Python for Power System Analysis: Publications
There is a preprint paper describing PyPSA (which has been accepted to the Journal of Open Research Software):
- T. Brown, J. Hörsch, D. Schlachtberger, PyPSA: Python for Power System Analysis, 2017, preprint arXiv:1707.09913
If you use PyPSA for your research, we would appreciate it if you would cite this preprint paper.
If you want to cite a specific PyPSA version, each release of PyPSA is stored on Zenodo with a release-specific DOI. This can be found linked from the overall PyPSA Zenodo DOI: .
The following research papers have used PyPSA:
- J. Hörsch, Joanne Calitz, PyPSA-ZA: Investment and operation co-optimization of integrating wind and solar in South Africa at high spatial and temporal detail, 2017, preprint, code
- Markus Groissböck, Alexandre Gusmao, Impact of high renewable penetration scenarios on system reliability: two case studies in the Kingdom of Saudi Arabia, 2017, preprint
- J. Hörsch, T. Brown, The role of spatial scale in joint optimisations of generation and transmission for European highly renewable scenarios, 2017, accepted to 14th International Conference on the European Energy Market - EEM 2017, preprint
- D. Schlachtberger, T. Brown, S. Schramm, M. Greiner, The Benefits of Cooperation in a Highly Renewable European Electricity Network, Energy, Volume 134, 1 September 2017, Pages 469-481, preprint, all input data, code, output data on Zenodo
- J. Hörsch, H. Ronellenfitsch, D. Witthaut, T. Brown, Linear Optimal Power Flow Using Cycle Flows, 2017, preprint
- Joao Gorenstein Dedecca, Rudi A. Hakvoort, Paulien M. Herder, Transmission expansion simulation for the European Northern Seas offshore grid, Energy, Volume 125, 15 April 2017, Pages 805-824
If you have written a paper or report using PyPSA, please send us the link so we can add it here by emailing Tom Brown (brown at fias.uni-frankfurt.de).