Power grid operational risk assessment using graph neural network surrogates

TL;DR

This paper explores using graph neural networks as fast surrogate models for power grid operational decision-making algorithms, enabling efficient and accurate risk assessment based on Monte Carlo simulations.

Contribution

It introduces GNN-based surrogates for OPF and SCUC, demonstrating their effectiveness in rapid, accurate risk and reliability quantification in power systems.

Findings

GNNs accurately predict key operational quantities.

GNN surrogates significantly reduce computation time.

GNN-based risk assessments align well with traditional methods.

Abstract

We investigate the utility of graph neural networks (GNNs) as proxies of power grid operational decision-making algorithms (optimal power flow (OPF) and security-constrained unit commitment (SCUC)) to enable rigorous quantification of the operational risk. To conduct principled risk analysis, numerous Monte Carlo (MC) samples are drawn from the (foretasted) probability distributions of spatio-temporally correlated stochastic grid variables. The corresponding OPF and SCUC solutions, which are needed to quantify the risk, are generated using traditional OPF and SCUC solvers to generate data for training GNN model(s). The GNN model performance is evaluated in terms of the accuracy of predicting quantities of interests (QoIs) derived from the decision variables in OPF and SCUC. Specifically, we focus on thermal power generation and load shedding at system and individual zone level. We also perform reliability and risk quantification based on GNN predictions and compare with that obtained from OPF/SCUC solutions. Our results demonstrate that GNNs are capable of providing fast and accurate prediction of QoIs and thus can be good surrogate models for OPF and SCUC. The excellent accuracy of GNN-based reliability and risk assessment further suggests that GNN surrogate has the potential to be applied in real-time and hours-ahead risk quantification.