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88 changes: 19 additions & 69 deletions docs/lecture_03/Lecture_3.1.md
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---
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.

![](assets/Figure_3.1.1.png){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.
# Learning objectives

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.
- Understand the role of the residential sector, its technologies and the main energy and societal challenges

![](assets/Figure_3.1.2.png){width=100%}
# Overview of the residential sector and its demands?

**Figure 3.1.2:** Variations of energy demand for the residential sector by population types [@Olaniyan2018]
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.

![](assets/Figure_3.1.1.png){width=100%}

## Long-term variations in energy demands
**Figure 3.1.1:** Residential sector in Italy and the different demands [@en12112055]. (Note: DHW refers to Domestic Hot Water).

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.
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.

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.
## Residential sector technologies

![](assets/Figure_3.1.3.png){width=100%}
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.

**Figure 3.1.3:** Long-term energy consumption by source
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.

## Capacity expansion planning
## Residential sector in MUSE

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)?
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.

![](assets/Figure_3.1.4.png){width=100%}

**Figure 3.1.4:** 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 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.

# 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.
56 changes: 28 additions & 28 deletions docs/lecture_03/Lecture_3.2.md
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---
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
![](assets/Figure_3.2.1.jpg){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 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.

- 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.
- Electric vehicles
- Conventional vehicles

# Summary
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.

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.
# 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.



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