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-model power-grid europe energy-system-analysis capacity-expansion-model transmission-network
Last synced: about 16 hours ago
JSON representation
Repository metadata
PyPSA: Python for Power System Analysis
- Host: GitHub
- URL: https://github.com/PyPSA/PyPSA
- Owner: PyPSA
- License: mit
- Created: 2016-01-11T09:04:18.000Z (almost 10 years ago)
- Default Branch: master
- Last Pushed: 2025-10-24T17:47:53.000Z (2 months ago)
- Last Synced: 2025-10-28T20:41:06.647Z (about 2 months ago)
- Topics: 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
- Language: Python
- Homepage: https://docs.pypsa.org
- Size: 54.2 MB
- Stars: 1,698
- Watchers: 69
- Forks: 568
- Open Issues: 115
- Releases: 74
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: docs/CODE_OF_CONDUCT.md
- Citation: CITATION.cff
README.md
PyPSA - Python for Power System Analysis
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.
PyPSA is maintained by the Department of Digital Transformation in Energy
Systems at the Technical University of
Berlin. 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:
- pandas for storing data about components and time series
- numpy and scipy for linear algebra and matrix calculations
- linopy for preparing optimisation problems
- matplotlib, seaborn and plotly for static and interactive plotting
- networkx for network calculations
- pytest for unit testing
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 sectionsin 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:
- T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power
System Analysis, 2018, Journal
of Open Research
Software, 6(1),
arXiv:1707.09913,
DOI:10.5334/jors.188
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:
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
version: 1.0.2 # 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: Schlachtberger
given-names: David
orcid: https://orcid.org/0000-0002-8167-8213
Owner metadata
- Name: PyPSA
- Login: PyPSA
- Email:
- Kind: organization
- Description: Python for Power System Analysis
- Website: www.pypsa.org
- Location:
- Twitter:
- Company:
- Icon url: https://avatars.githubusercontent.com/u/32890768?v=4
- Repositories: 29
- Last ynced at: 2024-03-26T13:25:46.062Z
- Profile URL: https://github.com/PyPSA
GitHub Events
Total
- Create event: 100
- Release event: 10
- Issues event: 96
- Watch event: 353
- Delete event: 285
- Issue comment event: 249
- Push event: 692
- Pull request review comment event: 164
- Pull request review event: 248
- Pull request event: 332
- Fork event: 104
Last Year
- Create event: 100
- Release event: 10
- Issues event: 96
- Watch event: 353
- Delete event: 285
- Issue comment event: 249
- Push event: 692
- Pull request review comment event: 164
- Pull request review event: 248
- Pull request event: 332
- Fork event: 104
Committers metadata
Last synced: about 2 months ago
Total Commits: 2,458
Total Committers: 102
Avg Commits per committer: 24.098
Development Distribution Score (DDS): 0.822
Commits in past year: 280
Committers in past year: 33
Avg Commits per committer in past year: 8.485
Development Distribution Score (DDS) in past year: 0.418
| Name | Commits | |
|---|---|---|
| Fabian | f****f@g****e | 437 |
| Tom Brown | b****n@f****e | 412 |
| Fabian Neumann | f****n@o****e | 382 |
| pre-commit-ci[bot] | 6****] | 236 |
| Lukas Trippe | l****p@p****e | 226 |
| Jonas Hoersch | j****s@c****t | 226 |
| Philipp Glaum | p****m@t****e | 70 |
| Max Parzen | m****n@e****k | 49 |
| lisazeyen | l****n@w****e | 35 |
| martacki | m****i@k****u | 27 |
| Koen van Greevenbroek | k****k@u****o | 25 |
| Irieo | i****n@g****m | 21 |
| energyls | l****m@o****e | 19 |
| Russell Smith | r****h@e****m | 18 |
| euronion | 4****n | 16 |
| JulianGeis | J****s@g****t | 14 |
| Matthew Dumlao | d****4@k****p | 12 |
| Enrico Giglio | 1****o@g****m | 12 |
| Bobby Xiong | 3****g | 10 |
| Nis Martensen | n****n@w****e | 10 |
| Ben Elliston | b****e@a****u | 9 |
| gailin-p | g****e@g****m | 9 |
| dependabot[bot] | 4****] | 8 |
| Jess | 1****n | 7 |
| ekatef | e****a@g****m | 7 |
| Heinz-Alexander Fuetterer | 3****r | 7 |
| Russell Smith | r****h@c****h | 6 |
| ksyranid | k****d@g****m | 6 |
| PeterKlein11 | 8****1 | 6 |
| Francesco Witte | g****b@w****h | 6 |
| and 72 more... | ||
Committer domains:
- fias.uni-frankfurt.de: 2
- posteo.de: 2
- tu-berlin.de: 2
- iee-extern.fraunhofer.de: 1
- witte.sh: 1
- cern.ch: 1
- air.net.au: 1
- kyoto-u.jp: 1
- gmx.net: 1
- edisonenergy.com: 1
- oth-regensburg.de: 1
- uit.no: 1
- kit.edu: 1
- ed.ac.uk: 1
- chaoflow.net: 1
- pm.me: 1
- outlook.de: 1
- gmx.de: 1
- informatik.uni-freiburg.de: 1
- transnetbw.de: 1
- rte-france.com: 1
- energynautics.com: 1
- tuhh.de: 1
- disroot.org: 1
- gridmatic.com: 1
- engie.com: 1
- iai-esm003.iai.kit.edu: 1
- esciencecenter.nl: 1
- pik-potsdam.de: 1
- uliege.be: 1
- eng.au.dk: 1
- climateanalytics.org: 1
- yahoo.de: 1
- gmx.at: 1
- hs-flensburg.de: 1
Issue and Pull Request metadata
Last synced: about 2 months ago
Total issues: 424
Total pull requests: 1,200
Average time to close issues: 6 months
Average time to close pull requests: 17 days
Total issue authors: 166
Total pull request authors: 109
Average comments per issue: 1.66
Average comments per pull request: 1.34
Merged pull request: 959
Bot issues: 0
Bot pull requests: 121
Past year issues: 70
Past year pull requests: 371
Past year average time to close issues: about 1 month
Past year average time to close pull requests: 7 days
Past year issue authors: 38
Past year pull request authors: 36
Past year average comments per issue: 0.69
Past year average comments per pull request: 0.75
Past year merged pull request: 259
Past year bot issues: 0
Past year bot pull requests: 26
Top Issue Authors
- fneum (64)
- FabianHofmann (41)
- nworbmot (14)
- lisazeyen (12)
- Cellophil (12)
- fhg-isi (12)
- lkstrp (10)
- pz-max (9)
- loongmxbt (8)
- PeterKlein11 (7)
- coroa (7)
- euronion (6)
- jankaeh (5)
- davide-f (5)
- matteodefelice (5)
Top Pull Request Authors
- lkstrp (274)
- FabianHofmann (204)
- fneum (189)
- pre-commit-ci[bot] (98)
- p-glaum (39)
- coroa (27)
- lisazeyen (25)
- koen-vg (22)
- Irieo (20)
- pz-max (15)
- martacki (14)
- gincrement (14)
- afuetterer (12)
- dependabot[bot] (12)
- nworbmot (12)
Top Issue Labels
- bug (137)
- enhancement (101)
- needs triage (36)
- help wanted (21)
- wontfix (12)
- needs discussion (8)
- feature (7)
- question (6)
- high-priority (6)
- discussion (5)
- good first issue (5)
- high priority (4)
- beginner-friendly (2)
- documentation (2)
- low-priority (1)
- needs info (1)
- usage question (1)
Top Pull Request Labels
- enhancement (15)
- dependencies (12)
- feature (8)
- new-opt (8)
- github_actions (7)
- bug (4)
- new-docs (4)
- high-priority (1)
- documentation (1)
Package metadata
- Total packages: 4
-
Total downloads:
- pypi: 28,845 last-month
- Total docker downloads: 204
- Total dependent packages: 8 (may contain duplicates)
- Total dependent repositories: 46 (may contain duplicates)
- Total versions: 245
- Total maintainers: 3
pypi.org: pypsa
Python for Power Systems Analysis
- Homepage: https://github.com/PyPSA/PyPSA
- Documentation: https://docs.pypsa.org
- Licenses: MIT License Copyright (c) <year> <copyright holders> Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
- Latest release: 1.0.2 (published 2 months ago)
- Last Synced: 2025-10-30T21:22:41.192Z (about 2 months ago)
- Versions: 77
- Dependent Packages: 8
- Dependent Repositories: 32
- Downloads: 28,845 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: v1.0.2 (published 2 months ago)
- Last Synced: 2025-10-30T21:22:41.264Z (about 2 months ago)
- Versions: 76
- 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: v1.0.2 (published 2 months ago)
- Last Synced: 2025-10-30T21:22:40.853Z (about 2 months ago)
- Versions: 76
- 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 about 3 years ago)
- Last Synced: 2025-11-01T09:02:52.983Z (about 2 months 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
- deprecation *
- matplotlib *
- netcdf4 *
- networkx >=1.10
- numpy *
- pandas >=0.24.0
- pyomo >=5.7
- scipy *
- tables *
- xarray *
- actions/checkout v2 composite
- actions/setup-python v2 composite
- crazy-max/ghaction-chocolatey v1 composite
- actions/checkout v2 composite
- actions/setup-python v2 composite
- pypa/gh-action-pypi-publish master composite
- actions/checkout v3 composite
- codecov/codecov-action v3 composite
- mamba-org/setup-micromamba v1 composite
- 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
- vresutils *
Score: 22.40661676333112