Optimal Power Flow Using Graph Neural Networks

TL;DR

This paper introduces a graph neural network approach trained via imitation learning to efficiently approximate optimal power flow solutions in large-scale power grids, addressing scalability and non-convexity issues.

Contribution

It presents a novel GNN-based method for approximating OPF solutions, overcoming scalability limitations of traditional methods and handling non-convexities in large networks.

Findings

GNN accurately approximates OPF solutions on IEEE test cases.

Method scales well to large power networks.

Outperforms traditional approximation techniques.

Abstract

Optimal power flow (OPF) is one of the most important optimization problems in the energy industry. In its simplest form, OPF attempts to find the optimal power that the generators within the grid have to produce to satisfy a given demand. Optimality is measured with respect to the cost that each generator incurs in producing this power. The OPF problem is non-convex due to the sinusoidal nature of electrical generation and thus is difficult to solve. Using small angle approximations leads to a convex problem known as DC OPF, but this approximation is no longer valid when power grids are heavily loaded. Many approximate solutions have been since put forward, but these do not scale to large power networks. In this paper, we propose using graph neural networks (which are localized, scalable parametrizations of network data) trained under the imitation learning framework to approximate a given optimal solution. While the optimal solution is costly, it is only required to be computed for network states in the training set. During test time, the GNN adequately learns how to compute the OPF solution. Numerical experiments are run on the IEEE-30 and IEEE-118 test cases.