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Integrating quantum and classical computing for multi-energy system optimization using Benders decomposition by Leenders, Ludger; Sollich, Martin; Reinert, Christiane; Bardow, André is a scholarly article available to read on EtoBox.

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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 pr

Author

Leenders, Ludger; Sollich, Martin; Reinert, Christiane; Bardow, André

Published

2023

Language

EN