Free as in Freedom: Create your repos and join us!
Join Donate
No Description
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Jonas Hoersch c3a6c83c3b networkclustering: Fix typo introduced in previous commit 4 days ago
doc PyPSA Version 0.13.2 1 month ago
examples Add carbon management / biomass example 2 months ago
pypsa networkclustering: Fix typo introduced in previous commit 4 days ago
test Skip tests against pypower 8 months ago
website PyPSA Version 0.13.2 1 month ago
.gitignore website: Add website files 1 year ago
.travis.yml travis: Switch off coveralls on travis until coverage testing works properly 1 year ago
LICENSE.txt Include full GPL v3 licence 2 years ago Prepare for travis-ci 1 year ago
Makefile Prepare for travis-ci 1 year ago
README.rst PyPSA Version 0.13.2 1 month ago
requirements.yml Prepare for travis-ci 1 year ago
requirements_dev.yml requirements_dev.yml: Remove currently broken anaconda channel 3 months ago
setup.cfg Include a more friendly package format with etc. 3 years ago PyPSA Version 0.13.2 1 month ago


|badge_travis| |badge_pypi| |badge_license|


Python for Power System Analysis

.. contents::

.. section-numbering::


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

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.

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 as a website <>`_

`Documentation as a PDF <>`_

`Quick start <>`_

`Examples <>`_

Documentation is in `sphinx
<>`_ reStructuredText format in
`doc/ <doc/>`_.

What PyPSA does and does not do (yet)

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

Functionality that may be added in the future:

* Multi-year investment optimisation
* Distributed active power slack
* Interactive web-based GUI with SVG
* OPF with the full non-linear network equations
* Port to `Julia <>`_

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/>`_.


Results from a PyPSA simulation can be converted into an interactive
online animation using `PyPSA-animation
<>`_, see the `PyPSA-Eur-30
example <>`_.

Another showcase for PyPSA is the `SciGRID example
<>`_ which
demonstrates interactive plots generated with the `plotly
<>`_ library.

.. image::

.. image::

.. image::

.. image::

.. image::

.. image::

.. image::

Optimised capacities of generation and storage for a 95% reduction in CO2 emissions in Europe compare to 1990 levels:

.. image::
.. image::

What PyPSA uses under the hood

PyPSA is written and tested to be compatible with both Python 2.7 and
Python 3.6.

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
* `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. the free software GLPK or the commercial
software Gurobi).

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

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: ::

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.
This can be found linked from the overall PyPSA Zenodo DOI:

.. image::


Copyright 2015-2019 Tom Brown (KIT, FIAS), Jonas Hörsch (KIT, FIAS),
David Schlachtberger (FIAS)

This program is free software: you can redistribute it and/or
modify it under the terms of the GNU General Public License as
published by the Free Software Foundation; either `version 3 of the
License <LICENSE.txt>`_, or (at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
`GNU General Public License <LICENSE.txt>`_ for more details.

.. |link-latest-doi| image::
.. _link-latest-doi:

.. |badge_pypi| image::
:alt: PyPI version

.. |badge_license| image::
:target: #license

.. |badge_travis| image::
:alt: Build status on Linux