  OPF problems are formulated and solved for power system operations,
especially for determining generation dispatch points in real-time. For large
and complex power system networks with large numbers of variables and
constraints, finding the optimal solution for real-time OPF in a timely manner
requires a massive amount of computing power. This paper presents a new method
to reduce the number of constraints in the original OPF problem using a graph
neural network (GNN). GNN is an innovative machine learning model that utilizes
features from nodes, edges, and network topology to maximize its performance.
In this paper, we proposed a GNN model to predict which lines would be heavily
loaded or congested with given load profiles and generation capacities. Only
these critical lines will be monitored in an OPF problem, creating a reduced
OPF (ROPF) problem. Significant saving in computing time is expected from the
proposed ROPF model. A comprehensive analysis of predictions from the GNN model
was also made. It is concluded that the application of GNN for ROPF is able to
reduce computing time while retaining solution quality.
