# PyPSA - Python for Power System Analysis [![PyPI version](https://img.shields.io/pypi/v/pypsa.svg)](https://pypi.python.org/pypi/pypsa) [![Conda version](https://img.shields.io/conda/vn/conda-forge/pypsa.svg)](https://anaconda.org/conda-forge/pypsa) [![CI](https://github.com/pypsa/pypsa/actions/workflows/CI.yml/badge.svg)](https://github.com/pypsa/pypsa/actions/workflows/CI.yml) [![CI with micromamba](https://github.com/pypsa/pypsa/actions/workflows/CI-micromamba.yml/badge.svg)](https://github.com/pypsa/pypsa/actions/workflows/CI-micromamba.yml) [![Code coverage](https://codecov.io/gh/PyPSA/PyPSA/branch/master/graph/badge.svg?token=kCpwJiV6Jr)](https://codecov.io/gh/PyPSA/PyPSA) [![Documentation Status](https://readthedocs.org/projects/pypsa/badge/?version=latest)](https://pypsa.readthedocs.io/en/latest/?badge=latest) [![License](https://img.shields.io/pypi/l/pypsa.svg)](LICENSE.txt) [![Zenodo](https://zenodo.org/badge/DOI/10.5281/zenodo.3946412.svg)](https://doi.org/10.5281/zenodo.3946412) [![Examples of use](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/PyPSA/PyPSA/master?filepath=examples%2Fnotebooks) [![pre-commit.ci status](https://results.pre-commit.ci/badge/github/PyPSA/PyPSA/master.svg)](https://results.pre-commit.ci/latest/github/PyPSA/PyPSA/master) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![Discord](https://img.shields.io/discord/911692131440148490?logo=discord)](https://discord.gg/AnuJBk23FU) [![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.1-4baaaa.svg)](CODE_OF_CONDUCT.md) [![Stack Exchange questions](https://img.shields.io/stackexchange/stackoverflow/t/pypsa)](https://stackoverflow.com/questions/tagged/pypsa) 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](https://tub-ensys.github.io) at the [Technical University of Berlin](https://www.tu.berlin). Previous versions were developed by the Energy System Modelling group at the [Institute for Automation and Applied Informatics](https://www.iai.kit.edu/english/index.php) at the [Karlsruhe Institute of Technology](http://www.kit.edu/english/index.php) funded by the [Helmholtz Association](https://www.helmholtz.de/en/), and by the [Renewable Energy Group](https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/) at [FIAS](https://fias.uni-frankfurt.de/) to carry out simulations for the [CoNDyNet project](http://condynet.de/), financed by the [German Federal Ministry for Education and Research (BMBF)](https://www.bmbf.de/en/index.html) as part of the [Stromnetze Research Initiative](http://forschung-stromnetze.info/projekte/grundlagen-und-konzepte-fuer-effiziente-dezentrale-stromnetze/). ## 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](https://www.pandapower.org/) - 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 [Documentation](https://pypsa.readthedocs.io/en/latest/index.html) [Quick start](https://pypsa.readthedocs.io/en/latest/quick_start.html) [Examples](https://pypsa.readthedocs.io/en/latest/examples-basic.html) [Known users of PyPSA](https://pypsa.readthedocs.io/en/latest/users.html) ## Installation pip: ```pip install pypsa``` conda/mamba: ```conda install -c conda-forge pypsa``` Additionally, install a solver. ## Usage ```py 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](https://pypsa.readthedocs.io/en/latest/examples-basic.html) available as [Jupyter notebooks](https://jupyter.org/). They are also described in the [doc/examples.rst](doc/examples.rst) and are available as Python scripts in [examples/](examples/). ## Screenshots [PyPSA-Eur](https://github.com/PyPSA/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) [SciGRID model](https://power.scigrid.de/) simulating the German power system for 2015. ![image](doc/img/stacked-gen_and_storage-scigrid.png) ![image](doc/img/lmp_and_line-loading.png) ## Dependencies PyPSA is written and tested to be compatible with Python 3.7 and above. The last release supporting Python 2.7 was PyPSA 0.15.0. It leans heavily on the following Python packages: - [pandas](http://pandas.pydata.org/) for storing data about components and time series - [numpy](http://www.numpy.org/) and [scipy](http://scipy.org/) for calculations, such as linear algebra and sparse matrix calculations - [networkx](https://networkx.github.io/) for some network calculations - [matplotlib](https://matplotlib.org/) for static plotting - [linpy](https://github.com/PyPSA/linopy) for preparing optimisation problems (currently only linear and mixed integer linear optimisation) - [cartopy](https://scitools.org.uk/cartopy) for plotting the baselayer map - [pytest](http://pytest.org/) for unit testing - [logging](https://docs.python.org/3/library/logging.html) for managing messages The optimisation uses interface libraries like `linopy` which are independent of the preferred solver. You can use e.g. one of the free solvers [GLPK](https://www.gnu.org/software/glpk/) and [CLP/CBC](https://github.com/coin-or/Cbc/) or the commercial solver [Gurobi](http://www.gurobi.com/) for which free academic licenses are available. ## Documentation Please check the [documentation](https://pypsa.readthedocs.io). ## 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. - In case of code-related **questions**, please post on [stack overflow](https://stackoverflow.com/questions/tagged/pypsa). - For non-programming related and more general questions please refer to the [mailing list](https://groups.google.com/group/pypsa). - To **discuss** with other PyPSA users, organise projects, share news, and get in touch with the community you can use the [discord server](https://discord.gg/AnuJBk23FU). - For **bugs and feature requests**, please use the [PyPSA Github Issues page](https://github.com/PyPSA/PyPSA/issues). - For **troubleshooting**, please check the [troubleshooting](https://pypsa.readthedocs.io/en/latest/troubleshooting.html) in the documentation. ## Code of Conduct Please respect our [code of conduct](CODE_OF_CONDUCT.md). ## 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](https://arxiv.org/abs/1707.09913), 2018, [Journal of Open Research Software](https://openresearchsoftware.metajnl.com/), 6(1), [arXiv:1707.09913](https://arxiv.org/abs/1707.09913), [DOI:10.5334/jors.188](https://doi.org/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 stored on [Zenodo](https://zenodo.org/) 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](https://zenodo.org/badge/DOI/10.5281/zenodo.3946412.svg)](https://doi.org/10.5281/zenodo.3946412) or from the overall PyPSA Zenodo DOI for Versions up to 0.17.0: [![image](https://zenodo.org/badge/DOI/10.5281/zenodo.786605.svg)](https://doi.org/10.5281/zenodo.786605) # Licence Copyright 2015-2023 [PyPSA Developers](https://pypsa.readthedocs.io/en/latest/developers.html) PyPSA is licensed under the open source [MIT License](https://github.com/PyPSA/PyPSA/blob/master/LICENSE.txt).