A library for optimizing energy systems using mixed integer linear programming.
Currently the library has two models:
- electric battery operating in price arbitrage,
- a combined heat & power plant.
The battery model is optimized against a set of prices, and returns a list of dictionaries - one per interval:
import energypylinear as epl
model = epl.Battery(power=2, capacity=4, efficiency=1.0)
prices = [10, 50, 10, 50, 10]
results = model.optimize(prices, freq="60T")pandas can be used to transform the results into a dataframe:
import pandas as pd
pd.DataFrame().from_dict(results)
Import [MW] Export [MW] Power [MW] Charge [MWh]
0 2.0 0.0 2.0 0.000000
1 0.0 2.0 -2.0 0.066667
2 2.0 0.0 2.0 0.000000
3 0.0 2.0 -2.0 0.066667
4 NaN NaN NaN 0.000000The last row is all NaN except for Charge - Charge indicates the battery position at the start of each interval. The last row is included so we can see the battery level at the end of the optimization run.
It is also possible to send in forecast prices along with actual prices, and to change the initial charge.
If the model receives forecasts it will optimize for them - this allows measurement of forecast quality by comparing actual with forecast costs:
# a forecast that is the inverse of the prices we used above
forecasts = [50, 10, 50, 10, 50]
results = model.optimize(prices, forecasts=forecasts, timestep='60T', objective='forecasts')The battery model also accounts for carbon. If no carbon profile is passed in, a constant value of 0.5 tC/MWh is assumed.
We can switch the optimization to focus on carbon:
results = model.optimize(prices, forecasts=forecasts, carbon=carbon, timestep="60T", objective='carbon')Install as an editable package:
$ make setupThe main dependency of this project is PuLP. For further reading on PuLP:
- An Introduction to pulp for Python Programmers,
- the blog post series Introduction to Linear Programming with Python and PuLP - especially Part 6 which covers how to formulate more complex conditional constraints.
$ make test