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,
• 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:

$ pip install energypylinear

Quick Start

Asset API

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,
    # 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:

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.

Test

$ make test