  Optimal power flow (OPF) is used to perform generation redispatch in power
system real-time operations. N-1 OPF can ensure safe grid operations under
diverse contingency scenarios. For large and intricate power networks with
numerous variables and constraints, achieving an optimal solution for real-time
N-1 OPF necessitates substantial computational resources. To mitigate this
challenge, machine learning (ML) is introduced as an additional tool for
predicting congested or heavily loaded lines dynamically. In this paper, an
advanced ML model known as the augmented hierarchical graph neural network
(AHGNN) was proposed to predict critical congested lines and create N-1 reduced
OPF (N-1 ROPF). The proposed AHGNN-enabled N-1 ROPF can result in a remarkable
reduction in computing time while retaining the solution quality. Several
variations of GNN-based ML models are also implemented as benchmark to
demonstrate effectiveness of the proposed AHGNN approach. Case studies prove
the proposed AHGNN and the associated N-1 ROPF are highly effective in reducing
computation time while preserving solution quality, highlighting the promising
potential of ML, particularly GNN in enhancing power system operations.
