Combinatorial Optimization of AC Optimal Power Flow with Discrete Demands in Radial Networks

TL;DR

This paper introduces a polynomial-time approximation scheme for solving the AC optimal power flow problem with discrete demands in radial networks, providing near-optimal solutions efficiently.

Contribution

It presents the first PTAS for convex-relaxed OPF with discrete demands in radial networks, combining theoretical guarantees with practical efficiency.

Findings

The PTAS achieves a provably small approximation ratio.

The algorithm produces solutions close to optimal in simulations.

It is among the best possible in theory given prior hardness results.

Abstract

The AC Optimal power flow (OPF) problem is one of the most fundamental problems in power systems engineering. For the past decades, researchers have been relying on unproven heuristics to tackle OPF. The hardness of OPF stems from two issues: (1) non-convexity and (2) combinatoric constraints (e.g., discrete power extraction constraints). The recent advances in providing sufficient conditions on the exactness of convex relaxation of OPF can address the issue of non-convexity. To complete the understanding of OPF, this paper presents a polynomial-time approximation algorithm to solve the convex-relaxed OPF with discrete demands as combinatoric constraints, which has a provably small parameterized approximation ratio (also known as PTAS algorithm). Together with the sufficient conditions on the exactness of the convex relaxation, we provide an efficient approximation algorithm to solve OPF with discrete demands, when the underlying network is radial with a fixed size and one feeder. The running time of PTAS is $O(n^{4m/\epsilon}T)$, where $T$ is the time required to solve a convex relaxation of the problem, and $m, \epsilon$ are fixed constants. Based on prior hardness results of OPF, our PTAS is among the best achievable in theory. Simulations show our algorithm can produce close-to-optimal solutions in practice.