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).

• 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 opti 8000 onal 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.

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

pip:

pip install pypsa

conda/mamba:

conda install -c conda-forge pypsa

uv:

uv add pypsa

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()

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.

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.

Please respect our Code of Conduct.

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:

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.