Metadata-Version: 2.1
Name: pypsa
Version: 0.21.0
Summary: Python for Power Systems Analysis
Home-page: https://github.com/PyPSA/PyPSA
Author: PyPSA Developers, see https://pypsa.readthedocs.io/en/latest/developers.html
Author-email: t.brown@tu-berlin.de
License: MIT
Description: # 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 conda](https://github.com/pypsa/pypsa/actions/workflows/CI-conda.yml/badge.svg)](https://github.com/pypsa/pypsa/actions/workflows/CI-conda.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)
        
        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 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.lopf()
        
        # plot results
        n.generators_t.p.plot()
        n.plot()
        ```
        
        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
        -   [pyomo](http://www.pyomo.org/) for preparing optimisation problems
            (currently only linear)
        -   [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 `pyomo` 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.
        
        ## Mailing list
        
        PyPSA has a Google Group [forum / mailing
        list](https://groups.google.com/group/pypsa) 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](https://github.com/PyPSA/PyPSA/issues).
        
        A `Discord server <https://discord.gg/AnuJBk23FU>` hosts every tool
        in the PyPSA ecosystem. We have there public voice and text channels
        that are suitable to organise projects, ask questions,
        share news, or chat with the community.
        
        ## 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 2015-2022 [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).
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: cartopy
Provides-Extra: docs
Provides-Extra: gurobipy
