Optimizer WIP

README.md

@@ -144,3 +144,17 @@ wt.region_from_loc(
 # get shape files for all regions of a signal type
 wt.get_maps_json('co2_moer')
 ```
+
+# Optimizer Package
+
+[Optimizer Read Me](https://github.com/jbadsdata/watttime-python-client/blob/5780c09e1a7aaae0bc9746cd0004c64c263ead1f/watttime_optimizer/Optimizer%20README.md)
+
+WattTime data users use WattTime electricity grid-related data for real-time, evidence-based emissions reduction strategies.
+
+The [WattTimeOptimizer](https://github.com/jbadsdata/watttime-python-client/tree/b45fd677cb38ec8e9095b1e4a53f5bb43383820b/watttime_optimizer) is an experimental feature  designed to support the rapid development of automated emissions reduction (“AER”) software applications. It produces a proposed power usage schedule that minimizes carbon emissions subject to user and device constraints.
+
+The feature has four basic requirements: Watttime’s forecast of marginal emissions (MOER) for a particular region, device capacity and energy needs, project usage window start time and projected window end time. The [underlying algorithms](https://github.com/jbadsdata/watttime-python-client/tree/b45fd677cb38ec8e9095b1e4a53f5bb43383820b/watttime_optimizer/alg) are simple enough to serve as a base for set of predefined use cases, outlined in the Optimizer Read Me, and mature enough to extend to encompass the requirements of more complex machinery.
+
+Get started by reviewing example notebooks [here](https://github.com/jbadsdata/watttime-python-client/tree/b45fd677cb38ec8e9095b1e4a53f5bb43383820b/watttime_optimizer/notebooks).
+
+

setup.py

@@ -6,7 +6,7 @@
     long_description=open('README.md').read(),
     long_description_content_type="text/markdown",
     version="v1.2.1",
-    packages=["watttime"],
+    packages=["watttime","watttime_optimizer"],
     python_requires=">=3.8",
-    install_requires=["requests", "pandas>1.0.0", "python-dateutil"],
+    install_requires=["requests", "pandas>1.0.0", "python-dateutil","tqdm"],
 )

tests/test_battery.py

@@ -0,0 +1,16 @@
+from watttime_optimizer.battery import Battery, CARS
+import pandas as pd
+
+tesla_charging_curve = pd.DataFrame(
+        columns=["SoC", "kW"],
+        data = CARS['tesla']
+    )
+
+capacity_kWh = 70
+initial_soc = .50
+
+batt = Battery(tesla_charging_curve)
+
+df = batt.get_usage_power_kw_df(capacity_kWh=capacity_kWh, initial_soc=initial_soc)
+
+print(df.head())