  Reliability Assessment Commitment (RAC) Optimization 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.
