Graph neural networks for power grid operational risk assessment under evolving grid topology

arXiv:2405.07343v1 Announce Type: cross
Abstract: This article investigates the ability of graph neural networks (GNNs) to identify risky conditions in a power grid over the subsequent few hours, without explicit, high-resolution information regarding future generator on/off status (grid topology) or power dispatch decisions. The GNNs are trained using supervised learning, to predict the power grid's aggregated bus-level (either zonal or system-level) or individual branch-level state under different power supply and demand conditions. The variability of the stochastic grid variables (wind/solar …

abstract article arxiv assessment cs.lg cs.sy decisions eess.sy future generator gnns graph graph neural networks grid identify information networks neural networks power resolution risk risk assessment stat.me topology type