An Iterative Approach to Finding Global Solutions of AC Optimal Power Flow Problems

TL;DR

This paper introduces an iterative method that enhances existing AC optimal power flow solvers to find globally optimal solutions, effectively escaping local minima through successive refinements on standard IEEE networks.

Contribution

The paper presents a novel iterative approach that combines local solvers with partial Lagrangian optimization to reliably find global solutions for ACOPF problems.

Findings

Algorithm successfully finds global solutions within a few iterations.

Method outperforms traditional local solvers on IEEE test networks.

Approach effectively escapes local minima to achieve global optimality.

Abstract

The existence of multiple solutions to AC optimal power flow (ACOPF) problems has been noted for decades. Existing solvers are generally successful in finding local solutions, which are stationary points but may not be globally optimal. In this paper, we propose a simple iterative approach to find globally optimal solutions to ACOPF problems. First, we call an existing solver for the ACOPF problem. From the solution and the associated dual variables, we form a partial Lagrangian. Then we optimize this partial Lagrangian and use its solution as a warm start to call the solver again for the ACOPF problem. By repeating this process, we can iteratively improve the solution quality, moving from local solutions to global ones. We show the effectiveness our algorithm on standard IEEE networks. The simulation results show that our algorithm can escape from local solutions to achieve global optimums within a few iterations.