Library dependency issues¶
If you are experiencing problems with PyPSA or with the importing of the libraries on which PyPSA depends, please first check that you are working with the latest versions of all packages.
Consistency check on network¶
Checks the network for consistency; e.g. that all components are connected to existing buses and that no impedances are singular.
Prints warnings if anything is potentially inconsistent.
Problems with power flow convergence¶
network.pf() is not converging there are two possible reasons:
The problem you have defined is not solvable (e.g. because in reality you would have a voltage collapse)
The problem is solvable, but there are numerical instabilities in the solving algorithm (e.g. Newton-Raphson is known not to converge even for solvable problems; or the flat solution PyPSA uses as an initial guess is too far from the correction solution because of transformer phase-shifts)
There are some steps you can take to distinguish these two cases:
Check the units you have used to define the problem are correct. If your units are out by a factor 1000 (e.g. using kW instead of MW) don’t be surprised if your problem is no longer solvable.
Check with a linear power flow
network.lpf()that all voltage angles differences across branches are less than 40 degrees. You can do this with the following code:
import pandas as pd, numpy as np now = network.snapshots angle_diff = pd.Series(network.buses_t.v_ang.loc[now,network.lines.bus0].values - network.buses_t.v_ang.loc[now,network.lines.bus1].values, index=network.lines.index) (angle_diff*180/np.pi).describe()
You can seed the non-linear power flow initial guess with the voltage angles from the linear power flow. This is advisable if you have transformers with phase shifts in the network, which lead to solutions far away from the flat initial guess of all voltage angles being zero. To seed the problem activate the
Reduce all power values
q_setof generators and loads to a fraction, e.g. 10%, solve the load flow and use it as a seed for the power at 20%, iteratively up to 100%.
Some attributes are generated dynamically and are therefore only copies. If you change data in them, this will NOT update the original data. They are all defined as functions to make this clear.
network.branches()returns a DataFrame which is a concatenation of
sub_network.generators()returns a DataFrame consisting of generators in