  A DC OPF surrogate modeling framework is developed for Monte Carlo (MC)
sampling-based risk quantification in power grid operation. MC simulation
necessitates solving a large number of DC OPF problems corresponding to the
samples of stochastic grid variables (power demand and renewable generation),
which is computationally prohibitive. Computationally inexpensive surrogates of
OPF provide an attractive alternative for expedited MC simulation. Graph neural
network (GNN) surrogates of DC OPF, which are especially suitable to
graph-structured data, are employed in this work. Previously developed DC OPF
surrogate models have focused on accurate operational decision-making and not
on risk quantification. Here, risk quantification-specific aspects of DC OPF
surrogate evaluation is the main focus. To this end, the proposed GNN
surrogates are evaluated using realistic joint probability distributions,
quantification of their risk estimation accuracy, and investigation of their
generalizability. Four synthetic grids (Case118, Case300, Case1354pegase, and
Case2848rte) are used for surrogate model performance evaluation. 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 tools for fast reliability and risk quantification in real-world power
grids using GNN-based surrogates.
