https://github.com/ADGEfficiency/energy-py-linear
Optimize energy assets using mixed-integer linear programming
https://github.com/ADGEfficiency/energy-py-linear
batteries electric-vehicles energy optimization python renewable-energy solar wind
Last synced: 2 months ago
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Optimize energy assets using mixed-integer linear programming
- Host: GitHub
- URL: https://github.com/ADGEfficiency/energy-py-linear
- Owner: ADGEfficiency
- License: gpl-3.0
- Created: 2019-01-15T21:59:41.000Z (over 6 years ago)
- Default Branch: main
- Last Pushed: 2025-03-30T09:36:42.000Z (3 months ago)
- Last Synced: 2025-04-17T21:21:54.186Z (3 months ago)
- Topics: batteries, electric-vehicles, energy, optimization, python, renewable-energy, solar, wind
- Language: Python
- Homepage:
- Size: 5.51 MB
- Stars: 93
- Watchers: 5
- Forks: 28
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
Awesome Lists containing this project
- open-sustainable-technology - energy-py-linear - Optimizing energy systems using mixed integer linear programming. (Energy Systems / Energy System Modeling Frameworks)
README
# energy-py-linear
[](https://mypy-lang.org/)
---
Documentation: [energypylinear.adgefficiency.com](https://energypylinear.adgefficiency.com/latest)
---
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 can be optimized to either maximize profit or minimize carbon emissions, or for user defined custom objective functions. Custom constraints can be used to further constrain asset behaviour.
A site is a collection of assets that can be optimized together. Sites can use custom objectives and constraints.
Energy balances are performed on electricity, high, and low temperature heat.
## Setup
Requires Python 3.11 or 3.12:
```shell-session
$ pip install energypylinear
```
## Quick Start
### Asset API
The asset API allows optimizing a single asset at once:
```python
import energypylinear as epl
# 2.0 MW, 4.0 MWh battery
asset = epl.Battery(
power_mw=2,
capacity_mwh=4,
efficiency_pct=0.9,
# different electricity prices for each interval
# length of electricity_prices is the length of the simulation
electricity_prices=[100.0, 50, 200, -100, 0, 200, 100, -100],
# a constant value for each interval
export_electricity_prices=40,
)
simulation = asset.optimize()
```
### Site API
The site API allows optimizing multiple assets together:
```python
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,
# length of energy prices is the length of the simulation
electricity_prices=[100, 50, 200, -100, 0],
# these should match the length of the export_electricity_prices
# if they don't, they will be repeated or cut to match the length of electricity_prices
high_temperature_load_mwh=[105, 110, 120, 110, 105],
low_temperature_load_mwh=[105, 110, 120, 110, 105],
)
simulation = site.optimize()
```
## Documentation
[See more asset types & use cases in the documentation](https://energypylinear.adgefficiency.com/latest).
## Test
```shell
$ make test
```