  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.
