Optimizing energy assets with mixed-integer linear programming.
Project description
energy-py-linear
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.
Assets can be optimized to either maximize profit or minimize carbon emissions.
Energy balances are performed on electricity, high & low temperature heat.
Setup
Requires Python 3.10+:
$ pip install energypylinear
Quick Start
Asset API
The asset API allows optimizing a single asset at once.
We can optimize an electric battery operating in wholesale price arbitrage using epl.Battery
:
import energypylinear as epl
# 2.0 MW, 4.0 MWh battery
asset = epl.battery.Battery(power_mw=2, capacity_mwh=4, efficiency=0.9)
results = asset.optimize(
electricity_prices=[100.0, 50, 200, -100, 0, 200, 100, -100]
)
See how to optimize other asset types in how-to/optimize-assets.
Site API
The site API allows optimizing multiple assets at once:
import energypylinear as epl
site = epl.Site(assets=[
# 2.0 MW, 4.0 MWh battery
epl.Battery(power_mw=2.0, capacity_mwh=4.0),
# 30 MW generator
epl.Generator(
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]
]
)
])
results = site.optimize(
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]
)
The site API will optimize the assets together, and return the results for each asset.
Examples
Examples as independent scripts are ./examples
:
$ ls ./examples
./examples
├── battery.py
├── chp.py
├── evs.py
└── forecast-accuracy.py
Test
$ make test
Documentation
Hosted at energypylinear.adgefficiency.com/latest.
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