Skip to content

Convenient Power System Modelling and Analysis based on PYPOWER and pandas

License

Notifications You must be signed in to change notification settings

hilbrich/pandapower

 
 

Repository files navigation

logo

PyPI versions docs codecov codacy BSD pepy binder

pandapower is an easy to use network calculation program aimed to automate the analysis and optimization of power systems. It uses the data analysis library pandas and is compatible with the commonly used MATPOWER / PYPOWER case format. pandapower allows using different solvers including an improved Newton-Raphson power flow implementation, all PYPOWER solvers, the C++ library solvers for fast steady-state distribution power system analysis of PowerGridModel, the Newton-Raphson power flow solvers in the C++ library lightsim2grid, and the PowerModels.jl library.

More information about pandapower can be found on www.pandapower.org:

About pandapower:

Getting Started:

If you are interested in the latest pandapower developments, subscribe to our mailing list!

SimBench_logo

To get realistic load profile data and grid models across all voltage levels that are ready to be used in pandapower, have a look at the SimBench project website or on GitHub.

pandapipes_logo

If you want to model pipe networks (heat, gas or water) as well, we recommend pandapower's sibling project pandapipes (website, GitHub repository).


pandapower is a joint development of the research group Energy Management and Power System Operation, University of Kassel and the Department for Distribution System Operation at the Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), Kassel.

http://www.pandapower.org/images/contact/Logo_e2n.png

logo

We welcome contributions to pandapower of any kind - if you want to contribute, please check out the pandapower contribution guidelines.

About

Convenient Power System Modelling and Analysis based on PYPOWER and pandas

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 86.8%
  • Jupyter Notebook 12.7%
  • Other 0.5%