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PyPSA

A free software toolbox for simulating and optimizing modern power systems that include features such as conventional generators with unit commitment, variable wind and solar generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks.
https://github.com/PyPSA/PyPSA

Category: Energy Systems
Sub Category: Energy System Modeling Frameworks

Keywords

capacity-expansion-planning clean-energy climate-change electrical-engineering electricity energy energy-system energy-system-model energy-system-modelling energy-systems loadflow optimal-power-flow optimisation power-flow power-systems power-systems-analysis powerflow python renewable-energy renewables

Keywords from Contributors

pypsa linopy pyomo energy-data energy-modelling power-grids transmission-network sector-coupling energy-system-analysis europe

Last synced: about 7 hours ago
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Repository metadata

PyPSA: Python for Power System Analysis

README.md

PyPSA - Python for Power System Analysis

PyPI version
Conda version
Python Version from PEP 621 TOML
Tests
Documentation Status
pre-commit.ci status
Code coverage
REUSE status
Ruff
License
Zenodo
Discord
Contributor Covenant

PyPSA stands for Python for Power System Analysis. It is pronounced
pipes-ah.

PyPSA is an open-source Python framework for optimising and simulating modern
power and energy systems that include features such as conventional generators
with unit commitment, variable wind and solar generation, hydro-electricity,
inter-temporal storage, coupling to other energy sectors, elastic demands, and
linearised power flow with loss approximations in DC and AC networks. PyPSA is
designed to scale well with large networks and long time series. It is made for
researchers, planners and utilities with basic coding aptitude who need a fast,
easy-to-use and transparent tool for power and energy system analysis.

[!NOTE]
PyPSA has many contributors, with the maintenance led by the Department of Digital Transformation in
Energy Systems at the Technical University of
Berlin. The project is currently supported by the
German Research Foundation
(grant number 528775426).
Previous versions were developed at the Karlsruhe
Institute of Technology funded by the
Helmholtz Association, and
at FIAS funded by the German Federal
Ministry for Education and Research (BMBF).

Features

  • Economic Dispatch (ED): Models short-term market-based dispatch including
    unit commitment, renewable availability, short-duration and seasonal storage
    including hydro reservoirs with inflow and spillage dynamics, elastic demands,
    load shedding and conversion between energy carriers, using either perfect
    operational foresight or rolling horizon time resolution.

  • Linear Optimal Power Flow (LOPF): Extends economic dispatch to determine
    the least-cost dispatch while respecting network constraints in meshed AC-DC
    networks, using a linearised representation of power flow (KVL, KCL) with
    optional loss approximations.

  • Security-Constrained LOPF (SCLOPF): Extends LOPF by accounting for line
    outage contingencies to ensure system reliability under $N-1$ conditions.

  • Capacity Expansion Planning (CEP): Supports least-cost
    long-term system planning with investment decisions for generation, storage,
    conversion, and transmission infrastructure. Handles both single and multiple
    investment periods. Continuous and discrete investments are supported.

  • Pathway Planning: Supports co-optimisation of multiple investment periods to
    plan energy system transitions over time with perfect planning foresight.

  • Stochastic Optimisation: Implements two-stage stochastic programming
    framework with scenario-weighted uncertain inputs, with investments as
    first-stage decisions and dispatch as recourse decisions.

  • Modelling-to-Generate-Alternatives (MGA): Explores near-optimal decision
    spaces to provide insight into the range of feasible system configurations with
    similar costs.

  • Sector-Coupling: Modelling integrated energy systems with multiple energy
    carriers (electricity, heat, hydrogen, etc.) and conversion between them.
    Flexible representation of technologies such as heat pumps, electrolysers,
    battery electric vehicles (BEVs), direct air capture (DAC), and synthetic
    fuels production.

  • Static Power Flow Analysis: Computes both full non-linear and linearised
    load flows for meshed AC and DC grids using Newton-Raphson method.

Documentation

PyPSA has extensive documentation with tutorials, user guides, examples and an API reference.

Installation

pip:

pip install pypsa

conda/mamba:

conda install -c conda-forge pypsa

uv:

uv add pypsa

Usage

import pypsa

# create a new network
n = pypsa.Network()
n.add("Bus", "mybus")
n.add("Load", "myload", bus="mybus", p_set=100)
n.add("Generator", "mygen", bus="mybus", p_nom=100, marginal_cost=20)

# load an example network
n = pypsa.examples.ac_dc_meshed()

# run the optimisation
n.optimize()

# plot results
n.generators_t.p.plot()
n.plot()

# get statistics
n.statistics()
n.statistics.energy_balance()

Dependencies

PyPSA relies heavily on other open-source Python packages. Some of them are:

Find the full list of dependencies in the pyproject.toml file.

PyPSA can be used with different solvers. For instance, the free solvers
HiGHS (installed by default), GLPK and
CBC or commercial solvers like
Gurobi or FICO Xpress for which free academic licenses are available.

Contributing and Support

We strongly welcome anyone interested in contributing to this project. If you have any ideas, suggestions or encounter problems, feel invited to file issues or make pull requests on GitHub.

  • To discuss with other PyPSA users, organise projects, share news, and get in touch with the community you can use the Discord server.
  • For bugs and feature requests, please open an issue.
  • For troubleshooting and support, please check the troubleshooting and support sections in the documentation.

Detailed guidelines can be found in the Contributing guidelines of our documentation.

Code of Conduct

Please respect our Code of Conduct.

Citing PyPSA

If you use PyPSA for your research, we would appreciate it if you would
cite the following paper:

Please use the following BibTeX:

@article{PyPSA,
   author = {T. Brown and J. H\"orsch and D. Schlachtberger},
   title = {{PyPSA: Python for Power System Analysis}},
   journal = {Journal of Open Research Software},
   volume = {6},
   issue = {1},
   number = {4},
   year = {2018},
   eprint = {1707.09913},
   url = {https://doi.org/10.5334/jors.188},
   doi = {10.5334/jors.188}
}

If you want to cite a specific PyPSA version, each release of PyPSA is archived
on Zenodo with a release-specific DOI:
DOI

Licence

Copyright PyPSA Contributors

PyPSA is licensed under the open source MIT License.
The documentation is licensed under CC-BY-4.0.

The repository uses REUSE to expose the licenses of its files.

Citation (CITATION.cff)

# SPDX-FileCopyrightText: PyPSA Contributors
#
# SPDX-License-Identifier: MIT

cff-version: 1.2.0
message: "If you use this package, please cite the corresponding manuscript in Journal of Open Research Software."
title: "PyPSA: Python for Power System Analysis"
repository: https://github.com/pypsa/pypsa
license: MIT
journal: Journal of Open Research Software
doi: 10.5334/jors.188
authors:
  - family-names: Brown
    given-names: Tom
    orcid: https://orcid.org/0000-0001-5898-1911
  - family-names: Hörsch
    given-names: Jonas
    orcid: https://orcid.org/0000-0001-9438-767X
  - family-names: Schlachtberger
    given-names: David
    orcid: https://orcid.org/0000-0002-8167-8213

Owner metadata

GitHub Events

Total
Last Year

Committers metadata

Last synced: 1 day ago

Total Commits: 2,705
Total Committers: 121
Avg Commits per committer: 22.355
Development Distribution Score (DDS): 0.821

Commits in past year: 353
Committers in past year: 45
Avg Commits per committer in past year: 7.844
Development Distribution Score (DDS) in past year: 0.589

Name Email Commits
Fabian f****f@g****e 483
Tom Brown b****n@f****e 412
Fabian Neumann f****n@o****e 388
Lukas Trippe l****p@p****e 312
pre-commit-ci[bot] 6****] 239
Jonas Hoersch j****s@c****t 231
Philipp Glaum p****m@t****e 70
Max Parzen m****n@e****k 51
lisazeyen l****n@w****e 35
dependabot[bot] 4****] 34
Iegor Riepin i****n@g****m 33
martacki m****i@k****u 28
Koen van Greevenbroek k****k@u****o 27
energyls l****m@o****e 20
Markus Groissböck m****k@g****t 20
euronion 4****n 18
Russell Smith r****h@e****m 18
JulianGeis J****s@g****t 14
Enrico Giglio 1****o@g****m 13
Matthew Dumlao d****4@k****p 12
Nis Martensen n****n@w****e 10
Bobby Xiong 3****g 10
gailin-p g****e@g****m 9
Ben Elliston b****e@a****u 9
ekatef e****a@g****m 7
Jess 1****n 7
Heinz-Alexander Fuetterer 3****r 7
Matěj m****y 6
PeterKlein11 8****1 6
ksyranid k****d@g****m 6
and 91 more...

Committer domains:

Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 534
Total pull requests: 1,400
Average time to close issues: 7 months
Average time to close pull requests: 19 days
Total issue authors: 191
Total pull request authors: 135
Average comments per issue: 1.82
Average comments per pull request: 1.33
Merged pull request: 1,084
Bot issues: 0
Bot pull requests: 162

Past year issues: 121
Past year pull requests: 258
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 8 days
Past year issue authors: 45
Past year pull request authors: 51
Past year average comments per issue: 1.66
Past year average comments per pull request: 1.2
Past year merged pull request: 152
Past year bot issues: 0
Past year bot pull requests: 46

More stats: https://issues.ecosyste.ms/repositories/lookup?url=https://github.com/PyPSA/PyPSA

Top Issue Authors

  • fneum (71)
  • FabianHofmann (63)
  • lkstrp (18)
  • euronion (16)
  • nworbmot (14)
  • Cellophil (13)
  • brynpickering (12)
  • lisazeyen (12)
  • fhg-isi (12)
  • pz-max (9)
  • loongmxbt (8)
  • PeterKlein11 (7)
  • coroa (7)
  • MaykThewessen (7)
  • davide-f (6)

Top Pull Request Authors

  • lkstrp (317)
  • FabianHofmann (246)
  • fneum (192)
  • pre-commit-ci[bot] (100)
  • dependabot[bot] (51)
  • p-glaum (39)
  • coroa (30)
  • lisazeyen (25)
  • Irieo (23)
  • koen-vg (23)
  • gincrement (22)
  • pz-max (16)
  • martacki (15)
  • nworbmot (12)
  • afuetterer (12)

Top Issue Labels

  • bug (136)
  • enhancement (96)
  • needs triage (54)
  • gap (29)
  • optimization (29)
  • help wanted (20)
  • needs discussion (17)
  • wontfix (12)
  • IO (11)
  • high priority (9)
  • operational realism (8)
  • data validation (8)
  • question (6)
  • high-priority (6)
  • components API (6)
  • feature (6)
  • good first issue (5)
  • discussion (5)
  • analysis (5)
  • investments (4)
  • AC power flow (4)
  • docs (3)
  • workflow (3)
  • model-building (3)
  • breaking (3)
  • documentation (2)
  • market modelling (2)
  • reliability & uncertainty (2)
  • needs info (2)
  • beginner-friendly (2)

Top Pull Request Labels

  • dependencies (51)
  • python:uv (33)
  • enhancement (15)
  • github_actions (13)
  • new-opt (8)
  • feature (8)
  • new-docs (4)
  • bug (4)
  • agent (4)
  • stacked (2)
  • gap (2)
  • optimization (1)
  • market modelling (1)
  • documentation (1)
  • high-priority (1)
  • needs revision (1)

Package metadata

pypi.org: pypsa

Python for Power Systems Analysis

  • Homepage: https://github.com/PyPSA/PyPSA
  • Documentation: https://docs.pypsa.org
  • Licenses: MIT License
  • Latest release: 1.2.4 (published about 2 months ago)
  • Last Synced: 2026-08-14T08:32:04.780Z (1 day ago)
  • Versions: 90
  • Dependent Packages: 8
  • Dependent Repositories: 32
  • Downloads: 87,936 Last month
  • Docker Downloads: 204
  • Rankings:
    • Dependent packages count: 1.256%
    • Stargazers count: 2.075%
    • Dependent repos count: 2.6%
    • Forks count: 2.716%
    • Average: 2.756%
    • Docker downloads count: 3.144%
    • Downloads: 4.747%
  • Maintainers (3)
proxy.golang.org: github.com/pypsa/pypsa

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/pypsa/pypsa#section-documentation
  • Licenses: mit
  • Latest release: v1.2.4 (published about 2 months ago)
  • Last Synced: 2026-08-14T08:01:29.276Z (1 day ago)
  • Versions: 89
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent repos count: 1.622%
    • Average: 4.057%
    • Dependent packages count: 6.492%
proxy.golang.org: github.com/PyPSA/PyPSA

  • Homepage:
  • Documentation: https://pkg.go.dev/github.com/PyPSA/PyPSA#section-documentation
  • Licenses: mit
  • Latest release: v1.2.4 (published about 2 months ago)
  • Last Synced: 2026-08-14T09:18:26.520Z (1 day ago)
  • Versions: 89
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Rankings:
    • Dependent packages count: 6.999%
    • Average: 8.173%
    • Dependent repos count: 9.346%
conda-forge.org: pypsa

PyPSA is a free software toolbox for simulating and optimising modern power systems that include features such as conventional generators with unit commitment, variable wind and solar generation, storage units, coupling to other energy sectors, and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series.

  • Homepage: https://pypsa.org/
  • Licenses: MIT
  • Latest release: 0.21.1 (published almost 4 years ago)
  • Last Synced: 2026-04-01T13:29:02.478Z (5 months ago)
  • Versions: 16
  • Dependent Packages: 0
  • Dependent Repositories: 14
  • Downloads: 214,557 Total
  • Rankings:
    • Forks count: 9.274%
    • Dependent repos count: 9.349%
    • Stargazers count: 14.05%
    • Average: 21.053%
    • Dependent packages count: 51.54%

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