Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments

Seonho Park, Wenbo Chen, Dahye Han, Mathieu Tanneau, Pascal Van Hentenryck

Abstract:

Reliability Assessment Commitment (RAC) Opti-
mization is increasingly important in grid operations due to
larger shares of renewable generations in the generation mix
and increased prediction errors. Independent System Operators
(ISOs) also aim at using finer time granularities, longer time
horizons, and possibly stochastic formulations for additional
economic and reliability benefits. The goal of this paper is to
address the computational challenges arising in extending the
scope of RAC formulations. It presents RACLEARN that (1)
uses a Graph Neural Network (GNN) based architecture to
predict generator commitments and active line constraints, (2)
associates a confidence value to each commitment prediction,
(3) selects a subset of the high-confidence predictions, which
are (4) repaired for feasibility, and (5) seeds a state-of-the-
art optimization algorithm with feasible predictions and active
constraints. Experimental results on exact RAC formulations
used by the Midcontinent Independent System Operator (MISO)
and an actual transmission network (8965 transmission lines,
6708 buses, 1890 generators, and 6262 load units) show that
the RACLEARN framework can speed up RAC optimization by
factors ranging from 2 to 4 with negligible loss in solution quality.