PyPSA: Python for Power System Analysis





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################################ Python for Power System Analysis ################################

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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 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 Energy System Modelling group <> at the Institute for Automation and Applied Informatics <> at the Karlsruhe Institute of Technology <>. The group is funded by the Helmholtz Association <> until 2024. Previous versions were developed 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 <>_.


Documentation <>_

Quick start <>_

Examples <>_

Known users of PyPSA <>_

Documentation is in sphinx <>_ reStructuredText format in the doc sub-folder of the repository.


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 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 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
  • basic components out of which more complicated assets can be built, such as Combined Heat and Power (CHP) units, heat pumps, resistive Power-to-Heat (P2H), Power-to-Gas (P2G), battery electric vehicles (BEVs), Fischer-Tropsch, direct air capture (DAC), etc.; each of these is demonstrated in the examples <>_

Other complementary libraries:

  • pandapower <>_ for more detailed modelling of distribution grids, short-circuit calculations, unbalanced load flow and more
  • PowerDynamics.jl <>_ for dynamic modelling of power grids at time scales where differential equations are relevant

Example scripts as Jupyter notebooks

There are extensive examples <> available as Jupyter notebooks <>. They are also described in the doc/examples.rst <doc/examples.rst> and are available as Python scripts in examples/ <examples/>.


  • 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:: doc/img/elec_s_256_lv1.09_Co2L-3H.png :align: center :width: 700px

  • SciGRID model <> simulating the German power system for 2015. Interactive plots also be generated with the plotly <> library, as shown in this Notebook <>_

.. image:: doc/img/stacked-gen_and_storage-scigrid.png :align: center

.. image:: doc/img/lmp_and_line-loading.png :align: right

.. image:: doc/img/reactive-power.png :align: center :width: 600px

  • Small meshed AC-DC toy model

.. image:: doc/img/ac_dc_meshed.png :align: center :width: 400px

All results from a PyPSA simulation can be converted into an interactive online animation using PyPSA-animation <>, for an example see the PyPSA-Eur-30 example <>.

What PyPSA uses under the hood

PyPSA is written and tested to be compatible with Python 3.6 and 3.7. 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
  • pyomo <>_ for preparing optimisation problems (currently only linear)
  • plotly <>_ for interactive plotting
  • matplotlib <>_ for static plotting
  • cartopy <>_ for plotting the baselayer map
  • networkx <>_ for some network calculations
  • py.test <>_ for unit testing
  • logging <>_ for managing messages

The optimisation uses pyomo so that it is independent of the preferred solver. You can use e.g. one of the free solvers GLPK <> and CLP/CBC <> or the commercial solver Gurobi <>_ for which free academic licenses are available.

The time-expensive calculations, such as solving sparse linear equations, are carried out using the scipy.sparse <>_ libraries.

Mailing list

PyPSA has a Google Group forum / mailing list <>_.

Anyone can join and anyone can read the posts; only members of the group can post to the list.

The intention is to have a place where announcements of new releases can be made and questions can be asked.

To discuss issues and suggest/contribute features for future development we prefer ticketing through the PyPSA Github Issues page <>_.

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 = {}, 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:: :target:

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

.. image:: :target:


Copyright 2015-2021 PyPSA Developers <>_

PyPSA is licensed under the open source MIT License <>_.

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.. |badge_license| image:: :target: LICENSE.txt :alt: License

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.. |badge_conda| image:: :target: :alt: Conda version

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