Metadata-Version: 1.1
Name: pypsa
Version: 0.13.0
Summary: Python for Power Systems Analysis
Home-page: https://github.com/PyPSA/PyPSA
Author: Tom Brown (FIAS), Jonas Hoersch (FIAS), David Schlachtberger (FIAS)
Author-email: brown@fias.uni-frankfurt.de
License: GPLv3
Description: |badge_travis| |badge_pypi| |badge_license|
        
        -----
        
        ################################
        Python for Power System Analysis
        ################################
        
        .. contents::
        
        .. section-numbering::
        
        
        About
        =====
        
        PyPSA stands for "Python for Power System Analysis". It is pronounced "pipes-ah".
        
        PyPSA is a `free software
        <http://www.gnu.org/philosophy/free-sw.en.html>`_ 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, sector coupling and mixed
        alternating and direct current networks. PyPSA is designed to scale
        well with large networks and long time series.
        
        As of 2018 PyPSA is under heavy development and therefore it is
        recommended to use caution when using it in a production environment.
        Some APIs may change - the changes in each PyPSA version are listed in
        the `doc/release_notes.rst <doc/release_notes.rst>`_.
        
        
        
        PyPSA was initially developed 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/>`_.
        
        
        Documentation
        =============
        
        `Documentation as a website <http://www.pypsa.org/doc/index.html>`_
        
        `Documentation as a PDF <http://www.pypsa.org/doc/PyPSA.pdf>`_
        
        `Quick start <http://www.pypsa.org/doc/quick_start.html>`_
        
        `Examples <http://www.pypsa.org/examples/>`_
        
        Documentation is in `sphinx
        <http://www.sphinx-doc.org/en/stable/>`_ 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 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 and transmission)
        
        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.uni-kassel.de/eecs/fachgebiete/e2n/software/pandapower.html>`_
        * 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), etc.; each of these is demonstrated in the `examples
          <http://www.pypsa.org/examples/>`_
        
        
        Functionality that will definitely be added soon:
        
        * Multi-year investment optimisation
        * Simple RMS simulations with the swing equation
        * Distributed active power slack
        * Non-linear power flow solution using `analytic continuation
          <https://en.wikipedia.org/wiki/Holomorphic_embedding_load_flow_method>`_
          in the complex plane following `GridCal
          <https://github.com/SanPen/GridCal>`_
        
        Functionality that may be added in the future:
        
        * Short-circuit current calculations
        * Dynamic RMS simulations
        * Small signal stability analysis
        * Interactive web-based GUI with SVG
        * OPF with the full non-linear network equations
        * Dynamic EMT simulations
        * Unbalanced load flow
        * Port to `Julia <http://julialang.org/>`_
        
        
        Example scripts as Jupyter notebooks
        ====================================
        
        There are `extensive examples <http://www.pypsa.org/examples/>`_
        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
        ===========
        
        The showcase for PyPSA is the `SciGRID example
        <https://pypsa.org/examples/scigrid-lopf-then-pf-plotly.html>`_ which
        demonstrates interactive plots generated with the `plotly
        <https://plot.ly/python/>`_ library.
        
        .. image:: http://www.pypsa.org/img/line-loading.png
        
        .. image:: http://www.pypsa.org/img/lmp.png
        
        .. image:: http://www.pypsa.org/img/reactive-power.png
        
        .. image:: http://www.pypsa.org/img/stacked-gen.png
        
        .. image:: http://www.pypsa.org/img/storage-scigrid.png
        
        .. image:: http://www.pypsa.org/img/scigrid-curtailment.png
        
        .. image:: http://www.pypsa.org/img/meshed-ac-dc.png
        
        Optimised capacities of generation and storage for a 95% reduction in CO2 emissions in Europe compare to 1990 levels:
        
        .. image:: http://www.pypsa.org/img/euro-pie-pre-7-branch_limit-1-256.png
        .. image:: http://www.pypsa.org/img/legend-flat.png
        
        
        
        What PyPSA uses under the hood
        ===============================
        
        PyPSA is written and tested to be compatible with both Python 2.7 and
        Python 3.5.
        
        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
        * `pyomo <http://www.pyomo.org/>`_ for preparing optimisation problems (currently only linear)
        * `plotly <https://plot.ly/python/>`_ for interactive plotting
        * `matplotlib <https://matplotlib.org/>`_ for static plotting
        * `networkx <https://networkx.github.io/>`_ for some network calculations
        * `py.test <http://pytest.org/>`_ for unit testing
        * `logging <https://docs.python.org/3/library/logging.html>`_ 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
        <https://groups.google.com/group/pypsa>`_.
        
        
        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.
        This can be found linked from the overall PyPSA Zenodo DOI:
        
        .. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.786605.svg
           :target: https://doi.org/10.5281/zenodo.786605
        
        
        Licence
        =======
        
        Copyright 2015-2018 Tom Brown (FIAS), Jonas Hörsch (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
        MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
        `GNU General Public License <LICENSE.txt>`_ for more details.
        
        .. |link-latest-doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.786605.svg
        .. _link-latest-doi: https://doi.org/10.5281/zenodo.786605
        
        .. |badge_pypi| image:: https://img.shields.io/pypi/v/pypsa.svg
            :target: https://pypi.python.org/pypi/pypsa
            :alt: PyPI version
        
        .. |badge_license| image:: https://img.shields.io/pypi/l/pypsa.svg
            :target: #license
        
        .. |badge_travis| image:: https://img.shields.io/travis/PyPSA/PyPSA/master.svg
            :target: https://travis-ci.org/PyPSA/PyPSA
            :alt: Build status on Linux
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Natural Language :: English
Classifier: Operating System :: OS Independent
