screen#

popo.network.screen(dss, transformers, window, start_step=0, step_minutes=30, max_control_iterations=200)#

Peak apparent power and voltage extremes per transformer over a window.

The start step matters on a cooling-dominated feeder, for the same reason it does in popo.powerflow.solver.solve_time_series(): a window in winter understates peak loading where demand is summer-peaking.

Parameters:

  • dss (Any) – An opendssdirect module with a compiled circuit, already carrying the scenario’s DER installations for this sweep point.

  • transformers (Sequence[Transformer] | DataFrame) – The transformers to screen, as objects with name, rating_kva and low_voltage_bus, or a frame with those columns; typically the whole population rather than a sample.

  • window (int) – Number of steps to screen over.

  • start_step (int) – Step offset at which to begin, in units of step_minutes.

  • step_minutes (int) – Length of one step.

  • max_control_iterations (int) – Iteration limit for the inverter control loop.

Return type:

DataFrame

Returns:

A frame with one row per transformer – transformer, kva_rating, s_peak_kva, utilisation (s_peak_kva / kva_rating), v_min_pu, v_max_pu and diverged_steps.

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