Unsupervised Optimal Power Flow Using Graph Neural Networks
This addresses the challenge of efficient power grid optimization for energy management, though it is incremental as it applies existing GNN methods to a known bottleneck.
The paper tackles the computationally infeasible optimal power flow problem by using an unsupervised graph neural network to learn power allocations from demand, achieving solutions comparable to standard solvers with computational efficiency and minimal constraint violations.
Optimal power flow (OPF) is a critical optimization problem that allocates power to the generators in order to satisfy the demand at a minimum cost. Solving this problem exactly is computationally infeasible in the general case. In this work, we propose to leverage graph signal processing and machine learning. More specifically, we use a graph neural network to learn a nonlinear parametrization between the power demanded and the corresponding allocation. We learn the solution in an unsupervised manner, minimizing the cost directly. In order to take into account the electrical constraints of the grid, we propose a novel barrier method that is differentiable and works on initially infeasible points. We show through simulations that the use of GNNs in this unsupervised learning context leads to solutions comparable to standard solvers while being computationally efficient and avoiding constraint violations most of the time.