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        "author": "Tom Brown (FIAS), Jonas Hoersch (FIAS), David Schlachtberger (FIAS)",
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        "description": "|badge_travis| |badge_pypi| |badge_license|\n\n-----\n\n################################\nPython for Power System Analysis\n################################\n\n.. contents::\n\n.. section-numbering::\n\n\nAbout\n=====\n\nPyPSA stands for \"Python for Power System Analysis\". It is pronounced \"pipes-ah\".\n\nPyPSA is a `free software\n<http://www.gnu.org/philosophy/free-sw.en.html>`_ toolbox for\nsimulating and optimising modern power systems that include features\nsuch as conventional generators with unit commitment, variable wind\nand solar generation, storage units, coupling to other energy sectors,\nand mixed alternating and direct current networks. PyPSA is designed\nto scale well with large networks and long time series.\n\nThis project is maintained by the `Energy System Modelling\ngroup <https://www.iai.kit.edu/english/2338.php>`_ at the `Institute for\nAutomation and Applied\nInformatics <https://www.iai.kit.edu/english/index.php>`_ at the\n`Karlsruhe Institute of\nTechnology <http://www.kit.edu/english/index.php>`_. The group is funded by the\n`Helmholtz Association <https://www.helmholtz.de/en/>`_ until 2024.\nPrevious versions were developed by the `Renewable Energy Group\n<https://fias.uni-frankfurt.de/physics/schramm/renewable-energy-system-and-network-analysis/>`_\nat `FIAS <https://fias.uni-frankfurt.de/>`_ to carry out simulations\nfor the `CoNDyNet project <http://condynet.de/>`_, financed by the\n`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/>`_.\n\n\nDocumentation\n=============\n\n`Documentation as a website <http://www.pypsa.org/doc/index.html>`_\n\n`Documentation as a PDF <http://www.pypsa.org/doc/PyPSA.pdf>`_\n\n`Quick start <http://www.pypsa.org/doc/quick_start.html>`_\n\n`Examples <http://www.pypsa.org/examples/>`_\n\nDocumentation is in `sphinx\n<http://www.sphinx-doc.org/en/stable/>`_ reStructuredText format in\nthe ``doc`` sub-folder of the repository.\n\n\nWhat PyPSA does and does not do (yet)\n=======================================\n\nPyPSA can calculate:\n\n* static power flow (using both the full non-linear network equations and\n  the linearised network equations)\n* linear optimal power flow (least-cost optimisation of power plant\n  and storage dispatch within network constraints, using the linear\n  network equations, over several snapshots)\n* security-constrained linear optimal power flow\n* total electricity/energy system least-cost investment optimisation\n  (using linear network equations, over several snapshots\n  simultaneously for optimisation of generation and storage dispatch\n  and investment in the capacities of generation, storage,\n  transmission and other infrastructure)\n\nIt has models for:\n\n* meshed multiply-connected AC and DC networks, with controllable\n  converters between AC and DC networks\n* standard types for lines and transformers following the implementation in `pandapower <https://www.pandapower.org/>`_\n* conventional dispatchable generators with unit commitment\n* generators with time-varying power availability, such as\n  wind and solar generators\n* storage units with efficiency losses\n* simple hydroelectricity with inflow and spillage\n* coupling with other energy carriers\n* basic components out of which more complicated assets can be built,\n  such as Combined Heat and Power (CHP) units, heat pumps, resistive\n  Power-to-Heat (P2H), Power-to-Gas (P2G), battery electric vehicles\n  (BEVs), Fischer-Tropsch, direct air capture (DAC), etc.; each of\n  these is demonstrated in the `examples\n  <http://www.pypsa.org/examples/>`_\n\n\nFunctionality that may be added in the future:\n\n* Multi-year investment optimisation\n* Distributed active power slack\n* Interactive web-based GUI with SVG\n* OPF with the full non-linear network equations\n* Port to `Julia <http://julialang.org/>`_\n\nOther complementary libraries:\n\n* `pandapower <https://www.pandapower.org/>`_ for more\n  detailed modelling of distribution grids, short-circuit\n  calculations, unbalanced load flow and more\n* `PowerDynamics.jl\n  <https://github.com/JuliaEnergy/PowerDynamics.jl>`_ for dynamic\n  modelling of power grids at time scales where differential equations are relevant\n\n\n\nExample scripts as Jupyter notebooks\n====================================\n\nThere are `extensive examples <http://www.pypsa.org/examples/>`_\navailable as `Jupyter notebooks <https://jupyter.org/>`_. They are\nalso described in the `doc/examples.rst <doc/examples.rst>`_ and are\navailable as Python scripts in `examples/ <examples/>`_.\n\nScreenshots\n===========\n\nResults from a PyPSA simulation can be converted into an interactive\nonline animation using `PyPSA-animation\n<https://github.com/PyPSA/PyPSA-animation>`_, see the `PyPSA-Eur-30\nexample <https://www.pypsa.org/animations/pypsa-eur-30/>`_.\n\nAnother showcase for PyPSA is the `SciGRID example\n<https://pypsa.org/examples/scigrid-lopf-then-pf-plotly.html>`_ which\ndemonstrates interactive plots generated with the `plotly\n<https://plot.ly/python/>`_ library.\n\n.. image:: http://www.pypsa.org/img/line-loading.png\n\n.. image:: http://www.pypsa.org/img/lmp.png\n\n.. image:: http://www.pypsa.org/img/reactive-power.png\n\n.. image:: http://www.pypsa.org/img/stacked-gen.png\n\n.. image:: http://www.pypsa.org/img/storage-scigrid.png\n\n.. image:: http://www.pypsa.org/img/scigrid-curtailment.png\n\n.. image:: http://www.pypsa.org/img/meshed-ac-dc.png\n\nOptimised capacities of generation and storage for a 95% reduction in CO2 emissions in Europe compare to 1990 levels:\n\n.. image:: http://www.pypsa.org/img/euro-pie-pre-7-branch_limit-1-256.png\n.. image:: http://www.pypsa.org/img/legend-flat.png\n\n\n\nWhat PyPSA uses under the hood\n===============================\n\nPyPSA is written and tested to be compatible with both Python 2.7 and\nPython 3.6.\n\nIt leans heavily on the following Python packages:\n\n* `pandas <http://pandas.pydata.org/>`_ for storing data about components and time series\n* `numpy <http://www.numpy.org/>`_ and `scipy <http://scipy.org/>`_ for calculations, such as\n  linear algebra and sparse matrix calculations\n* `pyomo <http://www.pyomo.org/>`_ for preparing optimisation problems (currently only linear)\n* `plotly <https://plot.ly/python/>`_ for interactive plotting\n* `matplotlib <https://matplotlib.org/>`_ for static plotting\n* `networkx <https://networkx.github.io/>`_ for some network calculations\n* `py.test <http://pytest.org/>`_ for unit testing\n* `logging <https://docs.python.org/3/library/logging.html>`_ for managing messages\n\nThe optimisation uses pyomo so that it is independent of the preferred\nsolver (you can use e.g. the free software GLPK or the commercial\nsoftware Gurobi).\n\nThe time-expensive calculations, such as solving sparse linear\nequations, are carried out using the scipy.sparse libraries.\n\n\n\nMailing list\n============\n\nPyPSA has a Google Group `forum / mailing list\n<https://groups.google.com/group/pypsa>`_.\n\n\nCiting PyPSA\n============\n\n\n\nIf you use PyPSA for your research, we would appreciate it if you\nwould cite the following paper:\n\n* T. Brown, J. H\u00f6rsch, D. Schlachtberger, `PyPSA: Python for Power\n  System Analysis <https://arxiv.org/abs/1707.09913>`_, 2018,\n  `Journal of Open Research Software\n  <https://openresearchsoftware.metajnl.com/>`_, 6(1),\n  `arXiv:1707.09913 <https://arxiv.org/abs/1707.09913>`_,\n  `DOI:10.5334/jors.188 <https://doi.org/10.5334/jors.188>`_\n\n\nPlease use the following BibTeX: ::\n\n   @article{PyPSA,\n      author = {T. Brown and J. H\\\"orsch and D. Schlachtberger},\n      title = {{PyPSA: Python for Power System Analysis}},\n      journal = {Journal of Open Research Software},\n      volume = {6},\n      issue = {1},\n      number = {4},\n      year = {2018},\n      eprint = {1707.09913},\n      url = {https://doi.org/10.5334/jors.188},\n      doi = {10.5334/jors.188}\n   }\n\n\nIf you want to cite a specific PyPSA version, each release of PyPSA is\nstored on `Zenodo <https://zenodo.org/>`_ with a release-specific DOI.\nThis can be found linked from the overall PyPSA Zenodo DOI:\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.786605.svg\n   :target: https://doi.org/10.5281/zenodo.786605\n\n\nLicence\n=======\n\nCopyright 2015-2019 Tom Brown (KIT, FIAS), Jonas H\u00f6rsch (KIT, FIAS),\nDavid Schlachtberger (FIAS)\n\nThis program is free software: you can redistribute it and/or\nmodify it under the terms of the GNU General Public License as\npublished by the Free Software Foundation; either `version 3 of the\nLicense <LICENSE.txt>`_, or (at your option) any later version.\n\nThis program is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n`GNU General Public License <LICENSE.txt>`_ for more details.\n\n.. |link-latest-doi| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.786605.svg\n.. _link-latest-doi: https://doi.org/10.5281/zenodo.786605\n\n.. |badge_pypi| image:: https://img.shields.io/pypi/v/pypsa.svg\n    :target: https://pypi.python.org/pypi/pypsa\n    :alt: PyPI version\n\n.. |badge_license| image:: https://img.shields.io/pypi/l/pypsa.svg\n    :target: #license\n\n.. |badge_travis| image:: https://img.shields.io/travis/PyPSA/PyPSA/master.svg\n    :target: https://travis-ci.org/PyPSA/PyPSA\n    :alt: Build status on Linux",
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