Accurate Semidefinite Programming Models for Optimal Power Flow in Distribution Systems

TL;DR

This paper introduces improved semidefinite programming models for optimal power flow in distribution systems, enhancing accuracy and stability over existing methods through novel symmetrical and voltage regulation models.

Contribution

It presents two new SDP models, including a symmetrical model and a voltage regulation model, that improve accuracy and numerical stability in solving OPF problems.

Findings

SDP models outperform existing approaches in accuracy

Models demonstrate better numerical stability

Case studies validate effectiveness with benchmark comparisons

Abstract

In this paper, we develop semidefinite programming (SDP) models aimed at solving optimal power flow (OPF) problems in distribution systems. We propose two models: the symmetrical SDP model which modifies the existing BFM-SDP model. Then based on the symmetrical SDP model, we develop a voltage regulation model that solves OPF problems with binding voltage constraints. To evaluate the accuracy of our proposed OPF models, we rely on OpenDSS, a power flow solver, to generate power flow solutions as the benchmarks. Comprehensive case studies are conducted showing our SDP models have better numerical stability and yield more accurate results than existing approaches