Large-scale power grid hierarchical segmentation based on power-flow affinities

Antoine Marot (1) , Sami Tazi (1) , Benjamin Donnot (2, 3, 1) , Patrick Panciatici (1)

1 RTE - Réseau de Transport d'Electricité [Paris]
2 LRI - Laboratoire de Recherche en Informatique
3 TAU - TAckling the Underspecified

The segmentation of large scale power grids into zones allows a better understanding of its structure, as the control room operators will naturally but manually do for any study. In this paper we provide a new automatic hierarchical method based on the community detection algorithm \textit{Infomap}. Our main contribution is to offer as input a new representation of the power grid, called the security analysis, that represents power flow affinities beyond the connectivity of the grid, a point that will become even more relevant for tomorrow's cyber-physical system. Indeed we already discover few relevant and important clusters that are not connected in the actual grid topology. To better describe and investigate the method, we apply it here on the well-studied IEEE-RTS-96 and IEEE-118. We further applied our method on the large-scale French Power Grid which showed promising results given its puzzling resemblance with the historical RTE regional segmentation.

Domaines: Machine Learning [stat.ML]; Energie électrique

Antoine Marot, Sami Tazi, Benjamin Donnot, Patrick Panciatici. Large-scale power grid hierarchical segmentation based on power-flow affinities. 2017. ⟨hal-01633508v2⟩