A Hybrid Sequential Convex Programming Framework for Unbalanced Three-Phase AC OPF

TL;DR

This paper introduces a hybrid Sequential Convex Programming framework for unbalanced three-phase AC OPF that guarantees convergence, maintains feasibility, and demonstrates high accuracy and efficiency in large-scale distribution networks.

Contribution

It develops a novel SCP method combining McCormick approximations, linearizations, and adaptive trust regions for improved convergence and accuracy in unbalanced AC OPF problems.

Findings

Achieves optimality gap below 0.1% in case studies.

Up to 2x faster runtimes than IPOPT.

Demonstrates high accuracy and efficiency in large-scale networks.

Abstract

This paper presents a hybrid Sequential Convex Programming (SCP) framework for solving the unbalanced three-phase AC Optimal Power Flow (OPF) problem. The method combines a fixed McCormick outer approximation of bilinear voltage-current terms, first-order Taylor linearisations, and an adaptive trust-region constraint to preserve feasibility and promote convergence. The resulting formulation remains convex at each iteration and ensures convergence to a stationary point that satisfies the first-order Karush-Kuhn-Tucker (KKT) conditions of the nonlinear OPF. Case studies on standard IEEE feeders and a real low-voltage (LV) network in Cyprus demonstrate high numerical accuracy with optimality gap below 0.1% and up to 2x faster runtimes compared to IPOPT. These results confirm that the method is accurate and computationally efficient for large-scale unbalanced distribution networks.