Fast AC Power Flow Optimization using Difference of Convex Functions Programming

TL;DR

This paper introduces a novel difference-of-convex-functions algorithm for AC optimal power flow that offers faster convergence, better warm-start capabilities, and linear complexity, improving simulation efficiency in power systems analysis.

Contribution

It proposes a new DC programming-based method for AC OPF that outperforms traditional interior-point methods in speed and scalability.

Findings

Significant speedups over existing solvers in MATPOWER

Linear computational complexity per iteration

Global convergence to KKT points with linear rate

Abstract

An effective means for analyzing the impact of novel operating schemes on power systems is time domain simulation, for example for investigating optimization-based curtailment of renewables to alleviate voltage violations. Traditionally, interior-point methods are used for solving the non-convex AC optimal power flow (OPF) problems arising in this type of simulation. This paper presents an alternative algorithm that better suits the simulation framework, because it can more effectively be warm-started, has linear computational and memory complexity in the problem size per iteration and globally converges to Karush-Kuhn-Tucker (KKT) points with a linear rate if they exist. The algorithm exploits a difference-of-convex-functions reformulation of the OPF problem, which can be performed effectively. Numerical results are presented comparing the method to state-of-the-art OPF solver implementations in MATPOWER, leading to significant speedups compared to the latter.