Evaluate Quantum Combinatorial Optimization for Distribution Network Reconfiguration

TL;DR

This paper develops a new mixed integer conic programming model for distribution network reconfiguration and evaluates the potential of quantum computing algorithms to solve this problem, highlighting current limitations and prospects.

Contribution

It introduces a numerically stable branch flows model for power grid reconfiguration and benchmarks quantum algorithms on real quantum computers for this problem.

Findings

Quantum algorithms show promise with fast execution times.

Current quantum heuristics require careful application in power systems.

The new model improves numerical stability over existing methods.

Abstract

This paper aims to implement and evaluate the performance of quantum computing on solving combinatorial optimization problems arising from the operations of the power grid. To this end, we construct a novel mixed integer conic programming formulation for the reconfiguration of radial distribution network in response to faults in distribution lines. Comparing to existing bus injection model in the literature, our formulation based the branch flows model is theoretically equivalent without needing non-explainable variables, thus being more numerically stable. The network reconfiguration model is then used as a benchmark to evaluate the performance of quantum computing algorithms in real quantum computers. It shows that while current quantum computing algorithms with fast execution time in quantum computers can be a promising solution candidate, its heuristic nature stem from its theoretical foundation should be considered carefully when applying into power grid optimization problems.