PyPSA: Python for Power System Analysis

1 About

PyPSA stands for "Python for Power System Analysis". It is pronounced "pipes-ah".

PyPSA is a free software 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, coupling to other energy sectors, and mixed alternating and direct current networks. PyPSA is designed to scale well with large networks and long time series.

This project is maintained by the Energy System Modelling group at the Institute for Automation and Applied Informatics at the Karlsruhe Institute of Technology. The group is funded by the Helmholtz Association until 2024. Previous versions were developed by the Renewable Energy Group at FIAS to carry out simulations for the CoNDyNet project, financed by the German Federal Ministry for Education and Research (BMBF) as part of the Stromnetze Research Initiative.

2 Download

See the installation instructions.

You can also see/download the code directly from github.

4 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/energy 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, transmission and other infrastructure)

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
  • 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), Fischer-Tropsch, direct air capture (DAC), etc.; each of these is demonstrated in the examples

Functionality that may be added in the future:

  • Multi-year investment optimisation
  • Distributed active power slack
  • Interactive web-based GUI with SVG
  • OPF with the full non-linear network equations
  • Port to Julia

Other complementary libraries:

  • pandapower for more detailed modelling of distribution grids, short-circuit calculations, unbalanced load flow and more
  • PowerDynamics.jl for dynamic modelling of power grids at time scales where differential equations are relevant

5 Example scripts as Jupyter notebooks

There are extensive examples available as Jupyter notebooks.

6 Screenshots

Results from a PyPSA simulation can be converted into an interactive online animation using PyPSA-animation, see the PyPSA-Eur-30 example.

Another showcase for PyPSA is the SciGRID example which demonstrates interactive plots generated with the plotly library.

line-loading.png

Figure 1: Line loading with high wind feed-in in North Germany.

lmp.png

Figure 2: Nodal prices with high wind feed-in in North Germany.

reactive-power.png

Figure 3: Reactive power in Germany.

stacked-gen.png

Figure 4: Aggregated feed-in over a day.

storage-scigrid.png

Figure 5: Storage operation.

scigrid-curtailment.png

Figure 6: Curtailment of wind.

meshed-ac-dc.png

Figure 7: Meshed AC-DC hybrid nework.

euro-pie-pre-7-branch_limit-1-256.png

Figure 8: Optimised capacities of generation and storage for a 95% reduction in CO2 emissions in Europe compare to 1990 levels.

legend-flat.png

7 What PyPSA uses under the hood

PyPSA is written and tested to be compatible with both Python 2.7 and Python 3.6.

It leans heavily on the following Python packages:

  • pandas for storing data about components and time series
  • numpy and scipy for calculations, such as linear algebra and sparse matrix calculations
  • pyomo for preparing optimisation problems (currently only linear)
  • plotly for interactive plotting
  • matplotlib for static plotting
  • networkx for some network calculations
  • py.test for unit testing
  • logging 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.

8 Mailing list

PyPSA has a Google Group forum / mailing list.

9 Citing PyPSA

If you use PyPSA for your research, we would appreciate it if you would cite the following paper:

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 with a release-specific DOI. This can be found linked from the overall PyPSA Zenodo DOI: zenodo.786605.svg.

10 Licence

PyPSA is released as free software under the GPLv3.