Unsupervised Optimal Power Flow Using Graph Neural Networks

Optimal power flow is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. This is a non-convex problem shown to be NP-hard. We use a graph neural network to learn a nonlinear function between the power demanded and the corresponding allocation. We learn the solution in an unsupervised manner, minimizing the cost directly. To consider the power system constraints, we propose a novel barrier method that is differentiable and works on initially infeasible points. We show through simulations that the use of graph neural networks in this unsupervised learning context leads to solutions comparable to standard solvers while being computationally efficient and avoiding constraint violations.