Introduction

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

Screenshots

  • PyPSA-Eur optimising capacities of generation, storage and transmission lines (9% line volume expansion allowed) for a 95% reduction in CO2 emissions in Europe compared to 1990 levels

_images/elec_s_256_lv1.09_Co2L-3H.png
  • SciGRID model simulating the German power system for 2015. Interactive plots also be generated with the plotly library, as shown in this Notebook

_images/stacked-gen_and_storage-scigrid.png _images/lmp_and_line-loading.png _images/reactive-power.png
  • Small meshed AC-DC toy model

_images/ac_dc_meshed.png

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

What PyPSA uses under the hood

PyPSA is written and tested to be compatible with Python 2.7, 3.6 and 3.7.

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

  • cartopy for plotting the baselayer map

  • 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. one of the free solvers GLPK and CLP/CBC or the commercial solver Gurobi for which free academic licenses are available.

The time-expensive calculations, such as solving sparse linear equations, are carried out using the scipy.sparse libraries.

Other comparable software

For a full list see Comparable Software.

PyPSA is not as fully featured as other power system simulation tools such as the Matlab-based free software PSAT or the commercial package DIgSILENT PowerFactory.

However for power flow and optimal power flow over several time snapshots with variable renewable energy sources and/or storage and/or mixed AC-DC systems, it offers the flexibility of Python and the transparency of free software.

Another Python power system tool is PYPOWER, which is based on the Matlab-based MATPOWER. In contrast to PYPOWER, PyPSA has an easier-to-use data model (pandas DataFrames instead of numpy arrays), support for time-varying data inputs and support for multiply-connected networks using both AC and DC. PyPSA uses some of the sparse-matrix constructs from PYPOWER.