Machine Learning for AC Optimal Power Flow

TL;DR

This paper investigates machine learning techniques to solve AC Optimal Power Flow problems by predicting generator settings and active constraints, validated on benchmark power grids.

Contribution

It introduces two novel ML formulations for ACOPF, focusing on direct prediction of solutions and constraint sets, advancing computational efficiency.

Findings

ML models accurately predict generator settings

Constraint prediction effectively identifies active constraints

Approaches validated on benchmark power grids

Abstract

We explore machine learning methods for AC Optimal Powerflow (ACOPF) - the task of optimizing power generation in a transmission network according while respecting physical and engineering constraints. We present two formulations of ACOPF as a machine learning problem: 1) an end-to-end prediction task where we directly predict the optimal generator settings, and 2) a constraint prediction task where we predict the set of active constraints in the optimal solution. We validate these approaches on two benchmark grids.