Metadata-Version: 1.1
Name: pypsa
Version: 0.5.0
Summary: Python for Power Systems Analysis
Home-page: https://github.com/FRESNA/PyPSA
Author: Tom Brown (FIAS), Jonas Hoersch (FIAS), David Schlachtberger (FIAS)
Author-email: brown@fias.uni-frankfurt.de
License: GPLv3
Description: 
        
        Python for Power System Analysis
        ================================
        
        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 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 2016 PyPSA is under heavy development and therefore it
        is recommended to use caution when using it in a production
        environment. Some APIs may change - those liable to be updated are
        listed in the `doc/todo.rst <doc/todo.rst>`_.
        
        PyPSA was initially developed by the `Renewable Energy Group
        <https://fias.uni-frankfurt.de/physics/schramm/complex-renewable-energy-networks/>`_
        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>`_
        
        
        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 (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 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
        * conventional dispatchable generators
        * 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
        
        
        Functionality that will definitely be added soon (see also `doc/todo.rst <doc/todo.rst>`_):
        
        * More explicit integration of heating and gas sectors (CHPs, heat pumps, P2H, P2G, etc.)
        * 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:
        
        * Unit Commitment using MILP
        * Short-circuit current calculations
        * Dynamic RMS simulations
        * Small signal stability analysis
        * Interactive web-based GUI
        * OPF with the full non-linear network equations
        * Dynamic EMT simulations
        * Unbalanced load flow
        * Port to `Julia <http://julialang.org/>`_
        
        
        Screenshots and example Jupyter/iPython notebooks
        =================================================
        
        There are `extensive examples <http://www.pypsa.org/examples/>`_ available as Jupyter/iPython notebooks. They are also described in the `doc/examples.rst <doc/examples.rst>`_ and are available as Python scripts in `examples/ <examples/>`_.
        
        Some screenshots:
        
        
        .. 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
        
        .. image:: http://www.pypsa.org/img/europe-map.png
        
        
        
        What PyPSA uses under the hood
        ===============================
        
        PyPSA is written and tested to be compatible with both Python 2.7 and
        Python 3.4.
        
        It leans heavily on the following Python packages:
        
        * `pandas <http://ipython.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)
        * `networkx <https://networkx.github.io/>`_ for some network calculations
        * `py.test <http://pytest.org/>`_ for unit testing
        
        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>`_.
        
        
        Licence
        ==========
        
        PyPSA is released as free software under the `GPLv3
        <http://www.gnu.org/licenses/gpl-3.0.en.html>`_, see `LICENSE.txt
        <LICENSE.txt>`_.
        
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
