Supervised Learning for Optimal Power Flow as a Real-Time Proxy

TL;DR

This paper develops supervised learning models to rapidly estimate ACOPF costs, serving as real-time proxies that significantly reduce computation time while maintaining high accuracy, aiding power network planning.

Contribution

It introduces a supervised learning framework for fast ACOPF cost approximation, enabling real-time decision support in power system planning.

Findings

Achieves less than 1% average error in cost estimation.

Runs several orders of magnitude faster than exact methods.

Validated on IEEE-RTS96 test case.

Abstract

In this work we design and compare different supervised learning algorithms to compute the cost of Alternating Current Optimal Power Flow (ACOPF). The motivation for quick calculation of OPF cost outcomes stems from the growing need of algorithmic-based long-term and medium-term planning methodologies in power networks. Integrated in a multiple time-horizon coordination framework, we refer to this approximation module as a proxy for predicting short-term decision outcomes without the need of actual simulation and optimization of them. Our method enables fast approximate calculation of OPF cost with less than 1% error on average, achieved in run-times that are several orders of magnitude lower than of exact computation. Several test-cases such as IEEE-RTS96 are used to demonstrate the efficiency of our approach.