Synergizing Machine Learning with ACOPF: A Comprehensive Overview

TL;DR

This paper reviews the integration of machine learning techniques with ACOPF problems, highlighting recent approaches, challenges, and future research directions in addressing the nonlinear and nonconvex nature of power flow optimization.

Contribution

It provides a comprehensive overview of how machine learning is being applied to ACOPF problems and suggests future research pathways in this emerging area.

Findings

Machine learning offers promising approaches for ACOPF solutions.

Current methods often yield near-optimal or local solutions.

Research in this area is still in early stages and expanding.

Abstract

Alternative current optimal power flow (ACOPF) problems have been studied for over fifty years, and yet the development of an optimal algorithm to solve them remains a hot and challenging topic for researchers because of their nonlinear and nonconvex nature. A number of methods based on linearization and convexification have been proposed to solve to ACOPF problems, which result in near-optimal or local solutions, not optimal solutions. Nowadays, with the prevalence of machine learning, some researchers have begun to utilize this technology to solve ACOPF problems using the historical data generated by the grid operators. The present paper reviews the research on solving ACOPF problems using machine learning and neural networks and proposes future studies. This body of research is at the beginning of this area, and further exploration can be undertaken into the possibilities of solving ACOPF problems using machine learning.