Minimal Sparsity for Second-Order Moment-SOS Relaxations of the AC-OPF Problem
Abstract
AC-OPF (Alternative Current Optimal Power Flow)aims at minimizing the operating costs of a power gridunder physical constraints on voltages and power injections.Its mathematical formulation results in a nonconvex polynomial optimizationproblem which is hard to solve in general,but that can be tackled by a sequence of SDP(Semidefinite Programming) relaxationscorresponding to the steps of the moment-SOS (Sums-Of-Squares) hierarchy.Unfortunately, the size of these SDPs grows drastically in the hierarchy,so that even second-order relaxationsexploiting the correlative sparsity pattern of AC-OPFare hardly numerically tractable for largeinstances -- with thousands of power buses.Our contribution lies in a new sparsityframework, termed minimal sparsity, inspiredfrom the specific structure of power flowequations.Despite its heuristic nature, numerical examples show that minimal sparsity allows the computation ofhighly accurate second-order moment-SOS relaxationsof AC-OPF, while requiring far less computing time and memory resources than the standard correlative sparsity pattern. Thus, we manage to compute second-order relaxations on test caseswith about 6000 power buses, which we believe to be unprecedented.
Keywords
Cite
@article{arxiv.2305.19232,
title = {Minimal Sparsity for Second-Order Moment-SOS Relaxations of the AC-OPF Problem},
author = {Adrien Le Franc and Victor Magron and Jean-Bernard Lasserre and Manuel Ruiz and Patrick Panciatici},
journal= {arXiv preprint arXiv:2305.19232},
year = {2023}
}