of electricity excluding capital costs, is usually much lower than other power plants such as gas or coal plants. It, therefore, makes sense that we place a minimum service factor, or minimum output allowed, on nuclear, to ensure their output does not fall below a certain level.
+**Figure 4.2.1:** Cost types [@Taliotis2018]
-Other technologies, however, such as gas power plants, can be turned on and off readily; therefore we can simply leave an average utilization factor for all the timeslices.
+In MUSE, these are defined by the `cap_par`, `cap_exp`, `fix_par`, `fix_exp`, `var_par`, and `var_exp` variables where:
-All of these features exist in MUSE, and during this lecture's hands-on, we will show you how to do this within MUSE.
+- `cap_par` is the capital costs, and `cap_exp` is the exponential component of this. Effectively, the `cap_exp` defines the reduction in cost due to economies of scale as the investment into this technology and its capacity increases. This should be a number between 0 and 1.
+- `fix_par` is the fixed costs, and `fix_exp` is the exponential component similar to the exponential component in `cap_exp`.
+- `var_par` is the fixed costs, and `var_exp` is the exponential component.
-# Summary
+The exponential component can be chosen from relevant data, but can often by difficult to find. In that case it is okay to use a number such as 1 or 0.95 as a rough indication.
+
+## Growth constraints
+
+As previously mentioned, it is important to place realistic constraints on the growth of technologies. For instance, there is only so much resource or land potential for renewable energy resources, such as offshore wind. If a country or region does not have any access to land offshore, the limit for offshore wind should be zero. On top of this, it may not be possible to grow and install technologies faster than a certain rate. For instance, there may not be enough resources, such as steel and labour, to double the capacity of wind in a certain country.
+
+The parameters which set these can be found in the `Technodata.csv` file and are called:
+
+- `MaxCapacityGrowth`
+- `MaxCapacityAddition`
+- `TotalCapacityLimit`
-In this mini-lecture we have explored the importance of characterising technologies not just by their economic data, but also by their physical characteristics. We discovered that different technologies have different outputs at different times, such as solar and wind. We also found out that nuclear power, for instance, must output a certain level to remain within a safety range.
+## Other technoeconomic parameters
+Other technoeconomic parameters include the lifetime of a technology, scaling size and interest rate. A technology may become much more attractive if we are able to use it for a longer amount of time. For instance, the economics of nuclear power plants can be very sensitive to the length of time they can be used for due to their high capital costs. It is therefore important that we have good data on the lifetime of the plant. This is set by the `TechnicalLife` parameter.
+
+The scaling size defines how small a single unit can be. For instance, a single nuclear power plant outputs a lot more energy than a single solar photovoltaic panel. This detail can be set by the `ScalingSize` parameter.
+
+The interest rate is the parameter which defines the discount rate. For instance, a technology may have a 2% return on investment, which may seem good. But it could also be possible to put the money required to build a technology into a high interest savings account and have a 4% investment. Thus the 2% return would actually reflect a loss relative to the rate of interest. This opportunity cost is the interest rate defined in the `InterestRate` parameter.
+
+## Inputs and outputs
+
+Finally, there are the input and output parameters. For a gas power plant, the input is gas and the end use is electricity. This can be set in the `Fuel` and `EndUse` parameters respectively.
+
+# Summary
+ In this mini-lecture we have discovered the main components which make up the Technodata sheet. We discovered the importance of properly defining the costs, lifetime and other characteristics which have a large impact on the final investment decisions.
diff --git a/docs/lecture_04/Lecture_4.3.md b/docs/lecture_04/Lecture_4.3.md
index 6ba9147..b38090a 100644
--- a/docs/lecture_04/Lecture_4.3.md
+++ b/docs/lecture_04/Lecture_4.3.md
@@ -1,39 +1,56 @@
---
-title: Mini-Lecture 4.3 - Different energy demands by timeslice
+title: Mini-Lecture 4.3 -- Input and output commodities
keywords:
-- Energy demands
-- Timeslice
-- Energy modelling
+- Technology efficiency
+- Input commodities
+- Output commodities
authors:
- Alexander J. M. Kell
---
-This mini-lecture will continue exploring the importance of timeslices in energy modelling; however, it will have a particular focus on energy demands, and how these can change by timeslice and over the years.
-
-In the previous lecture we explored energy demands and timeslices. In this lecture we will have a brief recap of this, and explore how energy demand can be represented within MUSE.
+In this mini-lecture we will learn about the input and output commodities within MUSE. Specifically we will learn what the `CommIn.csv` and `CommOut.csv` files do and how these relate to the energy system.
# Learning objectives
-- Understand how energy demand can change by timeslice
-- Learn how energy demand is represented in MUSE
+- To learn the importance of input and output commodities
+- To learn how we can modify these commodities in MUSE
-# Energy demand
+# Introducing commodities
-Energy demand can come in various forms. For instance, the demand we model can be for heating or cooling in the residential sector. It is the case that these demands have different characteristics. For instance, they may have different magnitudes and different technologies which serve these demands as well as they may be able to run at different times.
+Input commodities are the commodities consumed by each technology. This could be coal for a coal power plant, uranium for a nuclear power plant or electricity for an electric heater. This is dependent on the technology, and some technologies can have multiple inputs.
-Within MUSE, similarly to the supply sectors, we can model this time varying capability with timeslices. For instance, if we have 4 representative days which refer to the different seasons, we can model the high heating demand in winter and cooling demand in summer. On top of this we can vary these demands by time of day.
+Output commodities are similar, but are the outputs of technologies. For example the output of any power plant will be electricity, and for heaters the output will be heat. Again, this is dependent on the technology, and some technologies can have multiple outputs such as combined heat and power plants.
-To do this, we must edit the demand in the `preset/Residential2050Consumption.csv` sector. An example of which is shown in Figure 4.3.1.
+The ratio between these two parameters is very important in MUSE and in energy modelling in general. This is because it defines the efficiency of the technology. For instance, if a coal power plant requires 1 PJ of energy stored in coal to output 0.8 PJ of electricity, the coal power plant has an efficiency of 0.8. The higher the efficiency the more economical the power plant is and the more competitive it will be when compared to different technologies.
-{width=100%}
+## Editing the CommIn and CommOut files
-**Figure 4.3.1:** Example input for the preset sector.
+Within MUSE there are two files which one should change to edit these parameters: the `CommIn.csv` and `CommOut.csv` files. These files are found within the sector folders of the case study. For instance, in the `power/CommIn.csv` or `gas/CommOut.csv` directories.
-In this small example we see that there is only a demand for `heat` in the residential sector. However, this demand changes per timeslice (which are listed in the leftmost column). For instance, there is low demand for heat in timeslice 0 and a high demand for heat in timeslice 4. These timeslices refer to a single representative day, and therefore timeslice 4 has the highest demand for heat as it is in the late-evening, when people generally come home from work and turn on their radiators.
+In this example we will look at the residential sectors `CommIn.csv` and `CommOut.csv` files. An example `CommIn.csv` file can be seen in the figure below:
-In your models you can use datasets to disaggregate the demand into different types, or you can aggregate demand to include all gas or electricity utilised in the residential sector. This is largely dependent on the data available and the complexity of the model you would like.
+|ProcessName|RegionName|Time|electricity|gas|heat|CO2f|wind|
+|-----------|----------|----|-----------|---|----|----|----|
+|Unit|-|Year|PJ/PJ|PJ/PJ|PJ/PJ|kt/PJ|PJ/PJ|
+|gasboiler|R1|2020|0|1.67|0|0|0|
+|heatpump|R1|2020|0.4|0|0|0|0|
-# Summary
+**Figure 4.3.1:** CommIn file for the residential sector
+
+Here we see two technologies: `gasboiler` and `heatpump`. They are both in region R1 and we are specifying the characteristics for the year 2020. The `gasboiler` only requires gas, but requires 1.16 PJ, whereas the `heatpump` requires only 0.4 PJ to produce some energy.
+
+However, it is important to note that these figures are meaningless without the `CommOut.csv` file. We need to know how much energy does the 1.16 PJ of energy produce in the `gasboiler`? As can be seen in the figure below showing an example `CommOut.csv` file, it is convention to select an output of 1. That way we only have to vary the `CommIn.csv` to change the efficiencies consistently.
-In this mini-lecture, we explored the importance of timeslicing for modelling demand in energy models. We also covered how this can be done within MUSE using the preset sector.
+|ProcessName|RegionName|Time|electricity|gas|heat|CO2f|wind|
+|-----------|----------|----|-----------|---|----|----|----|
+|Unit|-|Year|PJ/PJ|PJ/PJ|PJ/PJ|kt/PJ|PJ/PJ|
+|gasboiler|R1|2020|0|0|1|64.71|0|
+|heatpump|R1|2020|0|0|1|0|0|
+
+**Figure 4.3.1:** CommOut file for the residential sector
+
+Therefore, we can now conclude that the `heatpump` is much more efficient than the `gasboiler` as only 0.4 PJ are required to output 1 PJ of heat. If we divide 1 by 0.4, we get the efficiency of the `heatpump`, where 1/0.4= 2.5. Notice that the `gasboiler` also outputs carbon dioxide. It is important to take these emissions into account to have a complete understanding of the energy system. MUSE calculates these emissions endogenously.
+
+# Summary
+This mini-lecture has explored the input and output commodities in MUSE. We have learnt that the `CommIn.csv` and `CommOut.csv` files relate to efficiencies when brought together in a ratio.
diff --git a/docs/lecture_04/Lecture_4.4.md b/docs/lecture_04/Lecture_4.4.md
index 53eab1f..653adda 100644
--- a/docs/lecture_04/Lecture_4.4.md
+++ b/docs/lecture_04/Lecture_4.4.md
@@ -1,34 +1,56 @@
---
-title: Mini-Lecture 4.4 -- Timeslicing and climate policy
+title: Mini-Lecture 4.4 -- Interpolation and future years
keywords:
-- Climate policy
-- Timeslicing
+- Interpolation
+- Energy technologies
authors:
- Alexander J. M. Kell
---
-This mini-lecture explores the relevance of timeslicing to climate policy. We will explore how different timeslicing can affect modelling results, why it is important to consider realistic timeslicing and how these can affect policy decisions.
+MUSE is flexible in its approach. It requires inputs for at least the base year, but does not necessarily need more than that to project forward. In this mini-lecture we will cover how MUSE deals with missing data and how to model future years
# Learning objectives
-- Understand the impact of timeslicing on modelling outputs
-- Learn how timeslicing can affect policy decisions
+- Learn how to model costs in multiple years
+- Understand how MUSE deals with missing data
+- Understand interpolation
-# Timeslicing and policy
+# Introduction
-Timeslicing is a core component of an energy systems model as we have previously discussed. If one were to use an inappropriate number of timeslices in an energy systems model, it is likely that this would have major implications on the model outputs.
+Within the input sheets you may have noticed the `Time` column. In the default example this is set to 2020. However, what happens beyond these years if we do not specify a cost, for example? Also, what happens in 2030 if we only specify a cost in 2020 and 2040?
-Let's look at an example: if we were to model solar panels with an average capacity factor for the entire time horizon of the model this would assume that the solar panels can be used at night and could displace other technologies, such as gas turbines. However, in reality, solar panels contribute to the grid during the day and produce nothing at night. Therefore, we need some sort of flexibility in the system to ramp up after the sun sets. This needs to be modelled explicitly within MUSE, so to allow gas (or other technologies) to fill this gap in supply.
+Within MUSE, we make some assumptions. We assume that if there are no costs input into a model beyond a certain year, that the costs remain the same. This is known as a flat-forward extension. If, for example, we input costs in 2020 and 2040, we will interpolate the values in between these years linearly.
-If we take this conclusion further, it is possible to see scenarios where the intermittency of solar and wind are not modelled, and therefore we observe scenarios with a majority in solar or wind. With current technologies this is not possible, and this therefore underscores the importance of timeslicing.
+An example of this is, say that the capital costs for a gas boiler is set to be 4 for a gas boiler in 2020 and 2 in 2040. We have not explicitly defined 2025, 2030 or 2035. Based on linear interpolation, MUSE will assume a value of 2.5 for 2025, 3 for 2030 (halfway between the year 2020 and 2040) and 3.5 for 2035.
-If we do not use accurate timeslicing then the model outputs can skew resulting policy, and so due care must be taken for sourcing data from different geographies.
+It must be noted, however, that MUSE does not allow a user to just update a single technology. For instance, if we want to specify the technology costs in 2035 for a coal power plant, we must also define the technology costs for every other technology in 2035 – although this cost need not be changed from the original value. We also do not need to define every year, however, as interpolation and a flat-forward extension can still be used.
-# Summary
+## Practical example
+
+The figure below shows a snippet of the technodata file for the residential sector. We can see that we have data parametrising the technologies in 2020.
+
+|ProcessName|RegionName|Time|cap_par|cap_exp|…|
+|-|-|-|-|-|-|
+|Unit|-|Year|MUS$2010/PJ_a|-|…|
+|gasboiler|R1|2020|3.8|1|…|
+|heatpump|R1|2020|8.866667|1|…|
-In this lecture we have looked into the implications of different timeslicing decisions made when creating an energy systems model. We learnt that if we do not get this right, the investments made could be skewed and unrealistic.
+**Figure 4.4.1:** Technodata for residential sector
+Let's say that we want to update the capital costs (`cap_par`) for heat pumps in 2040, but do not want to update the prices for gasboilers. This is how we do it:
+|ProcessName|RegionName|Time|cap_par|cap_exp|…|
+|-|-|-|-|-|-|
+|Unit|-|Year|MUS$2010/PJ_a|-|…|
+|gasboiler|R1|2020|3.8|1|…|
+|heatpump|R1|2020|8.866667|1|…|
+|gasboiler|R1|2040|3.8|1|…|
+|heatpump|R1|2040|5|1|…|
+**Figure 4.4.2:** Updated technodata for residential sector
+Notice that we need separate rows for both `heatpump` and `gasboiler` even though we are only making a change in the `heatpump` capital cost. If we do not do this we will encounter an error. In between 2020 and 2040 we will get interpolation.
+
+# Summary
+In this mini-lecture we learned how to update costs in the time domain, and the assumptions MUSE makes if we do not give costs for every year. Namely, flat-forward extension and interpolation. We also learnt how to practically input these values in MUSE with the `Technodata.csv` file.
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diff --git a/docs/lecture_04/bibliography.bib b/docs/lecture_04/bibliography.bib
index a0fb1e6..f706272 100644
--- a/docs/lecture_04/bibliography.bib
+++ b/docs/lecture_04/bibliography.bib
@@ -1,22 +1,9 @@
-@article{Kell2020,
- author = {Kell, Alexander J. M. and Forshaw, Matthew and McGough, A. Stephen},
- isbn = {9781450366717},
- journal = {The Eleventh ACM International Conference on Future Energy Systems (e-Energy'20)},
- keywords = {agent-based modelling,en-,energy market simulation,ergy models,genetic algorithm,long-term,op-,policy,simulation,validation},
- mendeley-groups = {Energy Modelling},
- title = {{Long-Term Electricity Market Agent Based Model Validation using Genetic Algorithm based Optimization}},
- year = {2020}
-}
-@article{Poncelet2017,
- abstract = {Due to computational restrictions, energy-system optimization models (ESOMs) and generation expansion planning models (GEPMs) frequently represent intra-annual variations in demand and supply by using the data of a limited number of representative historical days. The vast majority of the current approaches to select a representative set of days relies on either simple heuristics or clustering algorithms and comparison of different approaches is restricted to different clustering algorithms. This paper contributes by: (i) proposing criteria and metrics for evaluating representativeness, (ii) providing a novel optimization-based approach to select a representative set of days and (iii) evaluating and comparing the developed approach to multiple approaches available from the literature. The developed optimization-based approach is shown to achieve more accurate results than the approaches available from the literature. As a consequence, by applying this approach to select a representative set of days, the accuracy of ESOMs/GEPMs can be improved without increasing the computational cost. The main disadvantage is that the approach is computationally costly and requires an implementation effort.},
- author = {Poncelet, Kris and Hoschle, Hanspeter and Delarue, Erik and Virag, Ana and Drhaeseleer, William},
- issn = {08858950},
- journal = {IEEE Transactions on Power Systems},
- keywords = {Energy-system planning,generation expansion planning,power system economics,power system modeling,wind energy integration},
- mendeley-groups = {Electricity Market Simulations/Selecting Representative RES Days},
- number = {3},
- pages = {1936--1948},
- title = {{Selecting representative days for capturing the implications of integrating intermittent renewables in generation expansion planning problems}},
- volume = {32},
- year = {2017}
+@article{Taliotis2018,
+ abstract = {Defining final energy demands },
+ author = {Taliotis, Constantinos and Gardumi, Francesco and Shivakumar, Abhishek and Sridharan, Vignesh and Ramos, Eunice and Beltramo, Agnese and Rogner, Holger and Howells, Mark},
+ file = {:Users/alexanderkell/Downloads/Defining final energy demands in OSeMOSYS.pdf:pdf},
+ keywords = {Demand,Energy,Energy System,Modelling,Osemosys},
+ number = {January},
+ title = {{Defining final energy demands in OSeMOSYS}},
+ year = {2018}
}
diff --git a/docs/lecture_05/Lecture_5.1.md b/docs/lecture_05/Lecture_5.1.md
index d4a2479..148d571 100644
--- a/docs/lecture_05/Lecture_5.1.md
+++ b/docs/lecture_05/Lecture_5.1.md
@@ -1,71 +1,92 @@
---
-title: Mini-Lecture 5.1 -- Energy technologies
+title: Mini-Lecture 5.1 -- Energy demands in energy systems modelling
keywords:
-- Energy technologies
-- Technoeconomic data
+- Energy demand
+- Energy systems models
authors:
- Alexander J. M. Kell
---
-This lecture will introduce the various technologies and how we can represent them within MUSE. We will also learn about the supply chains in which these technologies exist. Finally, we will learn about the key characteristics of the different technologies in the context of MUSE.
+This mini-lecture provides an overview of energy demands within an energy system. We will cover differences in energy demands by sector, time and population classes. We will also begin to explore why these differences are important within energy models. The remaining mini-lectures will take you through the basics for modelling energy demand in MUSE, the different options available to do so, and some specific examples
# Learning objectives
-- Understand the concepts of technologies and supply chains
-
-- Learn how to represent technologies in MUSE
-
-- Understand the key characteristics of technologies
+- Learn what energy demands are in an energy modelling context
+- Understand how demands can change based on different variables
# Introduction
-A technology in MUSE represents a process, or a group of processes, that:
-
-- Converts energy from one form into another. For example, the conversion of crude oil to oil products, oil products to electricity or electricity to heat.
-- Transfers, transmits or distributes a form of energy, for example electricity transmission technologies.
-- Supplies or produces a form of energy, for example oil imports or extraction, or a hydropower plant generating electricity.
-
-## Technology examples
+Everyone needs energy for many different purposes. The form in which this energy should be delivered is dependent on the specific application. These demands for energy come from all sectors of society such as:
+
+- The residential sector (rural and urban)
+ - Cooking
+ - Heating
+ - Cooling
+ - Lighting
+ - Appliances
+- Industry
+ - Chemical processes
+ - Steam production
+ - Heating
+- Commerce
+ - Lighting
+ - Heating
+ - Cooling buildings
+ - Keeping products at low temperatures
+- Transport
+ - Cars
+ - Trucks
+ - Buses
+ - Aviation
+ - Shipping
+ - Trains
+- Agriculture
+ - Tractors
+ - Machinery
+ - Pumping water
+
+## Variations in daily energy demand
+
+These energy demands can vary on hourly, daily, weekly and monthly timescales. This mainly reflects the schedule of consumers' activities. For example, on a monthly timescale more cooling will be used in summer and more heating in winter. However, these energy demands can also vary by sector, as shown by Figure 5.1.1.
-Now we will discuss specific technologies and their role in the energy system.
+{width=100%}
-Within the energy system there exists natural gas for the generation of electricity. However, we have to represent a technology which extracts natural gas in the system. We can call this technology "gas extraction", which outputs natural gas. This technology does not have any input fuel as it is a primary energy supply technology.
+**Figure 5.1.1:** Variations of energy demand by sector in a hypothetical example [@Taliotis2018].
-A coal power plant, on the other hand, has an input of coal and an output commodity of electricity. This technology is an energy conversion technology and converts the energy in coal to electricity.
+Figure 5.1.1 shows us that the magnitude of demand varies by sector, with agricultural demand significantly lower than residential and commercial demand, in this example. The reason that the commercial and residential sectors consume more is because their activities are more energy intensive or they are simply larger.
-Similarly, an oil power plant converts the energy in oil to electricity. It therefore has an input fuel of oil and an output commodity of electricity.
+We can also see that the daily profile of demand varies by sector. For example, in Figure 5.1.1 we can see that there is a clear evening peak in residential demand, whereas agricultural and industrial demand remains flat throughout the day. This is because agricultural and industrial demands are consistent throughout the day. This is likely because the industrial and agricultural sector operate constantly, whereas energy use in homes peaks in the evening when consumers use more electricity for cooking, lighting and appliances when they return from work or other business.
-It must be noted that some technologies can have more than one input or output fuel, such as a refinery with oil as the input fuel, producing both gasoline and heavy fuel oil as output fuels.
+## Sector specific demands
-## Parameters that define technologies
+The differences between sectors means that it can sometimes be important to model demands separately by each sector. This feature allows the models to consider the specific characteristics of each demand.
-There are three main groups of parameters that are used to define technologies. These can be seen in Figure 5.1.1 below. These include input commodities, which refer to the fuel supply to the technology. For instance, what is the input fuel, what is the price of this, and what is the availability? Crucially, it can also contain the greenhouse gas emissions associated with the fuel.
+Within each of these sectors, the energy demand varies over time and across different types of consumers. For example, within the residential sector, demands can differ between rural and urban households, as shown in Figure 5.1.2. This can also be true between grid-connected and off-grid areas. Energy planners must ensure that energy demand is always met for all types of consumers. Therefore, it is important that the key characteristics of different demands are represented in energy models.
-Secondly, there is techno-economic and environmental characteristics of technologies. These include technology costs, efficiency, lifetime and availability.
+{width=100%}
-Finally, we need to define each technology's output commodity. This is the commodity which it produces, such as electricity from solar PV. Important data on output commodities includes their demand, impacts and when it is needed.
+**Figure 5.1.2:** Variations of energy demand for the residential sector by population types [@Olaniyan2018]
-{width=100%}
+## Long-term variations in energy demands
-**Figure 5.1.1:** Technology definitions by example parameters [@Taliotis2018]
+A major challenge in energy planning is that energy demands can change over time. This could be due to population growth or the creation of new industries. Figure 5.1.3 displays historical variations in energy demands. It is likely that these demands are correlated to changes in society. For example, increases in energy demand likely reflect increased industrial activity. For energy planning, we must also think about how energy demands are likely to change in the future.
+We can often forecast energy demand, such as with future projections as shown in Figure 5.1.3. These forecasts can be created using estimates of the key influencers of energy demand, such as population growth and economic activity. Future projections are often based on how energy demands have changed historically.
-## Representing technologies in MUSE
+{width=100%}
-Since models are abstractions of reality, we can define technologies at different levels of abstraction depending on the nature of our energy model. Within MUSE, for instance, a single technology can represent a single power plant, or a group of similar power plants (for example, a technology could represent all coal power plants in a region if they had similar characteristics). The information provided can create a model with more or less granular data based upon the requirements of the user. It must be noted, that with increased granularity, an increase in computation time will be observed.
+**Figure 5.1.3:** Long-term energy consumption by source
-It is possible within MUSE to represent all power plants as a single technology. This is appropriate when technologies do not change significantly between power plants or extraction plants.
+## Capacity expansion planning
-## Key characteristics of technologies
+One of the key purposes of MUSE is for capacity expansion. Figure 5.1.4 displays this key issue which MUSE can address. Essentially, if total demand increases (green line) and existing system capacities are retired (blue line), how can we invest to meet the energy capacity needed to supply demand (red line)?
-There are a number of different important technology characteristics that should be considered in capacity expansion planning. MUSE allows for several of these characteristics to be included. Such as:
+{width=100%}
-- Variation in the availability, efficiency and costs of a technology over short and long timescales. For example, it may be the case that solar power reduces in costs over the next 30 years. If this happens, we would like to model this process and see the long-term effect on the market.
-- MUSE can consider the limits on production by technology and capacity constraints. For example, there may only be a certain amount of hydro resources in a particular country, based on the number of rivers etc. It is important that MUSE takes this into account to ensure that the results are aligned to the reality in a region or country.
-- Finally, the emissions associated with technologies can be captured. For example, we may want to reduce the carbon dioxide emissions of an entire system. This would allow us to compare scenarios and enable us to understand how we can reduce these emissions to reduce the impact of climate change. MUSE is also able to impose a limit on emissions through a constraint.
+**Figure 5.1.4:** Capacity expansion [@Taliotis2018]
+You may notice that the red line is higher than the green line at all points. This is due to losses due to lower generating efficiencies. The gap between the red and blue lines demonstrates the required capacity expansion over time. MUSE enables us to plan such a capacity expansion whilst considering technical, economic and environmental constraints.
# Summary
-In this mini-lecture we have learned the importance of technologies within MUSE. We learnt that a technology can refer to a single power plant, to all coal power plants, for example. This is largely based on the requirements of individual case studies. We also learnt that technologies can also be processes, such as the extraction of natural gas. All of these different technologies come together to build an entire energy system, which MUSE is able to model.
-
+In this mini-lecture we covered the differences between energy demands in different population types, sectors and timescales. We learnt why it is important to model these differences in demand in energy systems models. We also explored how energy systems models can be used to meet a changing demand profile in the future.
diff --git a/docs/lecture_05/Lecture_5.2.md b/docs/lecture_05/Lecture_5.2.md
index aad9c9f..f5902be 100644
--- a/docs/lecture_05/Lecture_5.2.md
+++ b/docs/lecture_05/Lecture_5.2.md
@@ -1,66 +1,53 @@
---
-title: Mini-Lecture 5.2 -- Technoeconomic characteristics
+title: Mini-Lecture 5.2 -- Energy demands in modelling
keywords:
-- Technoeconomic data
-- Parametrisation
-
+- Energy demands
+- Scenario analysis
authors:
- Alexander J. M. Kell
---
-This mini-lecture will describe the techno-economic data that defines technologies in MUSE. These technoeconomics are fundamental to the functioning of a good MUSE model. Most technologies can be characterised by their efficiencies, technoeconomics and inputs and outputs. This is because the technologies must be competitive against each other in an economic sense.
+This mini-lecture outlines the general requirements for defining energy demands and how modelling different scenarios can help assess potential future energy demand.
# Learning objectives
-- Understand the main technoeconomic parameters
-- Understand how these parameters can impact investment decisions
-
-# Technology costs
-
-In this mini-lecture we will describe the different techno-economic parameters that MUSE defines, primarily in the `Technoeconomic.csv` file found in the different sector folders.
-
-Figure 5.2.1 displays the different cost types as defined in MUSE. The total costs are largely split into capital costs and annual costs. Capital costs, as shown by the figure, are the costs of depreciation, return on investment and other one-time fixed charges. This can include the initial costs of the technology such as construction.
-
-Then there are annual costs, which are split into variable and fixed costs. There is a distinction between these two types of costs, where fixed costs depend on the capacity of the power plant, whereas variable costs depend on the amount of energy output in a year. For instance, if a power plant does not output any electricity, it will not have to pay for fuel. However, it will still have to pay for salaries to look after the plant.
+- Understand how to define energy demands
+- Understand why we need scenario analysis
-{width=100%}
+# Introduction
-**Figure 5.2.1:** Cost types [@Taliotis2018]
+Within modelling we can break up the previously defined energy demands by sector. Electricity comes from the power sector and can be used to fulfil demand from each of the final service sectors. For example, the residential, commercial or industrial sector.
-In MUSE, these are defined in the `cap_par`, `cap_exp`, `fix_par`, `fix_exp`, `var_par`, and `var_exp` variables where:
+These sectors can have different electricity demands and needs and which can evolve over time as was seen in the last mini-lecture. We will now explore how these energy demands can be defined.
--- `cap_par` is the capital costs, and `cap_exp` is the exponential component of this. Effectively, the `cap_exp` defines the reduction in cost due to economies of scale as the investment into this technology and its capacity increases. This should be a number between 0 and 1.
--- `fix_par` is the fixed costs, and `fix_exp` is the exponential component similar to the exponential component in `cap_exp`.
--- `var_par` is the fixed costs, and `var_exp` is the exponential component.
+## Defining energy demands
-The exponential component can be chosen from relevant data, but can often by difficult to find. In that case it is okay to use a number such as 1 or 0.95 as a rough indication.
+When defining an energy demand for energy systems models, it is important to identify the following:
-## Growth constraints
+- The energy carrier which the demand arises for. For example, electricity, gasoline for transportation or biomass for cooking.
+- The sector the demand arises from. For example, residential (urban and/or rural, off- or on-grid), industrial or commercial.
+- The average variability of the demand within a year. This is usually expressed using average demand profiles, which are explained in more detail later in this lecture.
+- The current and expected future annual average demand.
-As previously mentioned, it is important to place realistic constraints on the growth of technologies. For instance, there is only so much resource or land potential for renewable energy resources, such as offshore wind. If a country or region does not have any access to land offshore, the limit for offshore wind should be zero. On top of this, it may not be possible to grow and install technologies faster than a certain rate. For instance, there may not be enough resources, such as steel and labour, to double the capacity of wind in a certain country.
+However, it is very difficult to predict future demand, and there will always be uncertainty in our predictions. Due to this it is important to model different scenarios.
-The parameters which set these can be found in the `Technodata.csv` file and are called:
+## Defining our own energy demand
-- `MaxCapacityGrowth`
-- `MaxCapacityAddition`
-- `TotalCapacityLimit`
+As has just been seen, when we want to define our own energy demand, we need to identify a number of different features. Let's say, for example, that we want to define the demand for electricity in urban homes. To do this, we need to define:
-## Other technoeconomic parameters
+- The energy carrier for which the demand arises for. In this case it is electricity.
+- The sector the demand arises from. In this example it is the residential sector, or the urban residential sector if you would like to be more specific.
+- The average variability of the demand over the year. In this example we can look at daily and yearly electricity demand profiles for a residential urban area. This will tell us how the demand varies on a daily and seasonal scale.
+- Current and predicted future demand. For this, we can look at an energy balance (covered in more detail later) to get data for the current and historical residential electricity demand. We can use these data as a baseline, and we could combine it with an estimate of population growth to create a future projection for the demand.
-Other technoeconomic parameters include the lifetime of a technology, scaling size and interest rate. A technology may become much more attractive if we are able to use it for a longer amount of time. For instance, the economics of nuclear power plants can be very sensitive to the length of time they can be used for due to their high capital costs. It is therefore important that we have good data on the lifetime of the plant. This is set by the `TechnicalLife` parameter.
+## Scenario analysis
-The scaling size defines how small a single unit can be. For instance, a single nuclear power plant outputs a lot more energy than a single solar photovoltaic panel. This detail can be set by the `ScalingSize` parameter.
-
-The interest rate is the parameter which defines the discount rate. For instance, a technology may have a 2% return on investment, which may seem good. But it could also be possible to put the money required to build a technology into a high interest savings account and have a 4% investment. Thus the 2% return would actually reflect a loss relative to the rate of interest. This opportunity cost is the interest rate defined in the `InterestRate` parameter.
-
-## Inputs and outputs
-
-Finally, there are the input and output parameters. For a gas power plant, the input is gas and the end use is electricity. This can be set in the `Fuel` and `EndUse` parameters respectively.
+Within energy systems modelling, we must explore different possibilities of what could happen in the future. This is known as scenario analysis. We do this as the future is uncertain, particularly over the long-term horizon. We therefore might want to consider multiple scenarios to assess how demand could vary in the future.
+For example, for different scenarios, key predictors of energy demand, such as population growth, economic development and energy policy can be varied across the scenarios. This would mean that each scenario has a different energy demand projection.
+Since we can not be certain of the scenario which will be the best predictor of the future, it is useful to model several scenarios and consider the implications of each of them to give useful insights for policymaking. This allows policy makers to assess which of the different policies and mixes suit their needs based upon likelihoods and risk tolerances.
# Summary
- In this mini-lecture we have discovered the main components which make up the Technodata sheet. We discovered the importance of properly defining the costs, lifetime and other characteristics which have a large impact on the final investment decisions.
-
-
+This mini-lecture provided an overview of energy demands, how we can define them and the details which make them up. We also explored how we can perform scenario analysis with energy demands, to understand what could happen in the future.
diff --git a/docs/lecture_05/Lecture_5.3.md b/docs/lecture_05/Lecture_5.3.md
index 84079c6..5dc695a 100644
--- a/docs/lecture_05/Lecture_5.3.md
+++ b/docs/lecture_05/Lecture_5.3.md
@@ -1,51 +1,57 @@
---
-title: Mini-Lecture 5.3 -- Input and output commodities
+title: Mini-Lecture 5.3 -- Energy demand in MUSE
keywords:
-- Technology efficiency
-- Input commodities
-- Output commodities
+- Energy demand
+- MUSE
authors:
- Alexander J. M. Kell
---
-In this mini-lecture we will learn about the input and output commodities within MUSE. Specifically we will learn what the `CommIn.csv` and `CommOut.csv` files do and how these relate to the energy system.
+## Short description
-# Learning objectives
+This mini-lecture provides an insight into how to model service demand within MUSE. There are two possible methods to model service demand in MUSE, from user input and by correlation. In this mini-lecture we will learn what the difference is between these.
-- To learn the importance of input and output commodities
-- To learn how we can modify these commodities in MUSE
+## Learning objectives
-# Introducing commodities
+- Understand how to input exogenous service demand
+- Understand what service demand by correlation is
-Input commodities are the commodities consumed by each technology. This could be coal for a coal power plant, uranium for a nuclear power plant or electricity for an electric heater. This is dependent on the technology, and some technologies can have multiple inputs.
+# Lecture content
-Output commodities are similar, but are the outputs of technologies. For example the output of any power plant will be electricity, and for heaters the output will be heat. Again, this is dependent on the technology, and some technologies can have multiple outputs such as combined heat and power plants.
+## Service Demand
-The ratio between these two parameters is very important in MUSE and in energy modelling in general. This is because it defines the efficiency of the technology. For instance, if a coal power plant requires 1 PJ of energy stored in coal to output 0.8 PJ of electricity, the coal power plant has an efficiency of 0.8. The higher the efficiency the more economical the power plant is and the more competitive it will be when compared to different technologies.
+A service demand is a term used to describe the consumption of energy by human activity. This could be, for instance, energy for lighting or cooking in the residential sector, personal vehicles in the transportation sector or machine usage in the industrial sector. The service demand drives the entire energy system, and it influences the total amount of energy used, the location of use and the types of fuels used in the energy supply system. It also includes the characteristics of the end-use technologies that consume energy.
-## Editing the CommIn and CommOut files
+## Exogenous service demand
-Within MUSE there are two files which one should change to edit these parameters: the `CommIn.csv` and `CommOut.csv` files. These files are found within the sector folders of the case study. For instance, in the `power/CommIn.csv` or `gas/CommOut.csv` directories.
+Within MUSE we must set the energy demand exogenously. That means that the model does not calculate how much the service demand is. Effectively, this means that the user must make an assumption on how much electricity is consumed in, for example, the residential sector for a particular region in the model.
-In this example we will look at the residential sectors `CommIn.csv` and `CommOut.csv` files. An example `CommIn.csv` file can be seen in the figure below:
+We can change this per scenario, but these values will not change during a simulation run, even if the price for all fuels increases significantly, for instance. We are able to define the exogenous service demand by year, sector, region and timeslice.
-{width=100%}
+## Service demand by correlation
-**Figure 5.3.1:** CommIn file for the residential sector
+In the previous section we learnt about the exogenous service demand. That is, we can explicitly specify what the demand would be per year, sector, region and timeslice. However, it may be the case that we do not know what the electricity demand is per year, especially in the future. We may instead conclude that our electricity demand is a function of the GDP and population of a particular region, as previously discussed.
-Here we see two technologies: `gasboiler` and `heatpump`. They are both in region R1 and we are specifying the characteristics for the year 2020. The `gasboiler` only requires gas, but requires 1.16 PJ, whereas the `heatpump` requires only 0.4 PJ to produce some energy.
+To accommodate such a scenario, MUSE enables us to choose a regression function that estimates service demands from GDP and population projections, which may be more predictable or have more accessible data in your case. A regression function is simply a mathematical model which fits a linear model to your data to predict what may happen in the future.
-However, it is important to note that these figures are meaningless without the `CommOut.csv` file. We need to know how much energy does the 1.16 PJ of energy produce in the `gasboiler`? As can be seen in the figure below showing an example `CommOut.csv` file, it is convention to select an output of 1. That way we only have to vary the `CommIn.csv` to change the efficiencies consistently.
+## Sources for energy demand data
-{width=100%}
+We can get publicly available energy balance data and/or demand projections from the following sources:
-**Figure 5.3.1:** CommOut file for the residential sector
+- International Energy Agency
+- International Renewable Energy Agency
+- United Nations Statistics
+- Asia-Pacific Economic Cooperation
-Therefore, we can now conclude that the `heatpump` is much more efficient than the `gasboiler` as only 0.4 PJ are required to output 1 PJ of heat. If we divide 1 by 0.4, we get the efficiency of the `heatpump`, where 1/0.4= 2.5. Notice that the `gasboiler` also outputs carbon dioxide. It is important to take these emissions into account to have a complete understanding of the energy system. MUSE calculates these emissions endogenously.
+Energy balances tell us the amount that each energy commodity is used in a country or region in a given year. This is usually broken down by sector.
+## Summary
+In this mini-lecture we introduced service demands, and the way we can input these into MUSE. The two ways we can input service demands are:
-# Summary
+- Exogenous service demand
+- Service demand by correlation
-This mini-lecture has explored the input and output commodities in MUSE. We have learnt that the `CommIn.csv` and `CommOut.csv` files relate to efficiencies when brought together in a ratio.
-
+We also learned where we can get energy data from for various countries.
+
+In the hands-on we will see how we can actually do this within MUSE.
diff --git a/docs/lecture_05/Lecture_5.4.md b/docs/lecture_05/Lecture_5.4.md
index 6e1cf13..0df5479 100644
--- a/docs/lecture_05/Lecture_5.4.md
+++ b/docs/lecture_05/Lecture_5.4.md
@@ -1,52 +1,61 @@
---
-title: Mini-Lecture 5.4 -- Interpolation and future years
+title: "Mini-Lecture 5.4 -- Demand examples and units"
keywords:
-- Interpolation
-- Energy technologies
+- Infrastructure performance
authors:
- Alexander J. M. Kell
---
-MUSE is flexible in its approach. It requires inputs for at least the base year, but does not necessarily need more than that to project forward. In this mini-lecture we will cover how MUSE deals with missing data and how to model future years
+# Short description
+This mini-lecture explains how we can use timeslices to approximate the real-world demand profile. We will look into the difference between power and energy. Finally, we will learn how to convert units to ensure we are consistent within MUSE.
# Learning objectives
-- Learn how to model costs in multiple years
-- Understand how MUSE deals with missing data
-- Understand interpolation
+- Understand how timeslices can be used in the context of demand
+- Understand the difference between power and energy
+- Know the units to use within MUSE and how to convert these
-# Introduction
+# Demand profile
-Within the input sheets you may have noticed the `Time` column. In the default example this is set to 2020. However, what happens beyond these years if we do not specify a cost, for example? Also, what happens in 2030 if we only specify a cost in 2020 and 2040?
+Figure 5.1.5 shown an example demand profile for electricity that could be used in MUSE. In this demand profile there are 96 bars: one for each of the timeslices used in MUSE. These timeslices are split into 16 different sections – seasonal and into day and night. This is because there are four different seasons, which are split into day and night (twice). The demand profile is used to represent the proportion of demand occurring in each timeslice.
-Within MUSE, we make some assumptions. We assume that if there are no costs input into a model beyond a certain year, that the costs remain the same. This is known as a flat-forward extension. If, for example, we input costs in 2020 and 2040, we will interpolate the values in between these years linearly.
+{width=100%}
-An example of this is, say that the capital costs for a gas boiler is set to be 4 for a gas boiler in 2020 and 2 in 2040. We have not explicitly defined 2025, 2030 or 2035. Based on linear interpolation, MUSE will assume a value of 2.5 for 2025, 3 for 2030 (halfway between the year 2020 and 2040) and 3.5 for 2035.
+**Figure 5.4.1:** Example demand profile for MUSE
-It must be noted, however, that MUSE does not allow a user to just update a single technology. For instance, if we want to specify the technology costs in 2035 for a coal power plant, we must also define the technology costs for every other technology in 2035 – although this cost need not be changed from the original value. We also do not need to define every year, however, as interpolation and a flat-forward extension can still be used.
+The chart shows us that electricity demand, in this example, is highest during the day in winter, while it is lowest during the night in spring. However, it is important to note that this is a simplification: in reality demand varies in the season and with each hour of the day. This simplification means that we model one representative day for each season, and we assume equal demand within days and nights of those seasons.
-## Practical example
+Whilst this is a simplification, it allows us to consider the variation in demand across seasons and days without having an incredibly complex model structure. This reduces the amount of time required to run a full model relative to having timeslices for each hour and day of the year, as well as reducing the data input requirements.
-The figure below shows a snippet of the technodata file for the residential sector. We can see that we have data parametrising the technologies in 2020.
+## Units
-{width=100%}
+We must ensure that during our data input process we are consistent with our units. Usually we will use the petajoules unit as this is the unit for energy for different sectors. If you were just modelling the power sector, you could use megawatt hours.
-**Figure 5.4.1:** Technodata for residential sector
+## Power vs. Energy
-Let's say that we want to update the capital costs (`cap_par`) for heat pumps in 2040, but do not want to update the prices for gasboilers. This is how we do it:
+When using energy modelling tools it is important to remember the difference between power and energy. Sometimes these terms are used interchangeably. However, there is an important difference between the two:
-{width=100%}
+- Energy is the total amount of work done or the total capacity for doing work
+- Power is the rate at which this energy is supplied or used.
-**Figure 5.4.2:** Updated technodata for residential sector
+Therefore, energy and power have different units. For example, energy is often measured in Joules, while power is often measured in Joules per Second (or Watts).
-Notice that we need separate rows for both `heatpump` and `gasboiler` even though we are only making a change in the `heatpump` capital cost. If we do not do this we will encounter an error. In between 2020 and 2040 we will get interpolation.
+For example, providing the weight stays the same, lifting a weight requires the exact same amount of energy no matter how quickly we lift it. However, if we lift the weight more quickly, the power has increased. We used the same amount of energy, but over a shorter amount of time.
+## Units for demand
-# Summary
+It is important that we convert our data from different sources to petajoules (PJ) when we include it in MUSE.
-In this mini-lecture we learned how to update costs in the time domain, and the assumptions MUSE makes if we do not give costs for every year. Namely, flat-forward extension and interpolation. We also learnt how to practically input these values in MUSE with the `Technodata.csv` file.
+Here are some example conversion factors:
+- 1 Petajoule (PJ) = 1000 Terajoules (TJ)
+- 1 Petajoule (PJ) = 1,000,000 Gigajoules (GJ)
+- 3.6 Petajoules (PJ) = 1 Terawatt hour (TWh)
+- 0.0036 Petajoules (PJ) = 1 Gigawatt hour (GWh)
+We must ensure that we are consistent with the units we use within MUSE.
+# Summary
+In this lecture we have learnt the difference between power and energy. We have also learnt how to use timeslicing to speed up our model and reduce complexity. Finally, we learnt that we must use consistent units.
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diff --git a/docs/lecture_05/bibliography.bib b/docs/lecture_05/bibliography.bib
index f706272..16a3e17 100644
--- a/docs/lecture_05/bibliography.bib
+++ b/docs/lecture_05/bibliography.bib
@@ -7,3 +7,18 @@ @article{Taliotis2018
title = {{Defining final energy demands in OSeMOSYS}},
year = {2018}
}
+
+@article{Olaniyan2018,
+ abstract = {Considering the challenge of accessing reliable household metering data in Nigeria, how can electricity consumption levels be determined? And how do disparities in electricity consumption patterns across the country affect the pursuit of sustainability, universal access and energy transition objectives? This study combined household-reported data on ownership of electrical appliances and energy expenditure with online sales records of household appliances to estimate current and future residential electricity demand in Nigeria, as well as the required generation capacity to achieve 100% electricity access, under various scenarios. Median residential electricity consumption was estimated at 18-27 kWh per capita but these estimates vary between the geographical zones with the North East and SouthWest representing extremes. Under a universal access scenario, the future electricity supply system would be expected to have installed generation capacity sufficient to meet the estimated residential demand of 85 TWh. To further understand the required infrastructure investment as a whole and the approaches that might be preferred in rural versus urban areas, the disaggregated, zone-by-zone and urban/rural data may offer more insight than a whole-of-country approach. The data obtained is useful for identifying specific transitions at the sub-national level that can minimize the required investment while maximizing households' energy access.},
+ author = {Olaniyan, Kayode and McLellan, Benjamin C. and Ogata, Seiichi and Tezuka, Tetsuo},
+ doi = {10.3390/su10051440},
+ file = {:Users/alexanderkell/Downloads/sustainability-10-01440.pdf:pdf},
+ isbn = {8180378314},
+ issn = {20711050},
+ journal = {Sustainability (Switzerland)},
+ keywords = {Electricity access,Energy transition,Household survey,Nigeria,Sustainability},
+ number = {5},
+ title = {{Estimating residential electricity consumption in Nigeria to support energy transitions}},
+ volume = {10},
+ year = {2018}
+}
diff --git a/docs/lecture_06/Lecture_6.1.md b/docs/lecture_06/Lecture_6.1.md
index f2cc6f7..5c50ea5 100644
--- a/docs/lecture_06/Lecture_6.1.md
+++ b/docs/lecture_06/Lecture_6.1.md
@@ -1,45 +1,26 @@
---
-title: Mini-Lecture 6.1 –- Residential Sectors in MUSE
+title: Mini-Lecture 6.1 -- Agents in energy systems models
keywords:
-- Residential sector
-- Sectors in MUSE
+- Agent-based model
authors:
- Alexander J. M. Kell
---
-This mini-lecture introduces the concept of the residential sector
-
+In this mini-lecture we will describe the importance of agents within MUSE and also within an energy systems modelling context.
# Learning objectives
-- Understand the role of the residential sector, its technologies and the main energy and societal challenges
-
-# Overview of the residential sector and its demands?
-
-Energy is used for many different reasons in the residential sector, as shown by Figure 6.1.1. This image shows the share of residential energy by service demand. We can see that energy is used for many different purposes, from heating and cooking to cleaning and ironing. This split of energy demand will vary across different countries. Figure 6.1.1 shows residential energy demand in Italy, which will differ to countries in Asia, for instance. This is largely dependent on different climates, levels of development and lifestyles.
-
-{width=100%}
-
-**Figure 6.1.1:** Residential sector in Italy and the different demands [@en12112055]. (Note: DHW refers to Domestic Hot Water).
-
-The total magnitude of energy demand varies by country as a total value, but also as energy demand per capita. This is strongly dependent on the level of electricity access and availability of other fuels in the country. Residential activities can use different forms of energy. For example, cooking can be met by burning biomass, oil products, natural gas or electricity. The fuels used vary by country.
+- Understand why agents are important in an energy modelling context
+- Understand how we can characterise these agents within MUSE
-## Residential sector technologies
+# Agents overview
-Some of the key residential technologies include lamps, cooking stoves, heating and air conditioning systems, as well as other electrical appliances. Some of these technologies can only use one fuel, such as electrical appliances and air conditioning which rely on electricity.
-
-However, in other cases multiple different fuels can be used for the same purpose. For example, heating. Heating can be met by burning biomass, natural gas, oil or electricity, for instance. These technologies have differing performance parameters. For example, electric stoves are usually much more efficient than biomass stoves. Different technological options also have different impacts on the environment and on human health. For example, the emissions from biomass can have detrimental impacts on human health, whereas electric stoves do not have emissions in the home.
-
-It is possible to model these different options in MUSE, which allows us to gain insights into their environmental and cost implications. Modelling can allow us to model the entire system as a whole, understand the trade-offs between certain technologies and make decisions on which policies to implement.
-
-## Residential sector in MUSE
-
-Within MUSE we can model different technology options. For instance, if we are to model an electric stove and a biomass stove we would have different inputs (CommIn.csv file). However, we would have the same output (CommOut.csv file) of cooking demand. We can also model an increase in efficiency of a technology by lowering the value in the CommIn.csv file. It is possible to change the efficiency over time using interpolation or a flat-forward extension as explained in mini-lecture 5.4. We can also consider the costs of investing in more energy efficient appliances by increasing the cost of these high efficiency appliances relative to the low efficiency appliances. By doing this, we can understand where and when investments in energy efficiency might be economic.
+Within real-life energy systems there are many different objectives that investors or consumers have. These objectives may differ by sector, by investor type or by proportions of the population. For instance, a certain percentage of the population may be willing to be spend more money on heating their homes than others.
+It is straightforward to specify these objectives and characteristics within MUSE. For instance, you may want to split a population based upon their geospatial and economic characteristics. This could be done by, for example, splitting a population into rural and urban categories. That would provide us with two groups. However, it is possible to go further, and we may want to split the rural and urban groups into different socioeconomic demographics, such as disposable income.
+Say, for example, we only split the population into rural and urban. We can specify these groups as two agents within MUSE. Once we have specified the two agents, we would have to give them characteristics which differentiate them from the each other and define the proportion of the population that they make up. It must be noted, at this stage, that we do not need to have a separate agent for each individual or entity. It is perfectly fine to group and aggregate similar individuals or agents.
# Summary
-In this lecture we have explored the residential sector. We considered the different demands that can reside within the residential sector and the different technologies that can be used to meet these demands. We also learnt of the difference in demands between countries and how we can model different technologies within MUSE.
-
-
+In this mini-lecture we understood the concept of agents and how they relate to an energy modelling context. We briefly understood how we can translate these concepts into MUSE. Urban populations might have greater energy needs or rural populations may not have access to the same energy sources. Giving the model a bit more detail will allow you to make sure that the model is both more accurate, and that its projections take into account different parts of society. In the hands-on we will learn how to add a new agent.
diff --git a/docs/lecture_06/Lecture_6.2.md b/docs/lecture_06/Lecture_6.2.md
index 697331d..c19912b 100644
--- a/docs/lecture_06/Lecture_6.2.md
+++ b/docs/lecture_06/Lecture_6.2.md
@@ -1,57 +1,35 @@
---
-title: Mini-Lecture 6.2 -- The transport sector in MUSE
+title: Mini-Lecture 6.2 -- How to relate agent representations to the real world
keywords:
-- Transport sector
-- Energy modelling
+- Agent-based modelling
+- Characterisation
authors:
- Alexander J. M. Kell
---
-This mini-lecture introduces the transport sector. We will explore the different demands and technologies within the transport sector and how we can model them within MUSE.
+In this mini-lecture we will introduce some methods to translate socioeconomic data into MUSE with a quantitative approach.
# Learning objectives
-- The main characteristics of the transport sector
-- How these can be modelled within MUSE
+- Discuss surveys and socioeconomic data and how these can relate to MUSE
+- Discover ways that surveys can be used in quantitative modelling
-# Overview of the transport sector and its demands
+# Qualitative representation in agent-based models
-The transport sector is vital in the modern age. In the last few decades, the use of transport has increased significantly. This is as more people gain access to vehicles and develop lifestyles which rely on transport.
+Through the use of qualitative data, such as using qualitative surveys, it is possible to gain greater insight into the different characteristics of consumers or investors. One example of how this can be done was by Moya et al. (2020). In this paper the authors explore fuel-switching investment in the long-term energy transitions of India's industry sector. They inform the modelled agents through a questionnaire that was carried out to inform MUSE.
-Figure 6.2.1 shows different modes of transport. As can be seen, road transport is the most used transport mode. We can also see that over 90% of fuel used in the EU transport sector is petroleum based. This is similar across the world. However, this creates challenges due to the unsustainability of fossil fuels.
+Some of the types of questions asked in the questionnaire to industrial companies are listed below:
-{width=100%}
+- Geographical location
+- Financial details
+- Investment plans
+- Type of fuels used
+- Willingness to switch fuels
-**Figure 6.2.1:** Transport modes and fuel share in the EU [@en13020432].
+Once these data have been collected, they can be used to find similar groups of investors and to start characterising the agents. For instance, if from the data it is clear that geographical location is an important consideration, the decision could be made to group companies by geographical region and form an agent on this basis. If the more important consideration is the investment plans, then a group can be made there.
-Due to the unsustainability of fossil fuels, other solutions have been taken up with support from governments around the world. For example, cars, motorbikes and buses can be fuelled by electricity. Electric vehicles have seen large reductions in cost and improvements in performance. Electric vehicles could play an important role in overcoming the sector's challenges.
-
-It is possible to model the different technologies in MUSE, and observe competition between technologies based upon their technoeconomic parameters.
-
-## Emissions
-
-The transport sector was estimated to be responsible for around 16% of global emissions in 2016 [@owidco2andothergreenhousegasemissions]. Thus, scenarios consistent with meeting global climate targets require transport sector emissions to decline rapidly. Therefore a rapid move towards sustainable technologies, such as electric vehicles is required. It is true, however, that some of the modes of transport are difficult to decarbonise. For example, it is difficult to decarbonise shipping and aviation technologies. This is because the energy density of lithium ion batteries and other technologies are lower than oil-based products. It is worth mentioning, however, that decarbonising transport is only useful if the energy sector increases its low-carbon electricity sources to supply the transport sector.
-
-## Transport sector in MUSE
-
-Similar to the residential sector, we can define different technologies for the transport sector using technoeconomic parameters. For example, we can split road transport into three categories:
-
-- Cars
-- Motorcycles
-- Buses
-
-We can then split these three categories into their propulsion system. For instance:
-
-- Electric vehicles
-- Conventional vehicles
-
-We can source road transport data from national energy balances such as from the IEA, and divide this between cars, motorcycles and buses based on the split of transport by mode in the country.
-
-We can then run a MUSE model with the different parameters and see the effect of these different parameters on agent investment decisions. These parameters could be fuel prices, technology costs or performance parameters. We can also run the model with a carbon limit, which places a tax on carbon emissions, allowing us to work out how to pick a desirable policy depending on what we are trying to achieve.
+This approach is a more than efficient method of better understanding the characteristics of agents of a system, and it can help to inform a better modelling process. The work by Moya et al. [@Moya2020] finds that the results represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. In other words, the survey results have an impact on the outcome of the energy system over the long-term.
# Summary
-In this mini-lecture we have considered the transport sector and how we can model this within MUSE. We discussed the emissions of the transport sector, and how different technologies can be used to reduce these emissions.
-
-
-
+In this mini-lecture we explored how surveys can be used to inform agents within MUSE. We also discovered how these results can affect the modelling outcomes of energy systems.
diff --git a/docs/lecture_06/Lecture_6.3.md b/docs/lecture_06/Lecture_6.3.md
index 0102300..503d9ee 100644
--- a/docs/lecture_06/Lecture_6.3.md
+++ b/docs/lecture_06/Lecture_6.3.md
@@ -1,47 +1,34 @@
---
-title: Mini-Lecture 6.3 -- The industrial and commercial sectors
+title: Mini-Lecture 6.3 -- Agents by sector
keywords:
-- Industrial sector
-- Commercial sectors
-- MUSE modelling
+- Sectors
+- Agent differentiation
+- Key agent parameters
authors:
- Alexander J. M. Kell
---
-This mini-lecture reflects on
+In this mini-lecture we will cover how agents and their characteristics can differ between sectors. We will also investigate the similarities between agents and sectors and consider the key parameters that make up agents.
# Learning objectives
-- The main characteristics of the industrial and commercial sectors
-- How these can be modelled within MUSE
+- Understand the differences between agents of different sectors
+- Understand the key parameters that differentiate agents
-# Overview of the industrial and commercial sectors
+# Agent parameters
-Next, we will explore the industrial and commercial sectors and their respective energy demands. Figure 6.3.1. shows the energy consumption for different sectors, including industrial, by OECD (generally high-income countries) and non-OECD countries (generally low- and middle-income countries). It is evident that the industrial sector is responsible for a large share of energy consumption across the world. The industrial sector is forecast to rise in non-OECD countries significantly. We must also consider this growing expected demand in the modelling process and during policy design.
+Different sectors may mean having agents with different characteristics. For instance, within the residential sector socioeconomic data can be used to characterise the agents. We could use wealth to characterise our agents in different geographic locations. For example we could place a constraint on the `Budget` parameter for residential users, and split these agents into different proportions. For example, we could prohibit 70% of residential users from spending more than a certain amount on heating which could affect their technology choice. The other 30% of users would form an agent that was not constricted in this way, and thus their choices may end up being different in the model.
-{width=100%}
-
-**Figure 6.3.1:** Energy consumption by sector, OECD and non-OECD [@world1020007].
-
-Energy is used in industry for a number of different purposes. For instance, heating and cooling, running machinery and chemical processes. These processes use a large variety of fuels and depend on the purpose, location and the technoeconomics.
-
-The commercial sector has a lower energy demand when compared to the industrial sector. This is because commercial processes, typically, are less energy intense and on smaller scales. This demand is often lighting, heating and to run office equipment and appliances.
-
-## Industrial and commercial technologies
+Another way we could classify residential agents is through the `Maturity` parameter. This would limit investments in novel technologies until the specified technology had a certain market share. This could be informed by the innovation adoption lifecycle, as shown by Figure 6.3.1. Where, for example, innovators make up 2.5% of the population but have no `Maturity` constraints. As we work our way up the curve from innovators to laggards, this `Maturity` constraint increases.
-Commercial activities use many different technologies which require energy inputs. For example, office electronics, lighting and heating systems. Many of these technologies use electricity. However, for some demands natural gas is used, for example for heating commercial buildings.
-
-The industrial sector uses a wide range of technologies. This includes heavy machinery, boilers, heating and air conditioning. Again, a wide variety of fuels can be used for this. However, there exist a number of processes, such as steel manufacturing which requires very high temperatures. This is usually only done by burning fossil fuels, as it can be difficult to reach these high temperatures with electricity.
-
-## Modelling industrial and commercial sectors in MUSE
+{width=100%}
-Similarly to the residential and transport sectors, we can use an energy balance [@iea_world_energy_balance] to estimate industry demands -- for instance, for industry heating demands. There are different technologies available for industrial heating. These can be grouped in a way that makes sense for your case study. However, as an example we can group these into high heat and low heat, which are modelled as separate demands. This is because generating very high temperatures requires different technologies and processes to generating low heat.
+**Figure 6.3.1:** Innovation adoption lifecycle
-Again, we can group the technologies by their input fuel, such as biomass, coal, oil products or electricity with the `CommIn.csv` file. Through modelling with MUSE we can understand the emissions and economics of different technologies.
+# Sectors
-In addition, the commercial sector will have a different demand load profile to the residential sector. This is because, typically, the demand will follow office times for the specific region, whereas the residential sector will follow the inverse of the office schedule.
+In this mini-lecture we have focused on the residential sector and seen the way we can characterise agents. Although these characteristics may not directly translate to the power sector, in some cases investors in the power sector can have similar characteristics. For instance, some companies are larger, and are more willing to invest their capital, reflecting a larger `Budget` parameter. Others may be less willing to invest in new technologies. The differing objectives of agents will often be the reason behind differences with other agents. For instance, some agents may only want to minimise their costs, whereas others may want to reduce their capital expenditure. It is easy to change these characteristics within MUSE to create diverse energy scenarios.
# Summary
-In this mini-lecture we explored the industrial and commercial sectors. We learnt the difference between these two sectors in terms of demand and the different types of technologies used in these sectors. We saw that demand for the industrial sector is expected to rise significantly in non-OECD countries. Finally, we learnt how we can model different technologies in MUSE.
-
+In this mini-lecture we covered the differences between agents and the different parameters that can be used to inform these differences. We saw how the `Maturity` constraint maps to the innovation adoption lifecycle and how the `Budget` parameter can be informed by socioeconomic characteristics. These parameters lead to a large amount of possible scenarios that can be tested and run.
diff --git a/docs/lecture_06/Lecture_6.4.md b/docs/lecture_06/Lecture_6.4.md
index 470ba05..465934c 100644
--- a/docs/lecture_06/Lecture_6.4.md
+++ b/docs/lecture_06/Lecture_6.4.md
@@ -1,36 +1,31 @@
---
-title: Mini-Lecture 6.4 -- Sector coupling
+title: Mini-Lecture 6.4 -- Agent parameters
keywords:
-- Preset sectors
-- Service demand
+- Agent parameters
+- MUSE
authors:
- Alexander J. M. Kell
---
-In this mini-lecture we will investigate the role of electrification in different sectors, as well as find out what sector coupling is.
+This mini-lecture explores all the major parameters that can define agents within MUSE.
# Learning objectives
-- Understand the importance of sector electrification
-- Understand the need for sector coupling
+- Understand the different agent parameters and their role within MUSE
-# Sector electrification
+# Overview agent parameters
-Electrification is becoming increasingly important in all sectors of the economy in order to achieve decarbonisation goals. As we saw earlier, electrification can be used to decarbonise the residential, transport, industrial and commercial sectors. However, some sectors are likely to be easier to electrify than other sectors. We have seen rapid progress with electric vehicles in parts of the transport sector, but sectors such as shipping and steel, which are harder to decarbonise, still have a way to go.
+Within MUSE each agent can have their own objectives. MUSE is flexible enough to allow for up to 3 objectives, which can be summed together at various weightings. To input these objectives into MUSE one would use the `Objective1`, `Objective2` and/or `Objective3` parameters and select an objective such as `comfort`, `lifetime_levelized_cost_of_energy` or `fixed_costs`.
-However, different options exist for the decarbonisation of steel, for example. This can be done by retrofitting blast furnaces and adding carbon capture and storage (CCS) or scaling up hydrogen-based direct reduced iron. However, this will require innovation and further research on the key technologies, such as CCS.
+Then we would select the weight of each of the objectives using the `ObjData1`, `ObjData2`, `ObjData3` inputs. For example, if we had 3 objectives, we could make the objective of `Objective1` dominant by setting `ObjData1` to 0.5. This would mean it would make up 50% of the final objective.
-## Sector coupling
+We can edit the `SearchRule` to reduce the space of technologies that those agents are likely to consider. For example, we could fill this with `same_fuels`, or `same_enduse`.
-We have seen that we must decarbonise to meet global climate targets. However, this is not a straightforward process. A large reason for this is the inflexibility of intermittent renewable resources such as solar and wind technologies. One method of mitigating this variability and inflexibility is through sector coupling. Sector coupling is where we connect energy demands and processes across differing sectors and increase the efficiency and flexibility of energy use. This would allows us to use renewable energy for all sectors.
+The rest of the parameters include the parameters discussed in the previous lecture:
-One way this could be achieved is through power to gas conversion. When there is a high supply of renewable power, excess electricity could be used to produce hydrogen and methane. This would allow us to store this energy for later use across multiple sectors. This would enable sectors that are difficult to electrify to be based on renewable energy.
-
-It is possible to model this sector coupling process within MUSE and to understand the tipping points which would make sector coupling possible. This could be based on the price and capacity of renewable energy, as well as the price of generating hydrogen or methane compared to the incumbent technologies.
+- `MaturityThreshold`
+- `Budget`
# Summary
-In this lecture we have covered the importance of electrifying different sectors to reduce carbon emissions and meet some of the Sustainable Development Goals. We have also learnt of the importance of sector coupling to address hard to decarbonise sectors.
-
-
-
+In this mini-lecture we discovered the main parameters that are used by agents within MUSE. For a full breakdown of the parameters please refer to the MUSE documentation that can be found online.
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diff --git a/docs/lecture_06/bibliography.bib b/docs/lecture_06/bibliography.bib
index 0d1365b..4fd8773 100644
--- a/docs/lecture_06/bibliography.bib
+++ b/docs/lecture_06/bibliography.bib
@@ -1,62 +1,15 @@
-
-@article{en12112055,
- author = {Mancini, Francesco and Lo Basso, Gianluigi and De Santoli, Livio},
- title = {Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey},
- journal = {Energies},
- volume = {12},
- year = {2019},
- number = {11},
- article-number = {2055},
- url = {https://www.mdpi.com/1996-1073/12/11/2055},
- issn = {1996-1073},
- abstract = {This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.},
- doi = {10.3390/en12112055}
-}
-
-
-@article{en13020432,
- author = {Arens, Stefan and Schlüters, Sunke and Hanke, Benedikt and Maydell, Karsten von and Agert, Carsten},
- title = {Sustainable Residential Energy Supply: A Literature Review-Based Morphological Analysis},
- journal = {Energies},
- volume = {13},
- year = {2020},
- number = {2},
- article-number = {432},
- url = {https://www.mdpi.com/1996-1073/13/2/432},
- issn = {1996-1073},
- abstract = {The decarbonization of the energy system will bring substantial changes, from supranational regions to residential sites. This review investigates sustainable energy supply, applying a multi-sectoral approach from a residential site perspective, especially with focus on identifying crucial, plausible factors and their influence on the operation of the system. The traditionally separated mobility, heat, and electricity sectors are examined in more detail with regard to their decarbonization approaches. For every sector, available technologies, demand, and future perspectives are described. Furthermore, the benefits of cross-sectoral integration and technology coupling are examined, besides challenges to the electricity grid due to upcoming technologies, such as electric vehicles and heat pumps. Measures such as transport mode shift and improving building insulation can reduce the demand in their respective sector, although their impact remains uncertain. Moreover, flexibility measures such as Power to X or vehicle to grid couple the electricity sector to other sectors such as the mobility and heat sectors. Based on these findings, a morphological analysis is conducted. A morphological box is presented to summarize the major characteristics of the future residential energy system and investigate mutually incompatible pairs of factors. Lastly, the scenario space is further analyzed in terms of annual energy demand for a district.},
- doi = {10.3390/en13020432}
-}
-
-
-@article{world1020007,
- author = {Mendoza, Daniel L. and Bianchi, Carlo and Thomas, Jermy and Ghaemi, Zahra},
- title = {Modeling County-Level Energy Demands for Commercial Buildings Due to Climate Variability with Prototype Building Simulations},
- journal = {World},
- volume = {1},
- year = {2020},
- number = {2},
- pages = {67--89},
- url = {https://www.mdpi.com/2673-4060/1/2/7},
- issn = {2673-4060},
- abstract = {The building sector accounts for nearly 40% of total primary energy consumption in the U.S. and E.U. and 20% of worldwide delivered energy consumption. Climate projections predict an increase of average annual temperatures between 1.1–5.4 °C by 2100. As urbanization is expected to continue increasing at a rapid pace, the energy consumption of buildings is likely to play a pivotal role in the overall energy budget. In this study, we used EnergyPlus building energy models to estimate the future energy demands of commercial buildings in Salt Lake County, Utah, USA, using locally-derived climate projections. We found significant variability in the energy demand profiles when simulating the study buildings under different climate scenarios, based on the energy standard the building was designed to meet, with reductions ranging from 10% to 60% in natural gas consumption for heating and increases ranging from 10% to 30% in electricity consumption for cooling. A case study, using projected 2040 building stock, showed a weighted average decrease in heating energy of 25% and an increase of 15% in cooling energy. We also found that building standards between ASHRAE 90.1-2004 and 90.1-2016 play a comparatively smaller role than variation in climate scenarios on the energy demand variability within building types. Our findings underscore the large range of potential future building energy consumption which depends on climatic conditions, as well as building types and standards.},
- doi = {10.3390/world1020007}
-}
-
-
-
-@article{owidco2andothergreenhousegasemissions,
- author = {Hannah Ritchie and Max Roser},
- title = {CO₂ and Greenhouse Gas Emissions},
- journal = {Our World in Data},
- year = {2020},
- note = {https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions}
-}
-
-@article{iea_world_energy_balance,
- author = {IEA},
- title = {World Energy Balances: Overview},
- journal = {IEA},
- year = {2021},
- note = {https://www.iea.org/reports/world-energy-balances-overview}
+@article{Moya2020,
+ abstract = {This paper presents the formulation and application of a novel agent-based integrated assessment approach to model the attributes, objectives and decision-making process of investors in a long-term energy transition in India's iron and steel sector. It takes empirical data from an on-site survey of 108 operating plants in Maharashtra to formulate objectives and decision-making metrics for the agent-based model and simulates possible future portfolio mixes. The studied decision drivers were capital costs, operating costs (including fuel consumption), a combination of capital and operating costs, and net present value. Where investors used a weighted combination of capital cost and operating costs, a natural gas uptake of $\sim$12PJ was obtained and the highest cumulative emissions reduction was obtained, 2 Mt CO2 in the period from 2020 to 2050. Conversely if net present value alone is used, cumulative emissions reduction in the same period was lower, 1.6 Mt CO2, and the cumulative uptake of natural gas was equal to 15PJ. Results show how the differing upfront investment cost of the technology options could cause prevalence of high-carbon fuels, particularly heavy fuel oil, in the final mix. Results also represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. The perception of high capital expenditures for decarbonisation represents a significant barrier to the energy transition in industry and should be addressed via effective policy making (e.g. carbon policy/price).},
+ author = {Moya, Diego and Budinis, Sara and Giarola, Sara and Hawkes, Adam},
+ doi = {10.1016/j.apenergy.2020.115295},
+ file = {:Users/alexanderkell/Downloads/1-s2.0-S0306261920308072-main-2.pdf:pdf},
+ issn = {03062619},
+ journal = {Applied Energy},
+ keywords = {Agent-based,Decarbonisation,Energy survey,Energy systems modelling,Investment metrics,Iron and steel},
+ pages = {115295},
+ publisher = {Elsevier},
+ title = {{Agent-based scenarios comparison for assessing fuel-switching investment in long-term energy transitions of the India's industry sector}},
+ url = {https://doi.org/10.1016/j.apenergy.2020.115295},
+ volume = {274},
+ year = {2020}
}
diff --git a/docs/lecture_07/Lecture_7.1.md b/docs/lecture_07/Lecture_7.1.md
index a650a3f..a6188da 100644
--- a/docs/lecture_07/Lecture_7.1.md
+++ b/docs/lecture_07/Lecture_7.1.md
@@ -1,27 +1,26 @@
---
-title: Mini-Lecture 7.1 -- Agents in energy systems models
+title: Mini-Lecture 7.1 -- Introduction to regions and aggregation
keywords:
-- Agent-based model
+- Regions
+- MUSE
authors:
- Alexander J. M. Kell
---
-In this mini-lecture we will describe the importance of agents within MUSE and also within an energy systems modelling context.
+This mini-lecture provides an overview of different regions within energy systems models and how these can be represented within MUSE.
# Learning objectives
-- Understand why agents are important in an energy modelling context
-- Understand how we can characterise these agents within MUSE
+- When to aggregate data into different regions
-# Agents overview
+# Aggregation
-Within real-life energy systems there are many different objectives that investors or consumers have. These objectives may differ by sector, by investor type or by proportions of the population. For instance, a certain percentage of the population may be willing to be spend more money on heating their homes than others.
+Regions within energy models play an important role. We often want to aggregate technoeconomic data from multiple regions into one. For example, the UK is made up of many different counties with different energy demands and supply. However, it could be the case that we do not have comprehensive data for each of these counties. We may, however, have plentiful data for the UK as a whole, or even for England, Scotland, Northern Ireland and Wales. We can therefore aggregate these data and make assumptions about the geographical locations of supply and demand.
-It is straightforward to specify these objectives and characteristics within MUSE. For instance, you may want to split a population based upon their geospatial and economic characteristics. This could be done by, for example, splitting a population into rural and urban categories. That would provide us with two groups. However, it is possible to go further, and we may want to split the rural and urban groups into different socioeconomic demographics, such as disposable income.
+This is an example of aggregation and can make the modelling process more straightforward, whilst losing a small amount of accuracy. This is because we do not need to model each individual power plant, demand centre or end-use sector. This means we can use aggregated data which are often easier to access.
-Say for example, we only split the population into rural and urban. We can specify these groups as two agents within MUSE. Once we have specified the two agents, we would have to give them characteristics which differentiate them from the each other and define the proportion of the population that they make up. It must be noted, at this stage, that we do not need to have a separate agent for each individual or entity. It is perfectly fine to group and aggregate similar individuals or agents.
+We can also aggregate multiple countries into regions. For example, we can merge the European continent together. This would be especially useful if we are considering a global model. However, it must be noted that we would lose significant detail by aggregating up to a supranational level. It is up to you, the model user, to consider the trade-offs between aggregation and disaggregation. For example, if you only wanted to model a single country, it would be possible to have a single region. However, if you had good access to data at the local level, you could disaggregate the data further. It does not matter whether the region is a single country, a number of counties or at a supranational level. The regions depend on your case study and the data you have access to.
# Summary
-In this mini-lecture we understood the concept of agents and how they relate to an energy modelling context. We briefly understood how we can translate these concepts into MUSE. Urban populations might have greater energy needs or rural populations may not have access to the same energy sources. Giving the model a bit more detail will allow you to make sure that the model is both more accurate, and that its projections take into account different parts of society. In the hands-on we will learn how to add a new agent.
-
+In this mini-lecture we learnt about the trade-offs between aggregation and disaggregation when defining regions. We learnt that the more aggregated the model, the less granular data are required. This can be helpful in cases where the data are not available at a local level, but available at a national level.
diff --git a/docs/lecture_07/Lecture_7.2.md b/docs/lecture_07/Lecture_7.2.md
index 4633f42..a5df4ba 100644
--- a/docs/lecture_07/Lecture_7.2.md
+++ b/docs/lecture_07/Lecture_7.2.md
@@ -1,38 +1,28 @@
---
-title: Mini-Lecture 7.2 -- How to relate agent representations to the real world
+title: Mini-Lecture 7.2 -- Disaggregation of regional data
keywords:
-- Agent-based modelling
-- Characterisation
+- Disaggregation
+- Regions
authors:
- Alexander J. M. Kell
---
-In this mini-lecture we will introduce some methods to translate socioeconomic data into MUSE with a quantitative approach.
+This mini-lecture introduces the concept of disaggregation of regions in further detail.
# Learning objectives
-- Discuss surveys and socioeconomic data and how these can relate to MUSE
-- Discover ways that surveys can be used in quantitative modelling
+- When to disaggregate regional data in MUSE and energy systems models
-# Qualitative representation in agent-based models
+# Disaggregation
-Through the use of qualitative data, such as using qualitative surveys, it is possible to gain greater insight into the different characteristics of consumers or investors. One example of how this can be done was by Moya et al. (2020). In this paper the authors explore fuel-switching investment in the long-term energy transitions of India's industry sector. They inform the modelled agents through a questionnaire that was carried out to inform MUSE.
+Disaggregation of regions can often be a good way of gaining a deeper understanding of the interactions between regions. For example, if you have a lot of technoeconomic data on the locations of supply and demand, then it may make sense to disaggregate regions. This will also allow the modeller to understand where there may be issues within a specific region or country.
-Some of the types of questions asked in the questionnaire to industrial companies are listed below:
+An interesting example of this would be for the Southeast Asia region. Laos has a good amount of hydropower availability, whereas Thailand has more solar and wind resources. If we modelled the Southeast Asia region as a single region in MUSE, we would lose information on the potential for trade between these two countries.
-- Geographical location
-- Financial details
-- Investment plans
-- Type of fuels used
-- Willingness to switch fuels
-
-Once these data have been collected, they can be used to find similar groups of investors and to start characterising the agents. For instance, if from the data it is clear that geographical location is an important consideration, the decision could be made to group companies by geographical region and form an agent on this basis. If the more important consideration is the investment plans, then a group can be made there.
-
-This approach is a more than efficient method of better understanding the characteristics of agents of a system, and it can help to inform a better modelling process. The work by Moya et al. ([@Moya2020]) finds that the results represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. In other words, the survey results have an impact on the outcome of the energy system over the long-term.
+It is also interesting to see energy flows between regions within a country, similar to the Southeast Asian example. For example, if a country has a large demand centre in the south of the country, but large energy resources in the north, it could be interesting to disaggregate this country into those two nodes.
+Similar to the previous mini-lecture, this disaggregation is largely dependent upon your requirements and the data available to you. There is no one solution for all areas, or even for the same area and different case studies. For example, one case study may only require the modelling of a country as a single region. Another case study, however, may require the modelling of that same country by many regions. It all depends on the question you are trying to answer and the data available to you. It must be noted, that a more disaggregated case study will take longer to run in MUSE.
# Summary
-In this mini-lecture we explored how surveys can be used to inform agents within MUSE. We also discovered how these results can affect the modelling outcomes of energy systems.
-
-
+In this mini-lecture we explored reasons for disaggregating a case study. We discovered that disaggregation (and aggregation) of regions depends largely on the data available to you and the questions you want to answer for your case study. However, we found out that the greater the disaggregation, the more detail the model may reveal, but the longer the model will take to run.
diff --git a/docs/lecture_07/Lecture_7.3.md b/docs/lecture_07/Lecture_7.3.md
deleted file mode 100644
index 68351e5..0000000
--- a/docs/lecture_07/Lecture_7.3.md
+++ /dev/null
@@ -1,38 +0,0 @@
----
-title: Mini-Lecture 7.3 -- Agents by sector
-keywords:
-- Sectors
-- Agent differentiation
-- Key agent parameters
-authors:
-- Alexander J. M. Kell
----
-
-In this mini-lecture we will cover how agents and their characteristics can differ between sectors. We will also investigate the similarities between agents and sectors and consider the key parameters that make up agents.
-
-# Learning objectives
-
-- Understand the differences between agents of different sectors
-- Understand the key parameters that differentiate agents
-
-
-# Agent parameters
-
-Different sectors may mean having agents with different characteristics. For instance, within the residential sector socioeconomic data can be used to characterise the agents. We could use wealth to characterise our agents in different geographic locations. For example we could place a constraint on the `Budget` parameter for residential users, and split these agents into different proportions. For example, we could prohibit 70% of residential users from spending more than a certain amount on heating which could affect their technology choice. The other 30% of users would form an agent that was not constricted in this way, and thus their choices may end up being differet in the model.
-
-Another way we could classify residential agents is through the `Maturity` parameter. This would limit investments in novel technologies until the specified technology had a certain market share. This could be informed by the innovation adoption lifecycle, as shown by Figure 7.3.1. Where, for example, innovators make up 2.5% of the population but have no `Maturity` constraints. As we work our way up the curve from innovators to laggards, this `Maturity` constraint increases.
-
-{width=100%}
-
-**Figure 7.3.1:** Innovation adoption lifecycle
-
-# Sectors
-
-In this mini-lecture we have focused on the residential sector and seen the way we can characterise agents. Although these characteristics may not directly translate to the power sector, in some cases investors in the power sector can have similar characteristics. For instance, some companies are larger, and are more willing to invest their capital, reflecting a larger `Budget` parameter. Others may be less willing to invest in new technologies. The differing objectives of agents will often be the reason behind differences with other agents. For instance, some agents may only want to minimise their costs, whereas others may want to reduce their capital expenditure. It is easy to change these characteristics within MUSE to create diverse energy scenarios.
-
-
-
-# Summary
-
-In this mini-lecture we covered the differences between agents and the different parameters that can be used to inform these differences. We saw how the `Maturity` constraint maps to the innovation adoption lifecycle and how the `Budget` parameter can be informed by socioeconomic characteristics. These parameters lead to a large amount of possible scenarios that can be tested and run.
-
diff --git a/docs/lecture_07/Lecture_7.4.md b/docs/lecture_07/Lecture_7.4.md
deleted file mode 100644
index 153f323..0000000
--- a/docs/lecture_07/Lecture_7.4.md
+++ /dev/null
@@ -1,33 +0,0 @@
----
-title: Mini-Lecture 7.4 -- Agent parameters
-keywords:
-- Agent parameters
-- MUSE
-authors:
-- Alexander J. M. Kell
----
-
-This mini-lecture explores all the major parameters that can define agents within MUSE.
-
-# Learning objectives
-
-- Understand the different agent parameters and their role within MUSE
-
-# Overview agent parameters
-
-Within MUSE each agent can have their own objectives. MUSE is flexible enough to allow for up to 3 objectives, which can be summed together at various weightings. To input these objectives into MUSE one would use the `Objective1`, `Objective2` and/or `Objective3` parameters and select an objective such as `comfort`, `lifetime_levelized_cost_of_energy` or `fixed_costs`.
-
-Then we would select the weight of each of the objectives using the `ObjData1`, `ObjData2`, `ObjData3` inputs. For example, if we had 3 objectives, we could make the objective of `Objective1` dominant by setting `ObjData1` to 0.5. This would mean it would make up 50% of the final objective.
-
-We can edit the `SearchRule` to reduce the space of technologies that those agents are likely to consider. For example, we could fill this with `same_fuels`, or `same_enduse`.
-
-The rest of the parameters include the parameters discussed in the previous lecture:
-
-- `MaturityThreshold`
-- `Budget`
-
-# Summary
-
-In this mini-lecture we discovered the main parameters that are used by agents within MUSE. For a full breakdown of the parameters please refer to the MUSE documentation that can be found online.
-
-
diff --git a/docs/lecture_07/assets/Figure_7.3.1.png b/docs/lecture_07/assets/Figure_7.3.1.png
deleted file mode 100644
index 04d9d66..0000000
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diff --git a/docs/lecture_07/bibliography.bib b/docs/lecture_07/bibliography.bib
index 4fd8773..178e9fd 100644
--- a/docs/lecture_07/bibliography.bib
+++ b/docs/lecture_07/bibliography.bib
@@ -1,15 +1,274 @@
-@article{Moya2020,
- abstract = {This paper presents the formulation and application of a novel agent-based integrated assessment approach to model the attributes, objectives and decision-making process of investors in a long-term energy transition in India's iron and steel sector. It takes empirical data from an on-site survey of 108 operating plants in Maharashtra to formulate objectives and decision-making metrics for the agent-based model and simulates possible future portfolio mixes. The studied decision drivers were capital costs, operating costs (including fuel consumption), a combination of capital and operating costs, and net present value. Where investors used a weighted combination of capital cost and operating costs, a natural gas uptake of $\sim$12PJ was obtained and the highest cumulative emissions reduction was obtained, 2 Mt CO2 in the period from 2020 to 2050. Conversely if net present value alone is used, cumulative emissions reduction in the same period was lower, 1.6 Mt CO2, and the cumulative uptake of natural gas was equal to 15PJ. Results show how the differing upfront investment cost of the technology options could cause prevalence of high-carbon fuels, particularly heavy fuel oil, in the final mix. Results also represent the unique heterogeneity of fuel-switching industrial investors with distinct investment goals and limited foresight on costs. The perception of high capital expenditures for decarbonisation represents a significant barrier to the energy transition in industry and should be addressed via effective policy making (e.g. carbon policy/price).},
- author = {Moya, Diego and Budinis, Sara and Giarola, Sara and Hawkes, Adam},
- doi = {10.1016/j.apenergy.2020.115295},
- file = {:Users/alexanderkell/Downloads/1-s2.0-S0306261920308072-main-2.pdf:pdf},
- issn = {03062619},
- journal = {Applied Energy},
- keywords = {Agent-based,Decarbonisation,Energy survey,Energy systems modelling,Investment metrics,Iron and steel},
- pages = {115295},
- publisher = {Elsevier},
- title = {{Agent-based scenarios comparison for assessing fuel-switching investment in long-term energy transitions of the India's industry sector}},
- url = {https://doi.org/10.1016/j.apenergy.2020.115295},
- volume = {274},
- year = {2020}
-}
+@article{Bloemendaal2019storm,
+ author = {Nadia Bloemendaal and Ivan Haigh and Hans {de Moel} and S. Muis and Reindert Haarsma and Jeroen Aerts},
+ date-added = {2021-08-13 12:34:08 +0200},
+ date-modified = {2021-08-13 12:34:14 +0200},
+ doi = {10.4121/uuid:82c1dc0d-5485-43d8-901a-ce7f26cda35d},
+ month = {11},
+ title = {{STORM IBTrACS present climate synthetic tropical cyclone tracks}},
+ url = {https://data.4tu.nl/articles/dataset/STORM_IBTrACS_present_climate_synthetic_tropical_cyclone_tracks/12706085},
+ year = {2019},
+ Bdsk-Url-1 = {https://data.4tu.nl/articles/dataset/STORM_IBTrACS_present_climate_synthetic_tropical_cyclone_tracks/12706085},
+ Bdsk-Url-2 = {https://doi.org/10.4121/uuid:82c1dc0d-5485-43d8-901a-ce7f26cda35d}}
+
+@report{Dawson2016,
+ author = {R.J. Dawson and D. Thompson and D. Johns and S. Gosling and L. Chapman and G. Darch and G. Watson and W. Powrie and S. Bell and K. Paulson and P. Hughes and R. Wood},
+ city = {London},
+ institution = {Adaptation Sub-Committee for the Committee on Climate Change},
+ publisher = {Paul Hughes},
+ title = {UK Climate Change Risk Assessment Evidence Report: Chapter 4: Infrastructure},
+ year = {2016}}
+
+@article{Steinbuks2010,
+ abstract = {This paper attempts to identify the underlying causes and costs of own generation of electric power in Africa. Rigorous empirical analysis of 8483 currently operating firms in 25 African countries shows that the prevalence of own generation would remain high (at around 20%) even if power supplies were perfectly reliable, suggesting that other factors such as firms' size, emergency back-up and export regulations play a critical role in the decision to own a generator. The costs of own-generation are about three times as high as the price of purchasing (subsidized) electricity from the public grid. However, because these generators only operate a small fraction of the time, they do not greatly affect the overall average cost of power to industry. The benefits of generator ownership are also substantial. Firms with their own generators report a value of lost load of less than US$50 per hour, compared with more than US$150 per hour for those without. Nevertheless, when costs and benefits are considered side by side, the balance is not found to be significantly positive. {\copyright} 2009 Elsevier B.V.},
+ author = {J. Steinbuks and V. Foster},
+ doi = {10.1016/j.eneco.2009.10.012},
+ issn = {01409883},
+ issue = {3},
+ journal = {Energy Economics},
+ keywords = {Africa,Electricity,Generators,Ownership,Reliability},
+ month = {5},
+ pages = {505-514},
+ publisher = {North-Holland},
+ title = {When do firms generate? Evidence on in-house electricity supply in Africa},
+ volume = {32},
+ year = {2010},
+ Bdsk-Url-1 = {https://doi.org/10.1016/j.eneco.2009.10.012}}
+
+@generic{Hall2015,
+ abstract = {The impacts of extreme events are triggering action and reaction --- sometimes in unexpected ways. Confronted by 'adaptation emergencies', the private sector is rapidly innovating climate risk management, but governments must also fulfil their responsibilities.},
+ author = {Jim W. Hall and Frans Berkhout and Rowan Douglas},
+ doi = {10.1038/nclimate2467},
+ issn = {17586798},
+ issue = {1},
+ journal = {Nature Climate Change},
+ keywords = {Business and industry,Governance,Government},
+ month = {12},
+ pages = {6-7},
+ publisher = {Nature Publishing Group},
+ title = {Responding to adaptation emergencies},
+ url = {http://dx.doi.org/10.5270/OceanObs09-FOO},
+ volume = {5},
+ year = {2015},
+ Bdsk-Url-1 = {http://dx.doi.org/10.5270/OceanObs09-FOO},
+ Bdsk-Url-2 = {http://dx.doi.org/10.1038/nclimate2467}}
+
+@webpage{Loew2019,
+ author = {Petra Loew},
+ title = {The natural disasters of 2018 in figures | Munich Re Topics Online},
+ url = {https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/the-natural-disasters-of-2018-in-figures.html},
+ year = {2019},
+ Bdsk-Url-1 = {https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/the-natural-disasters-of-2018-in-figures.html}}
+
+@article{Thacker2019,
+ abstract = {Infrastructure systems form the backbone of every society, providing essential services that include energy, water, waste management, transport and telecommunications. Infrastructure can also create harmful social and environmental impacts, increase vulnerability to natural disasters and leave an unsustainable burden of debt. Investment in infrastructure is at an all-time high globally, thus an ever-increasing number of decisions are being made now that will lock-in patterns of development for future generations. Although for the most part these investments are motivated by the desire to increase economic productivity and employment, we find that infrastructure either directly or indirectly influences the attainment of all of the Sustainable Development Goals (SDGs), including 72% of the targets. We categorize the positive and negative effects of infrastructure and the interdependencies between infrastructure sectors. To ensure that the right infrastructure is built, policymakers need to establish long-term visions for sustainable national infrastructure systems, informed by the SDGs, and develop adaptable plans that can demonstrably deliver their vision.},
+ author = {Scott Thacker and Daniel Adshead and Marianne Fay and St{\'e}phane Hallegatte and Mark Harvey and Hendrik Meller and Nicholas O'Regan and Julie Rozenberg and Graham Watkins and Jim W. Hall},
+ doi = {10.1038/s41893-019-0256-8},
+ issn = {23989629},
+ issue = {4},
+ journal = {Nature Sustainability},
+ keywords = {Civil engineering,Developing world,Sustainability},
+ month = {4},
+ pages = {324-331},
+ publisher = {Nature Publishing Group},
+ title = {Infrastructure for sustainable development},
+ url = {https://doi.org/10.1038/s41893-019-0256-8},
+ volume = {2},
+ year = {2019},
+ Bdsk-Url-1 = {https://doi.org/10.1038/s41893-019-0256-8}}
+
+@article{Koks2019,
+ abstract = {Transport infrastructure is exposed to natural hazards all around the world. Here we present the first global estimates of multi-hazard exposure and risk to road and rail infrastructure. Results reveal that ~27% of all global road and railway assets are exposed to at least one hazard and ~7.5% of all assets are exposed to a 1/100 year flood event. Global Expected Annual Damages (EAD) due to direct damage to road and railway assets range from 3.1 to 22 billion US dollars, of which ~73% is caused by surface and river flooding. Global EAD are small relative to global GDP (~0.02%). However, in some countries EAD reach 0.5 to 1% of GDP annually, which is the same order of magnitude as national transport infrastructure budgets. A cost-benefit analysis suggests that increasing flood protection would have positive returns on ~60% of roads exposed to a 1/100 year flood event.},
+ author = {E. E. Koks and J. Rozenberg and C. Zorn and M. Tariverdi and M. Vousdoukas and S. A. Fraser and J. W. Hall and S. Hallegatte},
+ doi = {10.1038/s41467-019-10442-3},
+ issn = {20411723},
+ issue = {1},
+ journal = {Nature Communications},
+ keywords = {Environmental impact,Natural hazards},
+ month = {12},
+ pages = {1-11},
+ pmid = {31239442},
+ publisher = {Nature Publishing Group},
+ title = {A global multi-hazard risk analysis of road and railway infrastructure assets},
+ url = {https://doi.org/10.1038/s41467-019-10442-3},
+ volume = {10},
+ year = {2019},
+ Bdsk-Url-1 = {https://doi.org/10.1038/s41467-019-10442-3}}
+
+@report{Hall2019,
+ author = {Jim W. Hall and Jeroen C.J.H. Aerts and Bilal M. Ayyub and Stephane Hallegatte and Mark Harvey and Xi Hu and Elco Koks and Caroline Lee and Xiawei Liao and Michael Mullan and Raghav Pant and Amelie Paszkowski and Julie Rozenberg and Fulai Sheng and Vladimir Stenek and Scott Thacker and Elina Vaananen and Lola Vallejo and Ted I.E. Veldkamp and Michelle van Vliet and Yoshihide Wada and Philip Ward and Graham Watkins and Conrad Zorn},
+ institution = {Global Commission on Adapatation},
+ title = {Adaptation of Infrastructure Systems},
+ url = {https://gca.org/reports/adaptation-of-infrastructure-systems/},
+ year = {2019},
+ Bdsk-Url-1 = {https://gca.org/reports/adaptation-of-infrastructure-systems/}}
+
+@article{THACKER201730,
+ abstract = {The complex and interdependent nature of modern critical national infrastructures provides the conditions for which localized failures can propagate within and between network systems, resulting in disruptions that are widespread and often unforeseen. Within this study, we characterize critical national infrastructures as a system-of-systems and develop methodology to perform a multi-scale disruption analysis. To achieve this, we map functional pathways between network source and sink assets across a range of operational scales. Customer demands are attributed to these pathways and are used to build a weighted network. The resultant functional path set and weighted network are used to perform a disruption analysis that encodes information on the long-range functionality within and between infrastructures, providing insights into failure propagation and the functional dependencies that exist between assets from multiple sectors. We supplement the methodological development with a detailed national scale demonstration for England and Wales using a unique representation of the integrated electricity network and the domestic flight network. The results highlight the potentially large disruptions that can result from the failure of individual electricity assets from a range of different sub-systems.},
+ author = {Scott Thacker and Raghav Pant and Jim W. Hall},
+ doi = {https://doi.org/10.1016/j.ress.2017.04.023},
+ issn = {0951-8320},
+ journal = {Reliability Engineering & System Safety},
+ keywords = {Infrastructure, Network, Interdependence, System-of-system, Multi-scale, Disruption analysis},
+ note = {Special Section: Applications of Probabilistic Graphical Models in Dependability, Diagnosis and Prognosis},
+ pages = {30-41},
+ title = {System-of-systems formulation and disruption analysis for multi-scale critical national infrastructures},
+ url = {https://www.sciencedirect.com/science/article/pii/S0951832017304994},
+ volume = {167},
+ year = {2017},
+ Bdsk-Url-1 = {https://www.sciencedirect.com/science/article/pii/S0951832017304994},
+ Bdsk-Url-2 = {https://doi.org/10.1016/j.ress.2017.04.023}}
+
+@article{Rinaldi2001,
+ author = {S. M. Rinaldi and J. P. Peerenboom and T. K. Kelly},
+ doi = {10.1109/37.969131},
+ journal = {IEEE Control Systems Magazine},
+ number = {6},
+ pages = {11-25},
+ title = {Identifying, understanding, and analyzing critical infrastructure interdependencies},
+ volume = {21},
+ year = {2001},
+ Bdsk-Url-1 = {https://doi.org/10.1109/37.969131}}
+
+@article{mann2009strategic,
+ author = {Mann, B},
+ journal = {Cabinet Office, London< http://www.cabinetoffice.gov.uk/media/308367/sfps-consultation.pdf},
+ title = {Strategic Framework and Policy Statement on Improving the Resilience of Critical Infrastructure to Disruption from Natural Hazards},
+ year = {2009}}
+
+@book{Hall2016,
+ address = {Cambridge},
+ author = {Hall, J.W. and Tran, M. and Hickford, A. and Nicholls, R.},
+ publisher = {Cambridge University Press},
+ title = {{The future of national infrastructure: A systems-of-systems approach}},
+ year = {2016}}
+
+@book{field2014climate,
+ author = {Field, Christopher B and Barros, Vicente R},
+ publisher = {Cambridge University Press},
+ title = {Climate change 2014--Impacts, adaptation and vulnerability: Regional aspects},
+ year = {2014}}
+
+@incollection{oppenheimer2015emergent,
+ author = {Oppenheimer, Michael and Campos, Maximiliano and Warren, Rachel and Birkmann, Joern and Luber, George and O'Neill, Brian and Takahashi, Kiyoshi and Brklacich, Mike and Semenov, Sergey and Licker, Rachel and others},
+ booktitle = {Climate Change 2014 Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects},
+ pages = {1039--1100},
+ publisher = {Cambridge University Press},
+ title = {Emergent risks and key vulnerabilities},
+ year = {2015}}
+
+@article{pant2018critical,
+ author = {Pant, Raghav and Thacker, Scott and Hall, Jim W and Alderson, David and Barr, Stuart},
+ journal = {Journal of Flood Risk Management},
+ number = {1},
+ pages = {22--33},
+ publisher = {Wiley Online Library},
+ title = {Critical infrastructure impact assessment due to flood exposure},
+ volume = {11},
+ year = {2018}}
+
+@article{watts2002simple,
+ author = {Watts, Duncan J},
+ journal = {Proceedings of the National Academy of Sciences},
+ number = {9},
+ pages = {5766--5771},
+ publisher = {National Acad Sciences},
+ title = {A simple model of global cascades on random networks},
+ volume = {99},
+ year = {2002}}
+
+@article{PANT2014183,
+ abstract = {While early research efforts were devoted to the protection of systems against disruptive events, be they malevolent attacks, man-made accidents, or natural disasters, recent attention has been given to the resilience, or the ability of systems to ``bounce back,'' of these events. Discussed here is a modeling paradigm for quantifying system resilience, primarily as a function of vulnerability (the adverse initial system impact of the disruption) and recoverability (the speed of system recovery). To account for uncertainty, stochastic measures of resilience are introduced, including Time to Total System Restoration, Time to Full System Service Resilience, and Time to α%-Resilience. These metrics are applied to quantify the resilience of inland waterway ports, important hubs in the flow of commodities, and the port resilience approach is deployed in a data-driven case study for the inland Port of Catoosa in Oklahoma. The contributions herein demonstrate a starting point in the development of a resilience decision making framework.},
+ author = {Raghav Pant and Kash Barker and Jose Emmanuel Ramirez-Marquez and Claudio M. Rocco},
+ doi = {https://doi.org/10.1016/j.cie.2014.01.017},
+ issn = {0360-8352},
+ journal = {Computers & Industrial Engineering},
+ keywords = {Resilience, Infrastructure systems, Vulnerability, Recoverability},
+ pages = {183-194},
+ title = {Stochastic measures of resilience and their application to container terminals},
+ url = {https://www.sciencedirect.com/science/article/pii/S0360835214000333},
+ volume = {70},
+ year = {2014},
+ Bdsk-Url-1 = {https://www.sciencedirect.com/science/article/pii/S0360835214000333},
+ Bdsk-Url-2 = {https://doi.org/10.1016/j.cie.2014.01.017}}
+
+@article{HOSSEINI201647,
+ abstract = {Modeling and evaluating the resilience of systems, potentially complex and large-scale in nature, has recently raised significant interest among both practitioners and researchers. This recent interest has resulted in several definitions of the concept of resilience and several approaches to measuring this concept, across several application domains. As such, this paper presents a review of recent research articles related to defining and quantifying resilience in various disciplines, with a focus on engineering systems. We provide a classification scheme to the approaches in the literature, focusing on qualitative and quantitative approaches and their subcategories. Addressed in this review are: an extensive coverage of the literature, an exploration of current gaps and challenges, and several directions for future research.},
+ author = {Seyedmohsen Hosseini and Kash Barker and Jose E. Ramirez-Marquez},
+ doi = {https://doi.org/10.1016/j.ress.2015.08.006},
+ issn = {0951-8320},
+ journal = {Reliability Engineering & System Safety},
+ keywords = {Resilience, Engineering systems},
+ pages = {47-61},
+ title = {A review of definitions and measures of system resilience},
+ url = {https://www.sciencedirect.com/science/article/pii/S0951832015002483},
+ volume = {145},
+ year = {2016},
+ Bdsk-Url-1 = {https://www.sciencedirect.com/science/article/pii/S0951832015002483},
+ Bdsk-Url-2 = {https://doi.org/10.1016/j.ress.2015.08.006}}
+
+@article{hickford2018resilience,
+ author = {Hickford, Adrian J and Blainey, Simon P and Hortelano, Alejandro Ortega and Pant, Raghav},
+ journal = {Environment Systems and Decisions},
+ number = {3},
+ pages = {278--291},
+ publisher = {Springer},
+ title = {Resilience engineering: theory and practice in interdependent infrastructure systems},
+ volume = {38},
+ year = {2018}}
+
+@article{trigg2016credibility,
+ author = {Trigg, MA and Birch, CE and Neal, JC and Bates, PD and Smith, A and Sampson, CC and Yamazaki, D and Hirabayashi, Y and Pappenberger, F and Dutra, E and others},
+ journal = {Environmental Research Letters},
+ number = {9},
+ pages = {094014},
+ publisher = {IOP Publishing},
+ title = {The credibility challenge for global fluvial flood risk analysis},
+ volume = {11},
+ year = {2016}}
+
+@article{wing2020toward,
+ author = {Wing, Oliver EJ and Quinn, Niall and Bates, Paul D and Neal, Jeffrey C and Smith, Andrew M and Sampson, Christopher C and Coxon, Gemma and Yamazaki, Dai and Sutanudjaja, Edwin H and Alfieri, Lorenzo},
+ journal = {Water Resources Research},
+ number = {8},
+ pages = {e2020WR027692},
+ publisher = {Wiley Online Library},
+ title = {Toward Global Stochastic River Flood Modeling},
+ volume = {56},
+ year = {2020}}
+
+@article{gassert2015aqueduct,
+ author = {Gassert, Francis and Reig, Paul and Shiao, Tien and Landis, Matt and Luck, Matt and others},
+ title = {Aqueduct global maps 2.1},
+ year = {2015}}
+
+@misc{oh2019addressing,
+ author = {Oh, Jung Eun and Espinet Alegre, Xavier and Pant, Raghav and Koks, Elco E and Russell, Tom and Schoenmakers, Roald and Hall, Jim W},
+ publisher = {World Bank},
+ title = {Addressing Climate Change in Transport: Volume 2: Pathway to Resilient Transport},
+ year = {2019}}
+
+@article{pant2016vulnerability,
+ author = {Pant, Raghav and Hall, Jim W and Blainey, Simon P},
+ journal = {European Journal of Transport and Infrastructure Research},
+ number = {1},
+ title = {Vulnerability assessment framework for interdependent critical infrastructures: case-study for Great Britain's rail network},
+ volume = {16},
+ year = {2016}}
+
+@article{jafino2020transport,
+ author = {Jafino, Bramka Arga and Kwakkel, Jan and Verbraeck, Alexander},
+ journal = {Transport Reviews},
+ number = {2},
+ pages = {241--264},
+ publisher = {Taylor \& Francis},
+ title = {Transport network criticality metrics: a comparative analysis and a guideline for selection},
+ volume = {40},
+ year = {2020}}
+
+@manual{QGIS_software,
+ author = {{QGIS Development Team}},
+ organization = {QGIS Association},
+ title = {QGIS Geographic Information System},
+ url = {https://www.qgis.org},
+ year = {2021},
+ Bdsk-Url-1 = {https://www.qgis.org}}
diff --git a/docs/lecture_08/Lecture_8.1.md b/docs/lecture_08/Lecture_8.1.md
index 5304ae4..21f984d 100644
--- a/docs/lecture_08/Lecture_8.1.md
+++ b/docs/lecture_08/Lecture_8.1.md
@@ -1,29 +1,36 @@
---
-title: Mini-Lecture 8.1 -- Introduction to regions and aggregation
+title: Mini-Lecture 8.1 -- Timeslicing in energy systems modelling
keywords:
-- Regions
-- MUSE
+- Timeslices
+- Energy modelling
+- Energy demands
authors:
- Alexander J. M. Kell
---
-This mini-lecture provides an overview of different regions within energy systems models and how these can be represented within MUSE.
+This mini-lecture provides an overview of timeslicing in energy systems modelling.
# Learning objectives
-- When to aggregate data into different regions
+- Learn why we use timeslices in energy systems models
+- Understand the importance of representative days
-# Aggregation
+# Introduction
-Regions within energy models play an important role. We often want to aggregate technoeconomic data from multiple regions into one. For example, the UK is made up of many different counties with different energy demands and supply. However, it could be the case that we do not have comprehensive data for each of these counties. We may, however, have plentiful data for the UK as a whole, or even for England, Scotland, Northern Ireland and Wales. We can therefore aggregate these data and make assumptions about the geographical locations of supply and demand.
+With energy systems models we must model how demand is met by supply. However, over the course of a year, or even over the course of 30 years we have large variations in demand and supply. For instance, the weather changes between years, seasons, and days. This all has an effect on the amount of energy that can be supplied by renewable energy sources such as solar and wind.
-This is an example of aggregation and can make the modelling process more straightforward, whilst losing a small amount of accuracy. This is because we do not need to model each individual power plant, demand centre or end-use sector. This means we can use aggregated data which are often easier to access.
+It is also true that this variation in demand has a large impact on the demand. In a particularly cold year, or on a particular cold day, energy demand may significantly increase as consumers use more energy for heating. The same may be true during a particularly warm period if people need energy for cooling systems. We therefore need to model this variability.
-We can also aggregate multiple countries into regions. For example, we can merge the European continent together. This would be especially useful if we are considering a global model. However, it must be noted that we would lose significant detail by aggregating up to a supranational level. It is up to you, the model user, to consider the trade-offs between aggregation and disaggregation. For example, if you only wanted to model a single country, it would be possible to have a single region. However, if you had good access to data at the local level, you could disaggregate the data further. It does not matter whether the region is a single country, a number of counties or at a supranational level. The regions depend on your case study and the data you have access to.
+## Representative days
+As you can probably imagine, matching supply and demand for every 30 minutes in a year is very costly in terms of computation time. If we must match supply and demand for every 30 minutes for 30 years (or more), we may end up with a very slow model in return for some gains in accuracy.
+However, it may be the case that we do not need to model a year in such high detail. In most cases, for long-term energy systems models, we can reduce the amount of detail to significantly increase the speed of the model, without losing significant accuracy [@Kell2020].
-# Summary
+A common approach is to model 4 days for each year. Each day corresponds to a season of the year and is split into 24 timeslices (which equates to a timeslice representing one hour). Therefore, we maintain the variability within a day, but also within seasons. We will lose some of the extremely hot or cold days, but that matters less when we're considering the long-term planning horizon.
+
+We do not always have to take into account entire days, to reduce the complexity further. For instance, we could have 8 days, but with only 2 timeslices (day and night). This will make the model run quickly, but may lose some detail. It is up to you, as the modeller, to find a sweet spot between accuracy and speed of computation. Various papers have been published to find this sweet spot, which you can look into in your own time [@Poncelet2017].
-In this mini-lecture we learnt about the trade-offs between aggregation and disaggregation when defining regions. We learnt that the more aggregated the model, the less granular data are required. This can be helpful in cases where the data are not available at a local level, but available at a national level.
+# Summary
+In this mini-lecture we discovered why long-term energy models consider timeslices and representative days. Through this approach we are able to maintain high accuracy whilst also reducing computation time.
diff --git a/docs/lecture_08/Lecture_8.2.md b/docs/lecture_08/Lecture_8.2.md
index 636cbf4..554f868 100644
--- a/docs/lecture_08/Lecture_8.2.md
+++ b/docs/lecture_08/Lecture_8.2.md
@@ -1,32 +1,34 @@
---
-title: Mini-Lecture 8.2 -- Disaggregation of regional data
+title: Mini-Lecture 8.2 - Technologies by timeslice
keywords:
-- Disaggregation
-- Regions
+- Energy technologies
+- Energy modelling
+- Timeslices
authors:
- Alexander J. M. Kell
---
-This mini-lecture introduces the concept of disaggregation of regions in further detail.
+In this mini-lecture we describe how different technologies can have different characteristics by timeslices.
# Learning objectives
-- When to disaggregate regional data in MUSE and energy systems models
+- Understand the different characteristics of technologies by timeslice
+- Understand how to characterise technologies by timeslice
+# Introduction
-# Disaggregation
+In the previous lecture we discovered the importance of timeslices. In this mini-lecture we will learn about how different technologies have different characteristics when it comes to timeslices, and how this can be modelled within MUSE.
-Disaggregation of regions can often be a good way of gaining a deeper understanding of the interactions between regions. For example, if you have a lot of technoeconomic data on the locations of supply and demand, then it may make sense to disaggregate regions. This will also allow the modeller to understand where there may be issues within a specific region or country.
+# Technologies by timeslices
-An interesting example of this would be for the Southeast Asia region. Laos has a good amount of hydropower availability, whereas Thailand has more solar and wind resources. If we modelled the Southeast Asia region as a single region in MUSE, we would lose information on the potential for trade between these two countries.
+Different technologies and supply sectors have different characteristics when it comes to timeslices. For instance, solar photovoltaics do not produce any energy when it is dark (for instance, at night) and produce less in the winter. Wind, on the other hand, has a completely different profile and is largely dependent on geography. Therefore, it would make sense to provide a maximum output of the technologies at different times. For instance, it would be useful if the model limited solar output at night time in the form of a maximum utilization factor. Where utilization factor is the ratio of average amount of energy output to total possible output of an energy technology if it were to run 100% of time.
-It is also interesting to see energy flows between regions within a country, similar to the Southeast Asian example. For example, if a country has a large demand centre in the south of the country, but large energy resources in the north, it could be interesting to disaggregate this country into those two nodes.
+However, it can be very difficult to turn off some technologies, such as a nuclear power plant. Nuclear power plants are expensive to turn on and can be unsafe if constantly varying their power. Also, their marginal cost, or the cost to produce 1MWh of electricity excluding capital costs, is usually much lower than other power plants such as gas or coal plants. It, therefore, makes sense that we place a minimum service factor, or minimum output allowed, on nuclear, to ensure their output does not fall below a certain level.
-Similar to the previous mini-lecture, this disaggregation is largely dependent upon your requirements and the data available to you. There is no one solution for all areas, or even for the same area and different case studies. For example, one case study may only require the modelling of a country as a single region. Another case study, however, may require the modelling of that same country by many regions. It all depends on the question you are trying to answer and the data available to you. It must be noted, that a more disaggregated case study will take longer to run in MUSE.
+Other technologies, however, such as gas power plants, can be turned on and off readily; therefore we can simply leave an average utilization factor for all the timeslices.
-# Summary
-
-In this mini-lecture we explored reasons for disaggregating a case study. We discovered that disaggregation (and aggregation) of regions depends largely on the data available to you and the questions you want to answer for your case study. However, we found out that the greater the disaggregation, the more detail the model may reveal, but the longer the model will take to run.
+All of these features exist in MUSE, and during this lecture's hands-on, we will show you how to do this within MUSE.
+# Summary
-
+In this mini-lecture we have explored the importance of characterising technologies not just by their economic data, but also by their physical characteristics. We discovered that different technologies have different outputs at different times, such as solar and wind. We also found out that nuclear power, for instance, must output a certain level to remain within a safety range.
diff --git a/docs/lecture_08/Lecture_8.3.md b/docs/lecture_08/Lecture_8.3.md
index 4939b37..099f8c3 100644
--- a/docs/lecture_08/Lecture_8.3.md
+++ b/docs/lecture_08/Lecture_8.3.md
@@ -1,40 +1,45 @@
---
-title: Mini-Lecture 8.3 -- Communicating research
+title: Mini-Lecture 8.3 - Different energy demands by timeslice
keywords:
-- Science communication
-- Visualisation
+- Energy demands
+- Timeslice
+- Energy modelling
authors:
-- Alexander J. M. Kell
+- Alexander J. M. Kell
---
-In this mini-lecture, we will explore the different ways that research can be communicated effectively.
+This mini-lecture will continue exploring the importance of timeslices in energy modelling; however, it will have a particular focus on energy demands, and how these can change by timeslice and over the years.
-# Learning objectives
-
-- Understand how to communicate the results of your research to influence policy development
+In the previous lecture we explored energy demands and timeslices. In this lecture we will have a brief recap of this, and explore how energy demand can be represented within MUSE.
-# Effective communication of research
+# Learning objectives
-Throughout this course, we have explored the useful insights and analysis that can be provided by energy systems models. We have also explored the types of results that can lead to changes in the planning of energy systems, for example by taking a more holistic approach to investment planning.
+- Understand how energy demand can change by timeslice
+- Learn how energy demand is represented in MUSE
-However, it is important that these results are communicated effectively to ensure that decision makers can fully understand the implications of these results. Effective communication also allows the methodology of the study to be better understood, which allows for the positives and limitations of the model to be explored.
+# Energy demand
-## Presenting figures
+Energy demand can come in various forms. For instance, the demand we model can be for heating or cooling in the residential sector. It is the case that these demands have different characteristics. For instance, they may have different magnitudes and different technologies which serve these demands as well as they may be able to run at different times.
-It is crucial to present figures in an understandable way. Figures are often the first thing that the audience will look at and try to understand. Figures can be used to convey the key results from your study in an impactful way. There are therefore some things that should be considered.
+Within MUSE, similarly to the supply sectors, we can model this time varying capability with timeslices. For instance, if we have 4 representative days which refer to the different seasons, we can model the high heating demand in winter and cooling demand in summer. On top of this we can vary these demands by time of day.
-The first of these is to design the figure with the target audience in mind. For example, if the audience is made of non-specialists, it may be sensible to ensure figures focus on the message without lots of technical jargon. For any audience, it is important that they understand the content of the figure, and so it is important to always include a figure caption, a legend (explaining colour coding and any symbols) and axis titles where appropriate. Finally, the colours chosen can have a large impact and so the colours should be chosen carefully with sufficient distinction between the colours.
+To do this, we must edit the demand in the `preset/Residential2050Consumption.csv` sector. An example of which is shown in Figure 8.3.1.
-## Common mistakes
+|RegionName|Timeslice|electricity|gas|heat|CO2f|wind|
+|-|-|-|-|-|-|-|
+|R1|1|0|0|3|0|0|
+|R1|2|0|0|4.5|0|0|
+|R1|3|0|0|3|0|0|
+|R1|4|0|0|4.5|0|0|
+|R1|5|0|0|9|0|0|
+|R1|6|0|0|6|0|0|
-This section focuses on the commonly made mistakes when presenting figures in research. It can often be the case that figures are too confusing and contain too much data. This can often result in the message of the figure being unclear. It may be the case that by confusing your audience you reduce the impact of your research findings. Therefore, it is advisable to make figures as simple as possible to ensure that they are understandable.
+**Figure 8.3.1:** Example input for the preset sector.
-Other common mistakes include:
-- The use of inappropriate axis for graphs which can distort results
-- Lack of figure captions, axis titles, labels or legends
+In this small example we see that there is only a demand for `heat` in the residential sector. However, this demand changes per timeslice (which are listed in the leftmost column). For instance, there is low demand for heat in timeslice 1 and a high demand for heat in timeslice 5. These timeslices refer to a single representative day, and therefore timeslice 5 has the highest demand for heat as it is in the late-evening, when people generally come home from work and turn on their radiators.
+In your models you can use datasets to disaggregate the demand into different types, or you can aggregate demand to include all gas or electricity utilised in the residential sector. This is largely dependent on the data available and the complexity of the model you would like.
# Summary
-In this mini-lecture we explored the different ways that we can communicate our research for maximum impact and ways to make figures understandable to our target audience.
-
+In this mini-lecture, we explored the importance of timeslicing for modelling demand in energy models. We also covered how this can be done within MUSE using the preset sector.
diff --git a/docs/lecture_08/Lecture_8.4.md b/docs/lecture_08/Lecture_8.4.md
index 3de3018..e4a3ea6 100644
--- a/docs/lecture_08/Lecture_8.4.md
+++ b/docs/lecture_08/Lecture_8.4.md
@@ -1,50 +1,29 @@
---
-title: Mini-Lecture 8.4 -- Oral presentations
+title: Mini-Lecture 8.4 -- Timeslicing and climate policy
keywords:
--
+- Climate policy
+- Timeslicing
authors:
-- Alexander J. M. Kell
+- Alexander J. M. Kell
---
-In this mini-lecture we will focus on effective oral communication of research.
+This mini-lecture explores the relevance of timeslicing to climate policy. We will explore how different timeslicing can affect modelling results, why it is important to consider realistic timeslicing and how these can affect policy decisions.
# Learning objectives
-- Implement tips for improved oral presentations to influence policy development
+- Understand the impact of timeslicing on modelling outputs
+- Learn how timeslicing can affect policy decisions
-# Key features of presentations
+# Timeslicing and policy
-The key features of presentations are:
+Timeslicing is a core component of an energy systems model as we have previously discussed. If one were to use an inappropriate number of timeslices in an energy systems model, it is likely that this would have major implications on the model outputs.
-- Entry point: capture the audience's attention
-- Aim: focus on what you want to achieve with the presentation
-- Structure: ensure consistency across the slides and tell a coherent story from beginning to end
-- Audience: plan for your audience and their background
-- Impact: identify key take-home points that the audience should remember
+Let's look at an example: if we were to model solar panels with an average capacity factor for the entire time horizon of the model this would assume that the solar panels can be used at night and could displace other technologies, such as gas turbines. However, in reality, solar panels contribute to the grid during the day and produce nothing at night. Therefore, we need some sort of flexibility in the system to ramp up after the sun sets. This needs to be modelled explicitly within MUSE, so to allow gas (or other technologies) to fill this gap in supply.
-Firstly, there is the entry point of the presentation. It is important to focus the audience's attention. This ensures that they are interested in the presentation and understand what will be presented. This could take the form of presenting a question that you know will interest your audience, and telling them that by the end of the presentation they will know the answer.
-
-Throughout the presentation it is important to have the aim in mind. For example, you could be trying to increase engagement with a new department. For example, if you wish to demonstrate the advantages and disadvantages of building a new coal-power plant in a particular country, the figures and data you present should be focused on this particular situation, rather than providing information about scenarios that are not affected by a new coal power plant.
-
-The structure of the presentation can be tailored to your aim. It is important to have a clear beginning, middle and end. There should be consistency across the presentation to maximise the audience's understanding.
-
-To further ensure that the audience understand and engage with the presentation, it should be designed with the audience's backgrounds and motivations in mind (see more below).
-
-Finally, it is important to consider the impact of the presentation and identify key points or policy recommendations that you would like the audience to remember.
-
-## Audiences
-
-It is important to understand the types of audience that you will be presenting to. For instance, they may be generalists or non-specialists. Or they could be scientists from different disciplines, or even scientists from the same discipline, but focusing on different topics.
-
-The presentation should be adapted depending on your audience in order to increase the audience's understanding and engagement. Technical content, for example, can be explained in a simple and understandable manner if the audience contains non-specialists. If you think that your audience, on the other hand, will have technical expertise, you can spend less time on explaining technical content. The amount of technical detail you provide may also change: if you are speaking to a policymaker they may be more interested in the results and recommendations than the modelling process.
-
-The purpose of the presentation should be optimised throughout. For example, if you are aiming to create a partnership with a new department, the presentation should have a focus on the implications of your research for that department and the benefits of the proposed partnership for the audience.
+If we take this conclusion further, it is possible to see scenarios where the intermittency of solar and wind are not modelled, and therefore we observe scenarios with a majority in solar or wind. With current technologies this is not possible, and this therefore underscores the importance of timeslicing.
+If we do not use accurate timeslicing then the model outputs can skew resulting policy, and so due care must be taken for sourcing data from different geographies.
# Summary
-In this mini-lecture we introduced some key tips for oral presentations. We explored why understanding your audience of importance, especially when introducing technical content. We also learnt that we can be strategic in our presentation planning and should optimise for the aims for which we want to achieve.
-
-This is the final lecture of the Agent-based energy systems modelling: MUSE course. After this lecture you should be in a good position to develop your own models through MUSE, which can then be used to assess the impact of different policy options.
-
-Thank you for engaging with this course, and we hope you have enjoyed the lectures, found them valuable, and find practical uses for MUSE in your research.
+In this lecture we have looked into the implications of different timeslicing decisions made when creating an energy systems model. We learnt that if we do not get this right, the investments made could be skewed and unrealistic.
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diff --git a/docs/lecture_08/bibliography.bib b/docs/lecture_08/bibliography.bib
index 178e9fd..a0fb1e6 100644
--- a/docs/lecture_08/bibliography.bib
+++ b/docs/lecture_08/bibliography.bib
@@ -1,274 +1,22 @@
-@article{Bloemendaal2019storm,
- author = {Nadia Bloemendaal and Ivan Haigh and Hans {de Moel} and S. Muis and Reindert Haarsma and Jeroen Aerts},
- date-added = {2021-08-13 12:34:08 +0200},
- date-modified = {2021-08-13 12:34:14 +0200},
- doi = {10.4121/uuid:82c1dc0d-5485-43d8-901a-ce7f26cda35d},
- month = {11},
- title = {{STORM IBTrACS present climate synthetic tropical cyclone tracks}},
- url = {https://data.4tu.nl/articles/dataset/STORM_IBTrACS_present_climate_synthetic_tropical_cyclone_tracks/12706085},
- year = {2019},
- Bdsk-Url-1 = {https://data.4tu.nl/articles/dataset/STORM_IBTrACS_present_climate_synthetic_tropical_cyclone_tracks/12706085},
- Bdsk-Url-2 = {https://doi.org/10.4121/uuid:82c1dc0d-5485-43d8-901a-ce7f26cda35d}}
-
-@report{Dawson2016,
- author = {R.J. Dawson and D. Thompson and D. Johns and S. Gosling and L. Chapman and G. Darch and G. Watson and W. Powrie and S. Bell and K. Paulson and P. Hughes and R. Wood},
- city = {London},
- institution = {Adaptation Sub-Committee for the Committee on Climate Change},
- publisher = {Paul Hughes},
- title = {UK Climate Change Risk Assessment Evidence Report: Chapter 4: Infrastructure},
- year = {2016}}
-
-@article{Steinbuks2010,
- abstract = {This paper attempts to identify the underlying causes and costs of own generation of electric power in Africa. Rigorous empirical analysis of 8483 currently operating firms in 25 African countries shows that the prevalence of own generation would remain high (at around 20%) even if power supplies were perfectly reliable, suggesting that other factors such as firms' size, emergency back-up and export regulations play a critical role in the decision to own a generator. The costs of own-generation are about three times as high as the price of purchasing (subsidized) electricity from the public grid. However, because these generators only operate a small fraction of the time, they do not greatly affect the overall average cost of power to industry. The benefits of generator ownership are also substantial. Firms with their own generators report a value of lost load of less than US$50 per hour, compared with more than US$150 per hour for those without. Nevertheless, when costs and benefits are considered side by side, the balance is not found to be significantly positive. {\copyright} 2009 Elsevier B.V.},
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- issn = {01409883},
- issue = {3},
- journal = {Energy Economics},
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- publisher = {North-Holland},
- title = {When do firms generate? Evidence on in-house electricity supply in Africa},
- volume = {32},
- year = {2010},
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-
-@generic{Hall2015,
- abstract = {The impacts of extreme events are triggering action and reaction --- sometimes in unexpected ways. Confronted by 'adaptation emergencies', the private sector is rapidly innovating climate risk management, but governments must also fulfil their responsibilities.},
- author = {Jim W. Hall and Frans Berkhout and Rowan Douglas},
- doi = {10.1038/nclimate2467},
- issn = {17586798},
- issue = {1},
- journal = {Nature Climate Change},
- keywords = {Business and industry,Governance,Government},
- month = {12},
- pages = {6-7},
- publisher = {Nature Publishing Group},
- title = {Responding to adaptation emergencies},
- url = {http://dx.doi.org/10.5270/OceanObs09-FOO},
- volume = {5},
- year = {2015},
- Bdsk-Url-1 = {http://dx.doi.org/10.5270/OceanObs09-FOO},
- Bdsk-Url-2 = {http://dx.doi.org/10.1038/nclimate2467}}
-
-@webpage{Loew2019,
- author = {Petra Loew},
- title = {The natural disasters of 2018 in figures | Munich Re Topics Online},
- url = {https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/the-natural-disasters-of-2018-in-figures.html},
- year = {2019},
- Bdsk-Url-1 = {https://www.munichre.com/topics-online/en/climate-change-and-natural-disasters/natural-disasters/the-natural-disasters-of-2018-in-figures.html}}
-
-@article{Thacker2019,
- abstract = {Infrastructure systems form the backbone of every society, providing essential services that include energy, water, waste management, transport and telecommunications. Infrastructure can also create harmful social and environmental impacts, increase vulnerability to natural disasters and leave an unsustainable burden of debt. Investment in infrastructure is at an all-time high globally, thus an ever-increasing number of decisions are being made now that will lock-in patterns of development for future generations. Although for the most part these investments are motivated by the desire to increase economic productivity and employment, we find that infrastructure either directly or indirectly influences the attainment of all of the Sustainable Development Goals (SDGs), including 72% of the targets. We categorize the positive and negative effects of infrastructure and the interdependencies between infrastructure sectors. To ensure that the right infrastructure is built, policymakers need to establish long-term visions for sustainable national infrastructure systems, informed by the SDGs, and develop adaptable plans that can demonstrably deliver their vision.},
- author = {Scott Thacker and Daniel Adshead and Marianne Fay and St{\'e}phane Hallegatte and Mark Harvey and Hendrik Meller and Nicholas O'Regan and Julie Rozenberg and Graham Watkins and Jim W. Hall},
- doi = {10.1038/s41893-019-0256-8},
- issn = {23989629},
- issue = {4},
- journal = {Nature Sustainability},
- keywords = {Civil engineering,Developing world,Sustainability},
- month = {4},
- pages = {324-331},
- publisher = {Nature Publishing Group},
- title = {Infrastructure for sustainable development},
- url = {https://doi.org/10.1038/s41893-019-0256-8},
- volume = {2},
- year = {2019},
- Bdsk-Url-1 = {https://doi.org/10.1038/s41893-019-0256-8}}
-
-@article{Koks2019,
- abstract = {Transport infrastructure is exposed to natural hazards all around the world. Here we present the first global estimates of multi-hazard exposure and risk to road and rail infrastructure. Results reveal that ~27% of all global road and railway assets are exposed to at least one hazard and ~7.5% of all assets are exposed to a 1/100 year flood event. Global Expected Annual Damages (EAD) due to direct damage to road and railway assets range from 3.1 to 22 billion US dollars, of which ~73% is caused by surface and river flooding. Global EAD are small relative to global GDP (~0.02%). However, in some countries EAD reach 0.5 to 1% of GDP annually, which is the same order of magnitude as national transport infrastructure budgets. A cost-benefit analysis suggests that increasing flood protection would have positive returns on ~60% of roads exposed to a 1/100 year flood event.},
- author = {E. E. Koks and J. Rozenberg and C. Zorn and M. Tariverdi and M. Vousdoukas and S. A. Fraser and J. W. Hall and S. Hallegatte},
- doi = {10.1038/s41467-019-10442-3},
- issn = {20411723},
- issue = {1},
- journal = {Nature Communications},
- keywords = {Environmental impact,Natural hazards},
- month = {12},
- pages = {1-11},
- pmid = {31239442},
- publisher = {Nature Publishing Group},
- title = {A global multi-hazard risk analysis of road and railway infrastructure assets},
- url = {https://doi.org/10.1038/s41467-019-10442-3},
- volume = {10},
- year = {2019},
- Bdsk-Url-1 = {https://doi.org/10.1038/s41467-019-10442-3}}
-
-@report{Hall2019,
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- institution = {Global Commission on Adapatation},
- title = {Adaptation of Infrastructure Systems},
- url = {https://gca.org/reports/adaptation-of-infrastructure-systems/},
- year = {2019},
- Bdsk-Url-1 = {https://gca.org/reports/adaptation-of-infrastructure-systems/}}
-
-@article{THACKER201730,
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- issn = {0951-8320},
- journal = {Reliability Engineering & System Safety},
- keywords = {Infrastructure, Network, Interdependence, System-of-system, Multi-scale, Disruption analysis},
- note = {Special Section: Applications of Probabilistic Graphical Models in Dependability, Diagnosis and Prognosis},
- pages = {30-41},
- title = {System-of-systems formulation and disruption analysis for multi-scale critical national infrastructures},
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- Bdsk-Url-2 = {https://doi.org/10.1016/j.ress.2017.04.023}}
-
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-
-@article{mann2009strategic,
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-
-@book{Hall2016,
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- publisher = {Cambridge University Press},
- title = {{The future of national infrastructure: A systems-of-systems approach}},
- year = {2016}}
-
-@book{field2014climate,
- author = {Field, Christopher B and Barros, Vicente R},
- publisher = {Cambridge University Press},
- title = {Climate change 2014--Impacts, adaptation and vulnerability: Regional aspects},
- year = {2014}}
-
-@incollection{oppenheimer2015emergent,
- author = {Oppenheimer, Michael and Campos, Maximiliano and Warren, Rachel and Birkmann, Joern and Luber, George and O'Neill, Brian and Takahashi, Kiyoshi and Brklacich, Mike and Semenov, Sergey and Licker, Rachel and others},
- booktitle = {Climate Change 2014 Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects},
- pages = {1039--1100},
- publisher = {Cambridge University Press},
- title = {Emergent risks and key vulnerabilities},
- year = {2015}}
-
-@article{pant2018critical,
- author = {Pant, Raghav and Thacker, Scott and Hall, Jim W and Alderson, David and Barr, Stuart},
- journal = {Journal of Flood Risk Management},
- number = {1},
- pages = {22--33},
- publisher = {Wiley Online Library},
- title = {Critical infrastructure impact assessment due to flood exposure},
- volume = {11},
- year = {2018}}
-
-@article{watts2002simple,
- author = {Watts, Duncan J},
- journal = {Proceedings of the National Academy of Sciences},
- number = {9},
- pages = {5766--5771},
- publisher = {National Acad Sciences},
- title = {A simple model of global cascades on random networks},
- volume = {99},
- year = {2002}}
-
-@article{PANT2014183,
- abstract = {While early research efforts were devoted to the protection of systems against disruptive events, be they malevolent attacks, man-made accidents, or natural disasters, recent attention has been given to the resilience, or the ability of systems to ``bounce back,'' of these events. Discussed here is a modeling paradigm for quantifying system resilience, primarily as a function of vulnerability (the adverse initial system impact of the disruption) and recoverability (the speed of system recovery). To account for uncertainty, stochastic measures of resilience are introduced, including Time to Total System Restoration, Time to Full System Service Resilience, and Time to α%-Resilience. These metrics are applied to quantify the resilience of inland waterway ports, important hubs in the flow of commodities, and the port resilience approach is deployed in a data-driven case study for the inland Port of Catoosa in Oklahoma. The contributions herein demonstrate a starting point in the development of a resilience decision making framework.},
- author = {Raghav Pant and Kash Barker and Jose Emmanuel Ramirez-Marquez and Claudio M. Rocco},
- doi = {https://doi.org/10.1016/j.cie.2014.01.017},
- issn = {0360-8352},
- journal = {Computers & Industrial Engineering},
- keywords = {Resilience, Infrastructure systems, Vulnerability, Recoverability},
- pages = {183-194},
- title = {Stochastic measures of resilience and their application to container terminals},
- url = {https://www.sciencedirect.com/science/article/pii/S0360835214000333},
- volume = {70},
- year = {2014},
- Bdsk-Url-1 = {https://www.sciencedirect.com/science/article/pii/S0360835214000333},
- Bdsk-Url-2 = {https://doi.org/10.1016/j.cie.2014.01.017}}
-
-@article{HOSSEINI201647,
- abstract = {Modeling and evaluating the resilience of systems, potentially complex and large-scale in nature, has recently raised significant interest among both practitioners and researchers. This recent interest has resulted in several definitions of the concept of resilience and several approaches to measuring this concept, across several application domains. As such, this paper presents a review of recent research articles related to defining and quantifying resilience in various disciplines, with a focus on engineering systems. We provide a classification scheme to the approaches in the literature, focusing on qualitative and quantitative approaches and their subcategories. Addressed in this review are: an extensive coverage of the literature, an exploration of current gaps and challenges, and several directions for future research.},
- author = {Seyedmohsen Hosseini and Kash Barker and Jose E. Ramirez-Marquez},
- doi = {https://doi.org/10.1016/j.ress.2015.08.006},
- issn = {0951-8320},
- journal = {Reliability Engineering & System Safety},
- keywords = {Resilience, Engineering systems},
- pages = {47-61},
- title = {A review of definitions and measures of system resilience},
- url = {https://www.sciencedirect.com/science/article/pii/S0951832015002483},
- volume = {145},
- year = {2016},
- Bdsk-Url-1 = {https://www.sciencedirect.com/science/article/pii/S0951832015002483},
- Bdsk-Url-2 = {https://doi.org/10.1016/j.ress.2015.08.006}}
-
-@article{hickford2018resilience,
- author = {Hickford, Adrian J and Blainey, Simon P and Hortelano, Alejandro Ortega and Pant, Raghav},
- journal = {Environment Systems and Decisions},
- number = {3},
- pages = {278--291},
- publisher = {Springer},
- title = {Resilience engineering: theory and practice in interdependent infrastructure systems},
- volume = {38},
- year = {2018}}
-
-@article{trigg2016credibility,
- author = {Trigg, MA and Birch, CE and Neal, JC and Bates, PD and Smith, A and Sampson, CC and Yamazaki, D and Hirabayashi, Y and Pappenberger, F and Dutra, E and others},
- journal = {Environmental Research Letters},
- number = {9},
- pages = {094014},
- publisher = {IOP Publishing},
- title = {The credibility challenge for global fluvial flood risk analysis},
- volume = {11},
- year = {2016}}
-
-@article{wing2020toward,
- author = {Wing, Oliver EJ and Quinn, Niall and Bates, Paul D and Neal, Jeffrey C and Smith, Andrew M and Sampson, Christopher C and Coxon, Gemma and Yamazaki, Dai and Sutanudjaja, Edwin H and Alfieri, Lorenzo},
- journal = {Water Resources Research},
- number = {8},
- pages = {e2020WR027692},
- publisher = {Wiley Online Library},
- title = {Toward Global Stochastic River Flood Modeling},
- volume = {56},
- year = {2020}}
-
-@article{gassert2015aqueduct,
- author = {Gassert, Francis and Reig, Paul and Shiao, Tien and Landis, Matt and Luck, Matt and others},
- title = {Aqueduct global maps 2.1},
- year = {2015}}
-
-@misc{oh2019addressing,
- author = {Oh, Jung Eun and Espinet Alegre, Xavier and Pant, Raghav and Koks, Elco E and Russell, Tom and Schoenmakers, Roald and Hall, Jim W},
- publisher = {World Bank},
- title = {Addressing Climate Change in Transport: Volume 2: Pathway to Resilient Transport},
- year = {2019}}
-
-@article{pant2016vulnerability,
- author = {Pant, Raghav and Hall, Jim W and Blainey, Simon P},
- journal = {European Journal of Transport and Infrastructure Research},
- number = {1},
- title = {Vulnerability assessment framework for interdependent critical infrastructures: case-study for Great Britain's rail network},
- volume = {16},
- year = {2016}}
-
-@article{jafino2020transport,
- author = {Jafino, Bramka Arga and Kwakkel, Jan and Verbraeck, Alexander},
- journal = {Transport Reviews},
- number = {2},
- pages = {241--264},
- publisher = {Taylor \& Francis},
- title = {Transport network criticality metrics: a comparative analysis and a guideline for selection},
- volume = {40},
- year = {2020}}
-
-@manual{QGIS_software,
- author = {{QGIS Development Team}},
- organization = {QGIS Association},
- title = {QGIS Geographic Information System},
- url = {https://www.qgis.org},
- year = {2021},
- Bdsk-Url-1 = {https://www.qgis.org}}
+@article{Kell2020,
+ author = {Kell, Alexander J. M. and Forshaw, Matthew and McGough, A. Stephen},
+ isbn = {9781450366717},
+ journal = {The Eleventh ACM International Conference on Future Energy Systems (e-Energy'20)},
+ keywords = {agent-based modelling,en-,energy market simulation,ergy models,genetic algorithm,long-term,op-,policy,simulation,validation},
+ mendeley-groups = {Energy Modelling},
+ title = {{Long-Term Electricity Market Agent Based Model Validation using Genetic Algorithm based Optimization}},
+ year = {2020}
+}
+@article{Poncelet2017,
+ abstract = {Due to computational restrictions, energy-system optimization models (ESOMs) and generation expansion planning models (GEPMs) frequently represent intra-annual variations in demand and supply by using the data of a limited number of representative historical days. The vast majority of the current approaches to select a representative set of days relies on either simple heuristics or clustering algorithms and comparison of different approaches is restricted to different clustering algorithms. This paper contributes by: (i) proposing criteria and metrics for evaluating representativeness, (ii) providing a novel optimization-based approach to select a representative set of days and (iii) evaluating and comparing the developed approach to multiple approaches available from the literature. The developed optimization-based approach is shown to achieve more accurate results than the approaches available from the literature. As a consequence, by applying this approach to select a representative set of days, the accuracy of ESOMs/GEPMs can be improved without increasing the computational cost. The main disadvantage is that the approach is computationally costly and requires an implementation effort.},
+ author = {Poncelet, Kris and Hoschle, Hanspeter and Delarue, Erik and Virag, Ana and Drhaeseleer, William},
+ issn = {08858950},
+ journal = {IEEE Transactions on Power Systems},
+ keywords = {Energy-system planning,generation expansion planning,power system economics,power system modeling,wind energy integration},
+ mendeley-groups = {Electricity Market Simulations/Selecting Representative RES Days},
+ number = {3},
+ pages = {1936--1948},
+ title = {{Selecting representative days for capturing the implications of integrating intermittent renewables in generation expansion planning problems}},
+ volume = {32},
+ year = {2017}
+}
diff --git a/docs/lecture_09/Lecture_9.1.md b/docs/lecture_09/Lecture_9.1.md
new file mode 100644
index 0000000..60ea1e2
--- /dev/null
+++ b/docs/lecture_09/Lecture_9.1.md
@@ -0,0 +1,39 @@
+---
+title: Mini-Lecture 9.1 -- Communicating research
+keywords:
+- Science communication
+- Visualisation
+authors:
+- Alexander J. M. Kell
+---
+
+In this mini-lecture, we will explore the different ways that research can be communicated effectively.
+
+# Learning objectives
+
+- Understand how to communicate the results of your research to influence policy development
+
+# Effective communication of research
+
+Throughout this course, we have explored the useful insights and analysis that can be provided by energy systems models. We have also explored the types of results that can lead to changes in the planning of energy systems, for example by taking a more holistic approach to investment planning.
+
+However, it is important that these results are communicated effectively to ensure that decision makers can fully understand the implications of these results. Effective communication also allows the methodology of the study to be better understood, which allows for the positives and limitations of the model to be explored.
+
+## Presenting figures
+
+It is crucial to present figures in an understandable way. Figures are often the first thing that the audience will look at and try to understand. Figures can be used to convey the key results from your study in an impactful way. There are therefore some things that should be considered.
+
+The first of these is to design the figure with the target audience in mind. For example, if the audience is made of non-specialists, it may be sensible to ensure figures focus on the message without lots of technical jargon. For any audience, it is important that they understand the content of the figure, and so it is important to always include a figure caption, a legend (explaining colour coding and any symbols) and axis titles where appropriate. Finally, the colours chosen can have a large impact and so the colours should be chosen carefully with sufficient distinction between the colours.
+
+## Common mistakes
+
+This section focuses on the commonly made mistakes when presenting figures in research. It can often be the case that figures are too confusing and contain too much data. This can often result in the message of the figure being unclear. It may be the case that by confusing your audience you reduce the impact of your research findings. Therefore, it is advisable to make figures as simple as possible to ensure that they are understandable.
+
+Other common mistakes include:
+
+- The use of inappropriate axis for graphs which can distort results
+- Lack of figure captions, axis titles, labels or legends
+
+# Summary
+
+In this mini-lecture we explored the different ways that we can communicate our research for maximum impact and ways to make figures understandable to our target audience.
diff --git a/docs/lecture_09/Lecture_9.2.md b/docs/lecture_09/Lecture_9.2.md
new file mode 100644
index 0000000..b490c28
--- /dev/null
+++ b/docs/lecture_09/Lecture_9.2.md
@@ -0,0 +1,49 @@
+---
+title: Mini-Lecture 9.2 -- Oral presentations
+keywords:
+-
+authors:
+- Alexander J. M. Kell
+---
+
+In this mini-lecture we will focus on effective oral communication of research.
+
+# Learning objectives
+
+- Implement tips for improved oral presentations to influence policy development
+
+# Key features of presentations
+
+The key features of presentations are:
+
+- Entry point: capture the audience's attention
+- Aim: focus on what you want to achieve with the presentation
+- Structure: ensure consistency across the slides and tell a coherent story from beginning to end
+- Audience: plan for your audience and their background
+- Impact: identify key take-home points that the audience should remember
+
+Firstly, there is the entry point of the presentation. It is important to focus the audience's attention. This ensures that they are interested in the presentation and understand what will be presented. This could take the form of presenting a question that you know will interest your audience, and telling them that by the end of the presentation they will know the answer.
+
+Throughout the presentation it is important to have the aim in mind. For example, you could be trying to increase engagement with a new department. For example, if you wish to demonstrate the advantages and disadvantages of building a new coal-power plant in a particular country, the figures and data you present should be focused on this particular situation, rather than providing information about scenarios that are not affected by a new coal power plant.
+
+The structure of the presentation can be tailored to your aim. It is important to have a clear beginning, middle and end. There should be consistency across the presentation to maximise the audience's understanding.
+
+To further ensure that the audience understand and engage with the presentation, it should be designed with the audience's backgrounds and motivations in mind (see more below).
+
+Finally, it is important to consider the impact of the presentation and identify key points or policy recommendations that you would like the audience to remember.
+
+## Audiences
+
+It is important to understand the types of audience that you will be presenting to. For instance, they may be generalists or non-specialists. Or they could be scientists from different disciplines, or even scientists from the same discipline, but focusing on different topics.
+
+The presentation should be adapted depending on your audience in order to increase the audience's understanding and engagement. Technical content, for example, can be explained in a simple and understandable manner if the audience contains non-specialists. If you think that your audience, on the other hand, will have technical expertise, you can spend less time on explaining technical content. The amount of technical detail you provide may also change: if you are speaking to a policymaker they may be more interested in the results and recommendations than the modelling process.
+
+The purpose of the presentation should be optimised throughout. For example, if you are aiming to create a partnership with a new department, the presentation should have a focus on the implications of your research for that department and the benefits of the proposed partnership for the audience.
+
+# Summary
+
+In this mini-lecture we introduced some key tips for oral presentations. We explored why understanding your audience of importance, especially when introducing technical content. We also learnt that we can be strategic in our presentation planning and should optimise for the aims for which we want to achieve.
+
+This is the final lecture of the Agent-based energy systems modelling: MUSE course. After this lecture you should be in a good position to develop your own models through MUSE, which can then be used to assess the impact of different policy options.
+
+Thank you for engaging with this course, and we hope you have enjoyed the lectures, found them valuable, and find practical uses for MUSE in your research.
diff --git a/docs/running-muse-example.ipynb b/docs/running-muse-example.ipynb
new file mode 100644
index 0000000..aa99d7d
--- /dev/null
+++ b/docs/running-muse-example.ipynb
@@ -0,0 +1,207 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Running MUSE\n",
+ "\n",
+ "Once MUSE have been installed, we can run an example. To start with, we will run one of the built-in MUSE examples. If you are using MUSE within a virtual environment, make sure you have it activated (refer back to exercise 1 if you need help with this). \n",
+ "\n",
+ "You should be able to run the default `muse` example running the following command in the terminal:\n",
+ "\n",
+ "```bash\n",
+ "python -m muse --model default\n",
+ "```\n",
+ "\n",
+ "If running correctly, your prompt should output text similar to [this](https://muse-os.readthedocs.io/en/v1.2.1/example-output.html). \n",
+ "You can check the available built-in models, as well as information on other input arguments, with:\n",
+ "\n",
+ "```bash\n",
+ "python -m muse -h\n",
+ "```\n",
+ "\n",
+ "A common use case is to take one of the built-in models as the starting point to create your own model. This is the approach we will take in the hands-on exercises in this course. To copy the files for the default model, run:\n",
+ "\n",
+ "```bash\n",
+ "python -m muse --model default --copy path/to/copy/the/model/to\n",
+ "```\n",
+ "\n",
+ "This will create a folder called `model` in the specified path. Navigate to this folder using the `cd` command, and use `ls` to see the contents of this folder.\n",
+ "We can then run the simulation using the following command:\n",
+ "\n",
+ "```bash\n",
+ "python -m muse settings.toml\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Results\n",
+ "\n",
+ "If the default MUSE example has run successfully, you should now have a folder called `Results` in the current working directory.\n",
+ "\n",
+ "This directory should contain two files:\n",
+ "\n",
+ "- `MCACapacity.csv`: contains information about the capacity each agent has per technology per benchmark year. Each benchmark year is the modelled year in the `settings.toml` file. In our example, this is 2020, 2025, ..., 2050.\n",
+ "\n",
+ "- `MCAPrices.csv`: has the converged price of each commodity per benchmark year and timeslice. eg. the cost of electricity at night in 2020.\n",
+ "\n",
+ "Additional files can be added by modifying `settings.toml`, as will be shown in future exercises.\n",
+ "\n",
+ "For the hands-on exercises in this course, we will use Python and [Jupyter Notebook](https://jupyter.org) to visualise these simulation results. You can, however, visualise the results using any language or program of your choice (for example Excel, R, MATLAB), but will get the most out of these exercises if you use Jupyter Notebook."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installing Jupyter\n",
+ "\n",
+ "First, you will need to install Jupyter Notebook in your environment, which you can do with the following command:\n",
+ "```bash\n",
+ "python -m pip install jupyter\n",
+ "```\n",
+ "\n",
+ "Then, once this has been installed, you can start Jupyter Notebook by running the following command:\n",
+ "```bash\n",
+ "python -m jupyter notebook\n",
+ "```\n",
+ "\n",
+ "A web browser should now open up with a URL such as the following: `http://localhost:8888/tree`. If it doesn't, copy and paste the command as directed in the terminal. This will likely take the form of:\n",
+ "\n",
+ "```bash\n",
+ "http://localhost:8888/?token=xxxxxxxxxx\n",
+ "```\n",
+ "With `xxxxxxxxxx` a very long collection of letters and numbers. Once you are on the page, you will be able to navigate to a location of your choice and create a new file, by clicking the `New` button in the top right, followed by `Python 3`. You should then be able to proceed and follow the tutorials in this documentation.\n",
+ "\n",
+ "### Missing packages\n",
+ "\n",
+ "If, when running a cell, you get any errors such as:\n",
+ "\n",
+ "```bash\n",
+ "ModuleNotFoundError: No module named 'pandas'\n",
+ "```\n",
+ "\n",
+ "Then you are trying to use a package (`pandas` in the example) that is not available in the current environment. It is possible to install the missing packages by running the following in the Jupyter notebook:\n",
+ "\n",
+ "```bash\n",
+ "!pip install pandas\n",
+ "```\n",
+ "\n",
+ "The package will be installed in whatever virtual environment Jupyter is running in."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Visualisation\n",
+ "\n",
+ "First, we need to load the appropriate packages required to load and visualise the results, which you can do by running the following Python commands in a notebook cell:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "import pandas as pd"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Next, we will load `MCACapacity.csv` file and print the first five rows of the table using the pandas library:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "mca_capacity = pd.read_csv(\"Results/MCACapacity.csv\")\n",
+ "mca_capacity.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Finally, we will visualise the data for each of the sectors, with capacity on the y-axis and year on the x-axis. Don't worry too much about the code if some of it is unfamiliar - we effectively split the data into each sector, sum the capacity for each technology, and then create a stacked bar chart for each."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "fig, axes = plt.subplots(1, 3)\n",
+ "for ax, (sector_name, sector_data) in zip(axes, mca_capacity.groupby(\"sector\")):\n",
+ " sector_capacity = sector_data.groupby([\"year\", \"technology\"]).sum().reset_index()\n",
+ " sector_capacity.pivot(index=\"year\", columns=\"technology\", values=\"capacity\").plot(\n",
+ " kind=\"bar\", stacked=True, ax=ax\n",
+ " )\n",
+ " ax.set_ylabel(\"Capacity (PJ)\")\n",
+ " ax.set_xlabel(\"Year\")\n",
+ " ax.set_title(f\"{sector_name.capitalize()} Sector:\", fontsize=10)\n",
+ " ax.legend(title=None, prop={\"size\": 8})\n",
+ " ax.tick_params(axis=\"both\", labelsize=8)\n",
+ "\n",
+ "fig.set_size_inches(8, 2.5)\n",
+ "fig.subplots_adjust(wspace=0.5)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this toy example, we can see that the end-use technology of choice in the residential sector becomes a heatpump, which displaces the gas boiler. To account for the increase in demand for electricity, the agent invests heavily in wind turbines.\n",
+ "\n",
+ "Note, that the units are in petajoules (PJ). MUSE requires consistent units across each of the sectors, and each of the input files (which we will see later). The model does not make any unit conversion internally."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Summary\n",
+ "\n",
+ "In this exercise we have shown how to run the default model that comes with MUSE, and how to visualise the investment decisions made in the simulation. In future exercises we will show how to modify the input files to run different, more interesting, scenarios."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.3"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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diff --git a/quizzes/Hands-on/HO2/CCG Lecture Quizzes Templates.docx b/quizzes/Lectures/Lecture_9/CCG Lecture Quizzes 9.docx
similarity index 87%
rename from quizzes/Hands-on/HO2/CCG Lecture Quizzes Templates.docx
rename to quizzes/Lectures/Lecture_9/CCG Lecture Quizzes 9.docx
index 5022854..267568f 100644
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diff --git a/readme.md b/readme.md
index 3ad91b9..369b8eb 100644
--- a/readme.md
+++ b/readme.md
@@ -1,32 +1,41 @@
# MUSE: Teaching Kit
-This repository contains the teaching material developed by Imperial College London
-for the course "MUSE: Agent-based energy systems modelling" developed under the
-Climate Compatible Growth project.
+This repository contains the teaching material developed by Imperial College London for the course "MUSE: Agent-based energy systems modelling" developed under the Climate Compatible Growth project.
-The course is published on [Open Learn Create](fill-here)
-and is free to learners.
+The course is published on [Open Learn Create](https://www.open.edu/openlearncreate/course/view.php?id=11717) and is free to learners.
The course material is licensed under a [Creative Commons BY 4.0 License](https://creativecommons.org/licenses/by/4.0).
-This license allows you to use, remix and publish the course material as long as you give correct
-attribution. Please use the following citation:
+This license allows you to use, remix and publish the course material as long as you give correct attribution.
+Please use the following citation:
Alexander J. M. Kell, Sara Giarola, Adam Hawkes. (2022, August 6). ClimateCompatibleGrowth/muse_teaching_kit: Initial release of lecture blocks. Zenodo. https://doi.org/10.5281/zenodo.5166742
-## Creating the teaching material
+## Setup
+
+Generating the course files requires Python, and the dependencies listed in the file `requirements.txt`.
+To create a suitable Python environment, run:
+
+ python -m venv .venv
+ source .venv/bin/activate
+ python -m pip install -r requirements.txt
+ python -m ipykernel install --name=muse_kernel
-The teaching material is rendered to HTML using a bash script.
-A key dependency is [pandoc](https://pandoc.org/), which is used to convert the markdown lecture block files into HTML.
+The latest version of the course was generated using Python version 3.12, and MUSE version 1.2.1.
+
+You must also have [pandoc](https://pandoc.org/) installed on your machine.
+
+## Creating the teaching material
1. To generate the HTML files in the `_build` folder, run:
- bash scripts/create_html.sh
+ bash scripts/create_hands_on.sh
+ bash scripts/create_lectures.sh
-2. To create a SCORM package for each of the lecture blocks, you'll need the Python package [scorm_package](https://github.com/ClimateCompatibleGrowth/scorm_package). Then run the deployment script::
+2. To create a SCORM package for each of the lecture blocks, run the deployment script:
bash scripts/deploy.sh
- This creates a zip archive for each lecture e.g. for `lecture17` from the material in folder `lecture_17`.
+ This creates a zip archive for each lecture e.g. for `lecture4` from the material in folder `lecture_4`.
Each zip files contains the following special files for a SCORM package:
adlcp_rootv1p2.xsd
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..6d0972f
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+muse_os==1.2.1rc1
+matplotlib
+seaborn
+setuptools
+ipykernel
+git+https://github.com/ClimateCompatibleGrowth/scorm_package.git
diff --git a/review/reviewed/hands_on/Hands-on 0903 upto and including hands-on3_AK.docx b/review/reviewed/hands_on/Hands-on 0903 upto and including hands-on3_AK.docx
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deleted file mode 100644
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diff --git a/review/reviewed/~$cture 7 with SP changes 07.03.docx b/review/reviewed/~$cture 7 with SP changes 07.03.docx
deleted file mode 100644
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diff --git a/scripts/bib.sed b/scripts/bib.sed
index 9ede00f..4bc5f5a 100644
--- a/scripts/bib.sed
+++ b/scripts/bib.sed
@@ -1,4 +1,4 @@
s/(\@web_page)/\@webpage/g
s/(\@book_section)/\@inbook/g
s/(\@newspaper_article)/\@article/g
-s/\{\@/\{/g
\ No newline at end of file
+s/\{\@/\{/g
diff --git a/scripts/create_hands_on.sh b/scripts/create_hands_on.sh
index 019c868..6a612d4 100644
--- a/scripts/create_hands_on.sh
+++ b/scripts/create_hands_on.sh
@@ -1,34 +1,67 @@
#!/bin/bash
-for handson in docs/hands_on_*; do
-for filename in $handson/*.md; do
- echo Converting "$filename"
-
- MD_NAME=$(basename "$filename" .md)
- DIR_NAME=$handson
- BIBFILE=$DIR_NAME/*.bib
- OUTPUT=_build/$(basename $handson)
-
- CSL=https://climatecompatiblegrowth.github.io/style/csl-style.css
- PAN=https://climatecompatiblegrowth.github.io/style/pandoc.css
- CITESTYLE=https://raw.githubusercontent.com/citation-style-language/styles/master/chicago-author-date.csl
-
- MD_TMP=$MD_NAME.tmp.md
- cat $filename > $MD_TMP
-
- if test -f "$BIBFILE"; then
- # Render citations and write bibliography to HTML
- echo "" >> $MD_TMP
- echo "# Bibliography" >> $MD_TMP
- pandoc --mathjax --standalone --css $PAN --css $CSL --citeproc $MD_TMP --bibliography $BIBFILE -o $OUTPUT/$MD_NAME.html --csl $CITESTYLE
- else
- pandoc --mathjax --standalone --css $PAN --css $CSL $MD_TMP -o $OUTPUT/$MD_NAME.html
- fi
- if test -d "$OUTPUT/assets"; then
- rm -r $OUTPUT/assets/*
- fi
- mkdir -p $OUTPUT/assets
- cp -f $DIR_NAME/assets/* $OUTPUT/assets
- rm $MD_TMP
-done;
-done;
-# Clean up
\ No newline at end of file
+
+convert_notebook() {
+ # Run notebook, convert to HTML and return path to the output file
+ local notebook=$1
+ jupyter nbconvert --to html --execute --ExecutePreprocessor.kernel_name=muse_kernel --template classic "$notebook" --output-dir="$output_dir"
+ local output_file="${output_dir}/$(basename "${notebook%.*}.html")"
+ echo "$output_file"
+}
+
+update_relative_links() {
+ # Update relative links in HTML file
+ local html_file=$1
+ local base_url=$2
+ sed -i '' -E "s|href=\"([^\"]*).rst\"|href=\"$base_url\1.html\"|g" "$html_file"
+}
+
+update_github_links() {
+ local html_file=$1
+ local new_tag=$2
+ sed -i '' 's|\(https://github.com/EnergySystemsModellingLab/MUSE_OS/blob/\)[^/]*|\1'"$new_tag"'|g' "$html_file"
+}
+
+# Prepare build directory
+output_dir="_build"
+mkdir -p "$output_dir"
+
+# Hands-on 1
+notebook="docs/installation.ipynb"
+convert_notebook $notebook
+
+# Hands-on 2
+notebook="docs/running-muse-example.ipynb"
+(cd docs && rm -rf Results && muse --model default)
+convert_notebook $notebook
+
+# Hands-on 3-9
+notebooks=(MUSE_OS/docs/user-guide/*.ipynb)
+for notebook in "${notebooks[@]}"; do
+ html_file=$(convert_notebook "$notebook")
+ update_relative_links $html_file "https://muse-os.readthedocs.io/en/v1.2.1/user-guide/"
+ update_github_links $html_file "v1.2.1"
+done
+
+# Specify tutorial order
+order=(
+ "installation"
+ "running-muse-example"
+ "add-solar"
+ "additional-service-demand"
+ "add-gdp-correlation-demand"
+ "add-agent"
+ "add-region"
+ "modify-timing-data"
+ "min-max-timeslice-constraints"
+)
+
+# Rename files
+for i in "${!order[@]}"; do
+ file="${order[$i]}"
+ number=$((i + 1))
+ new_name="hands_on_$number"
+ mv -f "$output_dir/$file.html" "$output_dir/$new_name.html"
+
+ # Modify title
+ sed -i '' "s/\(]*>\)\([^<]*\)/\1Hands-on exercise ${number}: \2/" "$output_dir/$new_name.html"
+done
diff --git a/scripts/create_html.sh b/scripts/create_lectures.sh
similarity index 81%
rename from scripts/create_html.sh
rename to scripts/create_lectures.sh
index 20b7f79..00e29cb 100644
--- a/scripts/create_html.sh
+++ b/scripts/create_lectures.sh
@@ -1,28 +1,37 @@
#!/bin/bash
+
+# Prepare build directory
+output_dir="_build"
+mkdir -p "$output_dir"
+
for lecture in docs/lecture_*; do
for filename in $lecture/*.md; do
echo Converting "$filename"
-
+
MD_NAME=$(basename "$filename" .md)
DIR_NAME=$lecture
BIBFILE=$DIR_NAME/bibliography.bib
- OUTPUT=_build/$(basename $lecture)
+ OUTPUT=$output_dir/$(basename $lecture)
+ mkdir -p $OUTPUT
+ # CSS styles
CSL=https://climatecompatiblegrowth.github.io/style/csl-style.css
PAN=https://climatecompatiblegrowth.github.io/style/pandoc.css
CITESTYLE=https://raw.githubusercontent.com/citation-style-language/styles/master/chicago-author-date.csl
+ # Convert Markdown to HTML
MD_TMP=$MD_NAME.tmp.md
cat $filename > $MD_TMP
-
if test -f "$BIBFILE"; then
# Render citations and write bibliography to HTML
echo "" >> $MD_TMP
echo "# Bibliography" >> $MD_TMP
pandoc --mathjax --standalone --css $PAN --css $CSL --citeproc $MD_TMP --bibliography $BIBFILE -o $OUTPUT/$MD_NAME.html --csl $CITESTYLE
else
- pandoc --mathjax --standalone --css $PAN --css $CSL $MD_TMP -o $OUTPUT/$BNAME.html
+ pandoc --mathjax --standalone --css $PAN --css $CSL $MD_TMP -o $OUTPUT/$MD_NAME.html
fi
+
+ # Copy assets
if test -d "$OUTPUT/assets"; then
rm -r $OUTPUT/assets/*
fi
@@ -31,4 +40,3 @@ for filename in $lecture/*.md; do
rm $MD_TMP
done;
done;
-# Clean up
diff --git a/scripts/deploy.sh b/scripts/deploy.sh
index 33c7557..01f24d0 100644
--- a/scripts/deploy.sh
+++ b/scripts/deploy.sh
@@ -1,3 +1,6 @@
+deploy_dir="_deploy"
+mkdir -p "$deploy_dir"
+
rm -r _deploy/*
for lecture in _build/lecture_*; do
@@ -6,4 +9,4 @@ for lecture in _build/lecture_*; do
echo $OUTPUT $lecture $NUMBER
scorm $OUTPUT $lecture $NUMBER
-done;
\ No newline at end of file
+done;
