pypowsybl.flowdecomposition.Parameters#

class Parameters(enable_losses_compensation=None, losses_compensation_epsilon=None, sensitivity_epsilon=None, rescale_mode=None, dc_fallback_enabled_after_ac_divergence=None, sensitivity_variable_batch_size=None)[source]#

Parameters for a flowdecomposition execution.

All parameters are first read from you configuration file, then overridden with the constructor arguments.

Parameters:

  • enable_losses_compensation (bool) – Enable losses compensation. Use True to enable AC losses compensation on the DC network.

  • losses_compensation_epsilon (float) – Filter loads from the losses compensation. The loads with a too small absolute active power will be not be connected to the network. Use pp.flowdecomposition.Parameters.DISABLE_LOSSES_COMPENSATION_EPSILON = -1 to disable filtering.

  • sensitivity_epsilon (float) – Filter sensitivity values The absolute small sensitivity values will be ignored. Use pp.flowdecomposition.Parameters.DISABLE_SENSITIVITY_EPSILON = -1 to disable filtering.

  • rescale_enabled – Rescale the flow decomposition to the AC reference. Use``True`` to rescale flow decomposition to the AC reference.

  • dc_fallback_enabled_after_ac_divergence (bool) – Defines the fallback bahavior after an AC divergence Use True to run DC loadflow if an AC loadflow diverges (default). Use False to throw an exception if an AC loadflow diverges.

  • sensitivity_variable_batch_size (int) – Defines the chunk size for sensitivity analysis. This will reduce memory footprint of flow decomposition but increase computation time. If setting a too high value, a max integer error may be thrown.

  • rescale_mode (pypowsybl._pypowsybl.RescaleMode) –