A Hybrid Sequential Convex Programming Framework for Unbalanced Three-Phase AC OPF
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.
Cite
@article{arxiv.2512.03712,
title = {A Hybrid Sequential Convex Programming Framework for Unbalanced Three-Phase AC OPF},
author = {Sary Yehia and Alessandra Parisio},
journal= {arXiv preprint arXiv:2512.03712},
year = {2025}
}