Documentation: energypylinear.adgefficiency.com
A Python library for optimizing energy assets with mixed-integer linear programming:
- electric batteries,
- combined heat & power (CHP) generators,
- electric vehicle smart charging,
- heat pumps,
- renewable (wind & solar) generators.
Assets & sites can be optimized to either maximize profit or minimize carbon emissions, or a user defined custom objective function.
Energy balances are performed on electricity, high, and low temperature heat.
Requires Python 3.11 or 3.12:
$ pip install energypylinear
The asset API allows optimizing a single asset at once:
import energypylinear as epl
# 2.0 MW, 4.0 MWh battery
asset = epl.Battery(
power_mw=2,
capacity_mwh=4,
efficiency_pct=0.9,
electricity_prices=[100.0, 50, 200, -100, 0, 200, 100, -100],
export_electricity_prices=40
)
simulation = asset.optimize()The site API allows optimizing multiple assets together:
import energypylinear as epl
assets = [
# 2.0 MW, 4.0 MWh battery
epl.Battery(
power_mw=2.0,
capacity_mwh=4.0
),
# 30 MW open cycle generator
epl.CHP(
electric_power_max_mw=100,
electric_power_min_mw=30,
electric_efficiency_pct=0.4
),
# 2 EV chargers & 4 charge events
epl.EVs(
chargers_power_mw=[100, 100],
charge_events_capacity_mwh=[50, 100, 30, 40],
charge_events=[
[1, 0, 0, 0, 0],
[0, 1, 1, 1, 0],
[0, 0, 0, 1, 1],
[0, 1, 0, 0, 0],
],
),
# natural gas boiler to generate high temperature heat
epl.Boiler(),
# valve to generate low temperature heat from high temperature heat
epl.Valve()
]
site = epl.Site(
assets=assets,
electricity_prices=[100, 50, 200, -100, 0],
high_temperature_load_mwh=[105, 110, 120, 110, 105],
low_temperature_load_mwh=[105, 110, 120, 110, 105]
)
simulation = site.optimize()See more asset types & use cases in the documentation.
$ make test