Graph Neural Networks for Power Grid Operational Risk Assessment
Yadong Zhang Pranav M Karve Sankaran Mahadevan
In this article, the utility of graph neural network (GNN) surrogates for
Monte Carlo (MC) sampling-based risk quantification in daily operations of
power grid is investigated. The MC simulation process necessitates solving a
large number of optimal power flow (OPF) problems corresponding to the sample
values of stochastic grid variables (power demand and renewable generation),
which is computationally prohibitive. Computationally inexpensive surrogates of
the OPF problem provide an attractive alternative for expedited MC simulation.
GNN surrogates are especially suitable due to their superior ability to handle
graph-structured data. Therefore, GNN surrogates of OPF problem are trained
using supervised learning. They are then used to obtain Monte Carlo (MC)
samples of the quantities of interest (operating reserve, transmission line
flow) given the (hours-ahead) probabilistic wind generation and load forecast.
The utility of GNN surrogates is evaluated by comparing OPF-based and GNN-based
grid reliability and risk for IEEE Case118 synthetic grid. It is shown that the
GNN surrogates are sufficiently accurate for predicting the (bus-level,
branch-level and system-level) grid state and enable fast as well as accurate
operational risk quantification for power grids. The article thus develops
various tools for fast reliability and risk quantification for real-world power
grids using GNNs.