Beyond Relaxation and Newton-Raphson: Solving AC OPF for Multi-phase Systems with Renewables

TL;DR

This paper introduces a novel approach using Feasible Point Pursuit - Successive Convex Approximation to solve complex AC OPF problems in multi-phase systems with renewables, addressing limitations of existing relaxation methods.

Contribution

It applies a powerful nonconvex optimization algorithm to find feasible and optimal solutions for AC OPF in unbalanced multi-phase systems with renewables, surpassing previous relaxation-based methods.

Findings

Effective in unbalanced multi-phase systems with renewables

Outperforms traditional relaxation techniques in feasibility and optimality

Validated on distribution and transmission network models

Abstract

This paper focuses on the AC Optimal Power Flow (OPF) problem for multi-phase systems. Particular emphasis is given to systems with high integration of renewables, where adjustments of the real and reactive output powers from renewable sources of energy are necessary in order to enforce voltage regulation. The AC OPF problem is known to be nonconvex (and, in fact, NP-hard). Convex relaxation techniques have been recently explored to solve the OPF task with reduced computational burden; however, sufficient conditions for tightness of these relaxations are only available for restricted classes of system topologies and problem setups. Identifying feasible power-flow solutions remains hard in more general problem formulations, especially in unbalanced multi-phase systems with renewables. To identify feasible and optimal AC OPF solutions in challenging scenarios where existing methods may fail, this paper leverages the Feasible Point Pursuit - Successive Convex Approximation algorithm -- a powerful approach for general nonconvex quadratically constrained quadratic programs. The merits of the approach are illustrated using single- and multi-phase distribution networks with renewables, as well as several transmission systems.