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: 3 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 (almost 6 years ago)
• Default Branch: main
• Last Pushed: 2024-06-09T04:15:31.000Z (5 months ago)
• Last Synced: 2024-06-11T17:45:38.350Z (5 months ago)
• Topics: batteries, electric-vehicles, energy, optimization, python, renewable-energy, solar, wind
• Language: Python
• Homepage:
• Size: 4.47 MB
• Stars: 79
• Watchers: 5
• Forks: 26
• 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

 [![Checked with mypy](https://www.mypy-lang.org/static/mypy_badge.svg)](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 & 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.

## Setup

Requires Python 3.10+:

```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,

  electricity_prices=[100.0, 50, 200, -100, 0, 200, 100, -100],

  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,

  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()

```

## Documentation

[See more asset types & use cases in the documentation](https://energypylinear.adgefficiency.com/latest).

## Test

```shell

$ make test

```