Integrating quantum and classical computing for multi-energy system optimization using Benders decomposition

TL;DR

This paper proposes a hybrid quantum-classical Benders decomposition method for multi-energy system optimization, demonstrating proof of concept but noting current computational limitations and future potential with larger quantum computers.

Contribution

It introduces a novel hybrid approach combining quantum and classical computing for multi-energy system optimization using Benders decomposition.

Findings

Proof of concept for hybrid quantum-classical optimization

Current computational time is higher than classical methods

Potential improvements with larger, fault-tolerant quantum computers

Abstract

During recent years, quantum computers have received increasing attention, primarily due to their ability to significantly increase computational performance for specific problems. Computational performance could be improved for mathematical optimization by quantum annealers. This special type of quantum computer can solve quadratic unconstrained binary optimization problems. However, multi-energy systems optimization commonly involves integer and continuous decision variables. Due to their mixed-integer problem structure, quantum annealers cannot be directly used for multi-energy system optimization. To solve multi-energy system optimization problems, we present a hybrid Benders decomposition approach combining optimization on quantum and classical computers. In our approach, the quantum computer solves the master problem, which involves only the integer variables from the original energy system optimization problem. The subproblem includes the continuous variables and is solved by a classical computer. For better performance, we apply improvement techniques to the Benders decomposition. We test the approach on a case study to design a cost-optimal multi-energy system. While we provide a proof of concept that our Benders decomposition approach is applicable for the design of multi-energy systems, the computational time is still higher than for approaches using classical computers only. We therefore estimate the potential improvement of our approach to be expected for larger and fault-tolerant quantum computers.