Leveraging power grid topology in machine learning assisted optimal power flow

TL;DR

This paper compares neural network architectures for machine learning-assisted optimal power flow, highlighting the advantages of GNNs in handling variable grid topologies over FCNNs and CNNs.

Contribution

It introduces a framework for evaluating FCNN, CNN, and GNN models in OPF and demonstrates GNNs' effectiveness with changing power grid topologies.

Findings

GNNs outperform FCNNs and CNNs in variable topology scenarios.

Locality properties are limited in synthetic power grids.

GNNs effectively incorporate topological changes like line contingencies.

Abstract

Machine learning assisted optimal power flow (OPF) aims to reduce the computational complexity of these non-linear and non-convex constrained optimization problems by consigning expensive (online) optimization to offline training. The majority of work in this area typically employs fully connected neural networks (FCNN). However, recently convolutional (CNN) and graph (GNN) neural networks have also been investigated, in effort to exploit topological information within the power grid. Although promising results have been obtained, there lacks a systematic comparison between these architectures throughout literature. Accordingly, we introduce a concise framework for generalizing methods for machine learning assisted OPF and assess the performance of a variety of FCNN, CNN and GNN models for two fundamental approaches in this domain: regression (predicting optimal generator set-points) and classification (predicting the active set of constraints). For several synthetic power grids with interconnected utilities, we show that locality properties between feature and target variables are scarce and subsequently demonstrate marginal utility of applying CNN and GNN architectures compared to FCNN for a fixed grid topology. However, with variable topology (for instance, modeling transmission line contingency), GNN models are able to straightforwardly take the change of topological information into account and outperform both FCNN and CNN models.