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

TL;DR

This paper demonstrates that graph neural networks can accurately and efficiently predict power grid risk conditions hours ahead, improving situational awareness without requiring detailed future topology data.

Contribution

The study introduces a GNN-based approach for hours-ahead risk assessment in power grids, effectively handling stochastic variables and complex grid configurations.

Findings

GNNs predict system-level and branch-level QoIs with high accuracy.

GNNs outperform traditional methods in speed and reliability.

Method validated on synthetic grids of varying sizes.

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 generation and load demand), and their statistical correlations, are rigorously considered while generating the inputs for the training data. The outputs in the training data, obtained by solving numerous mixed-integer linear programming (MILP) optimal power flow problems, correspond to system-level, zonal and transmission line-level quantities of interest (QoIs). The QoIs predicted by the GNNs are used to conduct hours-ahead, sampling-based reliability and risk assessment w.r.t. zonal and system-level (load shedding) as well as branch-level (overloading) failure events. The proposed methodology is demonstrated for three synthetic grids with sizes ranging from 118 to 2848 buses. Our results demonstrate that GNNs are capable of providing fast and accurate prediction of QoIs and can be good proxies for computationally expensive MILP algorithms. The excellent accuracy of GNN-based reliability and risk assessment suggests that GNN models can substantially improve situational awareness by quickly providing rigorous reliability and risk estimates.