Utilities API

Net Augmentation

potpourri.net_augmentation.prepare_net

Utility functions for pre-processing pandapower networks before building OPF models.

apply_loadcase_to_sb_net(net, case)

Scales load and generation according to a named SimBench load case.

Parameters:

  • net — pandapower network with net.loadcases DataFrame
  • case — load case name (e.g. 'lW' for low wind, 'hL' for high load)

Applies scaling factors from net.loadcases.loc[case] to the following columns: pload, qload, Wind_p, PV_p, RES_p, Slack_vm.

Example:

from potpourri.net_augmentation.prepare_net import apply_loadcase_to_sb_net
apply_loadcase_to_sb_net(net, case='lW')

add_regulatory_q_control_to_wind(net, variant)

Marks wind generators as controllable and assigns a Q-V characteristic variant for reactive power control.

Parameters:

  • net — pandapower network
  • variant — Q-V curve variant (integer 0–2):
    • 0 — constant Q, no voltage dependency
    • 1 — Q-P droop (grid code variant 1)
    • 2 — Q-U droop (grid code variant 2)

Sets sgen.controllable = True and sgen.var_q = variant for all sgens of type 'Wind'. Also copies p_mw to p_inst_mw for installed capacity tracking.

Example:

from potpourri.net_augmentation.prepare_net import add_regulatory_q_control_to_wind
add_regulatory_q_control_to_wind(net, variant=1)

upgrade_pandapower_net(old_net)

Migrates a pandapower network saved with an older version to the current pandapower format.

Parameters:

  • old_net — network object from an older pandapower version

Returns: A fresh pandapower network with all DataFrames copied from old_net.

Example:

from potpourri.net_augmentation.prepare_net import upgrade_pandapower_net
net = upgrade_pandapower_net(old_net)

Plotting

potpourri.plotting.plot_functions

Visualisation utilities for OPF results and grid code characteristics. All plotting functions use Plotly or Matplotlib depending on the function.

set_plt_config()

Applies standard figure configuration (text width, colour scheme, font settings) to Matplotlib.

plot_wind_hc_results(nets, offset=None)

Plots wind generator locations and their optimised output across multiple HC_ACOPF solutions.

Parameters:

  • nets — list of pandapower networks with HC_ACOPF results
  • offset — optional coordinate offset for label placement

plot_pq_gridcodes()

Plots Q-P grid code envelopes for all three reactive power control variants. Returns a Plotly figure.

plot_pq_res(nets, labels=None)

Overlays actual Q-P operating points from nets onto the grid code envelope plot.

Parameters:

  • nets — list of pandapower networks with res_sgen populated
  • labels — optional list of legend labels

plot_qu_gridcodes()

Plots Q-U grid code envelopes (reactive power vs. voltage magnitude).

plot_qu_res(nets, labels=None)

Overlays actual Q-U operating points from solved networks onto the Q-U envelope.

plot_all_pG(hcs)

Plots a bar chart of wind generator real power output across a list of HC_ACOPF model instances.

Parameters:

  • hcs — list of solved HC_ACOPF model instances