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:
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
Small meshed AC-DC toy model
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
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.
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.