Dual Conic Proxies for AC Optimal Power Flow

arXiv:2310.02969v2 Announce Type: replace
Abstract: In recent years, there has been significant interest in the development of machine learning-based optimization proxies for AC Optimal Power Flow (AC-OPF). Although significant progress has been achieved in predicting high-quality primal solutions, no existing learning-based approach can provide valid dual bounds for AC-OPF. This paper addresses this gap by training optimization proxies for a convex relaxation of AC-OPF. Namely, the paper considers a second-order cone (SOC) relaxation of AC-OPF, and proposes \revision{a novel architecture} …

abstract arxiv cs.lg development flow machine machine learning math.oc optimization paper power primal progress proxies quality solutions type