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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-systems linopy loadflow optimal-power-flow optimisation power-flow power-systems power-systems-analysis powerflow pypsa python renewable-energy renewables

Keywords from Contributors

pyomo energy-system-model energy-model heat-pump energy-transition snakemake-workflow sector-coupling great-britain demand-flexibility heating

Last synced: about 11 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
Ruff
License
Zenodo
Discord
Contributor Covenant

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

PyPSA is an open source toolbox for simulating and optimising modern power and
energy 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.

This project is maintained by the Department of Digital Transformation in
Energy Systems
at the Technical University of
Berlin
. Previous versions were developed by the Energy
System Modelling group at the Institute for Automation and Applied
Informatics
at the Karlsruhe
Institute of Technology
funded by the
Helmholtz Association, and by the Renewable
Energy
Group

at FIAS to carry out simulations for the
CoNDyNet project, financed by the German Federal
Ministry for Education and Research (BMBF)

as part of the Stromnetze Research
Initiative
.

Functionality

PyPSA can calculate:

  • static power flow (using both the full non-linear network equations and the
    linearised network equations)
  • linear optimal power flow (least-cost optimisation of power plant and
    storage dispatch within network constraints, using the linear network
    equations, over several snapshots)
  • security-constrained linear optimal power flow
  • total electricity/energy system least-cost investment optimisation (using
    linear network equations, over several snapshots and investment periods
    simultaneously for optimisation of generation and storage dispatch and
    investment in the capacities of generation, storage, transmission and other
    infrastructure)

It has models for:

  • meshed multiply-connected AC and DC networks, with controllable converters
    between AC and DC networks
  • standard types for lines and transformers following the implementation in
    pandapower
  • conventional dispatchable generators and links with unit commitment
  • generators with time-varying power availability, such as wind and solar
    generators
  • storage units with efficiency losses
  • simple hydroelectricity with inflow and spillage
  • coupling with other energy carriers (e.g. resistive Power-to-Heat (P2H),
    Power-to-Gas (P2G), battery electric vehicles (BEVs), Fischer-Tropsch,
    direct air capture (DAC))
  • basic components out of which more complicated assets can be built, such as
    Combined Heat and Power (CHP) units and heat pumps.

Documentation

Installation

pip:

pip install pypsa

conda/mamba:

conda install -c conda-forge pypsa

Additionally, install a solver (see here).

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()

There are more extensive
examples
available
as Jupyter notebooks. They are also available as Python scripts in
examples/notebooks/ directory.

Screenshots

PyPSA-Eur optimising capacities of
generation, storage and transmission lines (9% line volume expansion allowed)
for a 95% reduction in CO2 emissions in Europe compared to 1990 levels

image

SciGRID model simulating the German power system
for 2015.

image

image

Dependencies

PyPSA is written and tested to be compatible with Python 3.10 and above.
The last release supporting Python 2.7 was PyPSA 0.15.0.

It leans heavily on the following Python packages:

  • pandas for storing data about
    components and time series
  • numpy and scipy for
    calculations, such as linear algebra and sparse matrix calculations
  • networkx for some network
    calculations
  • matplotlib for static plotting
  • linopy for preparing optimisation problems
    (currently only linear and mixed integer linear optimisation)
  • cartopy for plotting the
    baselayer map
  • pytest for unit testing
  • logging for
    managing messages

Find the full list of dependencies in the
dependency graph.

The optimisation uses interface libraries like linopy which are independent of
the preferred solver. You can use e.g. one of the free solvers
HiGHS, GLPK and
CLP/CBC or the commercial solver
Gurobi 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 use the PyPSA Github Issues page.
  • For troubleshooting, please check the troubleshooting in the documentation.

Detailed guidelines can be found in the Contributing section 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
stored on Zenodo with a release-specific DOI. The
release-specific DOIs can be found linked from the overall PyPSA Zenodo
DOI for Version 0.17.1 and onwards:

image

or from the overall PyPSA Zenodo DOI for Versions up to 0.17.0:

image

Licence

Copyright 2015-2025 PyPSA
Developers

PyPSA is licensed under the open source MIT
License
.

Citation (CITATION.cff)

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
version: 0.34.0 # Don't touch, will be updated by the release script
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: Hofmann
    given-names: Fabian
    orcid: https://orcid.org/0000-0002-6604-5450
  - family-names: Neumann
    given-names: Fabian
    orcid: https://orcid.org/0000-0001-8551-1480
  - family-names: Zeyen
    given-names: Lisa
    orcid: https://orcid.org/0000-0002-7262-3296
  - family-names: Syranidis
    given-names: Chloe
  - family-names: Frysztacki
    given-names: Martha
    orcid: https://orcid.org/0000-0002-0788-1328
  - family-names: Schlachtberger
    given-names: David
    orcid: https://orcid.org/0000-0002-8167-8213
  - family-names: Glaum
    given-names: Philipp
  - family-names: Parzen
    given-names: Max

Owner metadata


GitHub Events

Total
Last Year

Committers metadata

Last synced: 6 days ago

Total Commits: 2,290
Total Committers: 95
Avg Commits per committer: 24.105
Development Distribution Score (DDS): 0.81

Commits in past year: 313
Committers in past year: 35
Avg Commits per committer in past year: 8.943
Development Distribution Score (DDS) in past year: 0.604

Name Email Commits
Fabian f****f@g****e 434
Tom Brown b****n@f****e 412
Fabian Neumann f****n@o****e 362
pre-commit-ci[bot] 6****] 233
Jonas Hoersch j****s@c****t 225
Lukas Trippe l****p@p****e 124
Philipp Glaum p****m@t****e 69
Max Parzen m****n@e****k 49
lisazeyen l****n@w****e 34
martacki m****i@k****u 27
Koen van Greevenbroek k****k@u****o 21
energyls l****m@o****e 19
Russell Smith r****h@e****m 18
euronion 4****n 14
JulianGeis J****s@g****t 13
Matthew Dumlao d****4@k****p 12
Irieo i****n@g****m 12
Enrico Giglio 1****o@g****m 12
Nis Martensen n****n@w****e 10
Ben Elliston b****e@a****u 9
gailin-p g****e@g****m 9
Jess 1****n 7
Heinz-Alexander Fuetterer 3****r 7
ekatef e****a@g****m 7
Russell Smith r****h@c****h 6
Alex-Neve 1****e 6
David Schlachtberger s****r@f****e 6
Francesco Witte g****b@w****h 6
PeterKlein11 8****1 6
ksyranid k****d@g****m 6
and 65 more...

Committer domains:


Issue and Pull Request metadata

Last synced: 1 day ago

Total issues: 410
Total pull requests: 847
Average time to close issues: 5 months
Average time to close pull requests: 17 days
Total issue authors: 158
Total pull request authors: 102
Average comments per issue: 1.83
Average comments per pull request: 1.56
Merged pull request: 733
Bot issues: 0
Bot pull requests: 81

Past year issues: 86
Past year pull requests: 303
Past year average time to close issues: 28 days
Past year average time to close pull requests: 6 days
Past year issue authors: 41
Past year pull request authors: 38
Past year average comments per issue: 1.24
Past year average comments per pull request: 0.86
Past year merged pull request: 259
Past year bot issues: 0
Past year bot pull requests: 32

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

Top Issue Authors

  • fneum (51)
  • FabianHofmann (40)
  • lisazeyen (14)
  • nworbmot (14)
  • Cellophil (13)
  • lkstrp (12)
  • fhg-isi (12)
  • pz-max (9)
  • loongmxbt (8)
  • coroa (7)
  • PeterKlein11 (7)
  • matteodefelice (5)
  • davide-f (5)
  • p-glaum (5)
  • jankaeh (5)

Top Pull Request Authors

  • FabianHofmann (172)
  • fneum (153)
  • lkstrp (127)
  • pre-commit-ci[bot] (69)
  • p-glaum (35)
  • lisazeyen (25)
  • coroa (19)
  • martacki (14)
  • pz-max (13)
  • koen-vg (12)
  • nworbmot (11)
  • Irieo (10)
  • afuetterer (9)
  • bje- (8)
  • gincrement (7)

Top Issue Labels

  • bug (138)
  • enhancement (96)
  • help wanted (21)
  • wontfix (12)
  • good first issue (9)
  • discussion (8)
  • needs triage (8)
  • feature (7)
  • high-priority (6)
  • question (6)
  • needs discussion (6)
  • high priority (3)
  • needs info (2)
  • low-priority (2)
  • beginner-friendly (2)
  • usage question (1)
  • documentation (1)

Top Pull Request Labels

  • enhancement (14)
  • feature (5)
  • bug (4)
  • new-opt (4)
  • dependencies (3)
  • high-priority (1)
  • documentation (1)
  • new-docs (1)

Package metadata

pypi.org: pypsa

Python for Power Systems Analysis

  • Homepage: https://github.com/PyPSA/PyPSA
  • Documentation: https://pypsa.readthedocs.io/
  • Licenses: MIT License
  • Latest release: 0.34.1 (published 20 days ago)
  • Last Synced: 2025-04-25T14:36:15.074Z (1 day ago)
  • Versions: 70
  • Dependent Packages: 8
  • Dependent Repositories: 32
  • Downloads: 16,523 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: v0.34.1 (published 20 days ago)
  • Last Synced: 2025-04-25T14:36:15.214Z (1 day ago)
  • Versions: 70
  • 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: v0.34.1 (published 20 days ago)
  • Last Synced: 2025-04-25T14:36:15.457Z (1 day ago)
  • Versions: 70
  • 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 over 2 years ago)
  • Last Synced: 2025-04-01T02:09:47.028Z (26 days ago)
  • Versions: 16
  • Dependent Packages: 0
  • Dependent Repositories: 14
  • Rankings:
    • Forks count: 9.274%
    • Dependent repos count: 9.349%
    • Stargazers count: 14.05%
    • Average: 21.053%
    • Dependent packages count: 51.54%

Dependencies

setup.py pypi
  • deprecation *
  • matplotlib *
  • netcdf4 *
  • networkx >=1.10
  • numpy *
  • pandas >=0.24.0
  • pyomo >=5.7
  • scipy *
  • tables *
  • xarray *
.github/workflows/CI.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • crazy-max/ghaction-chocolatey v1 composite
.github/workflows/deploy.yml actions
  • actions/checkout v2 composite
  • actions/setup-python v2 composite
  • pypa/gh-action-pypi-publish master composite
.github/workflows/CI-micromamba.yml actions
  • actions/checkout v3 composite
  • codecov/codecov-action v3 composite
  • mamba-org/setup-micromamba v1 composite
environment.yaml conda
  • cartopy >=0.16
  • deprecation
  • glpk
  • libnetcdf
  • linopy >=0.2
  • matplotlib
  • netcdf4
  • networkx >=1.10
  • numexpr <=2.8.4
  • numpy
  • pandas >=0.24.0,<2.1
  • pyomo >=5.7.0,<6.6.2
  • pytables
  • python >=3.9
  • scipy
  • validators
  • xarray
binder/environment.yml pypi
  • vresutils *
pyproject.toml pypi

Score: 21.6601745499