  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.
