| Name | energypylinear JSON |
| Version |
1.4.0
JSON |
| download |
| home_page | None |
| Summary | Optimizing energy assets with mixed-integer linear programming. |
| upload_time | 2024-06-22 02:31:18 |
| maintainer | None |
| docs_url | None |
| author | Adam Green |
| requires_python | <3.13,>=3.10 |
| license | GNU GPLv3 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
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No Travis.
|
| coveralls test coverage |
No coveralls.
|
# energy-py-linear
<img src="./static/coverage.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
```
Raw data
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"description": "# energy-py-linear\n\n<img src=\"./static/coverage.svg\"> [](https://mypy-lang.org/)\n\n---\n\nDocumentation: [energypylinear.adgefficiency.com](https://energypylinear.adgefficiency.com/latest)\n\n---\n\nA Python library for optimizing energy assets with mixed-integer linear programming:\n\n- electric batteries,\n- combined heat & power (CHP) generators,\n- electric vehicle smart charging,\n- heat pumps,\n- renewable (wind & solar) generators.\n\nAssets & sites can be optimized to either maximize profit or minimize carbon emissions, or a user defined custom objective function.\n\nEnergy balances are performed on electricity, high, and low temperature heat.\n\n## Setup\n\nRequires Python 3.10+:\n\n```shell-session\n$ pip install energypylinear\n```\n\n## Quick Start\n\n### Asset API\n\nThe asset API allows optimizing a single asset at once:\n\n```python\nimport energypylinear as epl\n\n# 2.0 MW, 4.0 MWh battery\nasset = epl.Battery(\n power_mw=2,\n capacity_mwh=4,\n efficiency_pct=0.9,\n electricity_prices=[100.0, 50, 200, -100, 0, 200, 100, -100],\n export_electricity_prices=40\n)\n\nsimulation = asset.optimize()\n```\n\n### Site API\n\nThe site API allows optimizing multiple assets together:\n\n```python\nimport energypylinear as epl\n\nassets = [\n # 2.0 MW, 4.0 MWh battery\n epl.Battery(\n power_mw=2.0,\n capacity_mwh=4.0\n ),\n # 30 MW open cycle generator\n epl.CHP(\n electric_power_max_mw=100,\n electric_power_min_mw=30,\n electric_efficiency_pct=0.4\n ),\n # 2 EV chargers & 4 charge events\n epl.EVs(\n chargers_power_mw=[100, 100],\n charge_events_capacity_mwh=[50, 100, 30, 40],\n charge_events=[\n [1, 0, 0, 0, 0],\n [0, 1, 1, 1, 0],\n [0, 0, 0, 1, 1],\n [0, 1, 0, 0, 0],\n ],\n ),\n # natural gas boiler to generate high temperature heat\n epl.Boiler(),\n # valve to generate low temperature heat from high temperature heat\n epl.Valve()\n]\n\nsite = epl.Site(\n assets=assets,\n electricity_prices=[100, 50, 200, -100, 0],\n high_temperature_load_mwh=[105, 110, 120, 110, 105],\n low_temperature_load_mwh=[105, 110, 120, 110, 105]\n)\n\nsimulation = site.optimize()\n```\n\n## Documentation\n\n[See more asset types & use cases in the documentation](https://energypylinear.adgefficiency.com/latest).\n\n## Test\n\n```shell\n$ make test\n```\n",
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