Distributed quantum generalized benders decomposition for unit commitment problems

Fang Gao, Dejian Huang, Ziwei Zhao, Wei Dai, Mingyu Yang, Qing Gao, Yu Pan

Abstract: Centralized and distributed hybrid quantum–classical generalized Benders decomposition (GBD) algorithms are proposed to address unit commitment (UC) problems. In the centralized approach, the quantum GBD transforms the master problem (MP) into a quadratic unconstrained binary optimization form suitable for quantum computing. For distributed systems, the consensus-inspired quantum GBD (CIQGBD) and its partially distributed variant, D-CIQGBD, are proposed based on optimizing the allocation of relaxation variables directly. D-CIQGBD leverages the dual information of the improved sub-problems to construct more rational local cutting planes, which are then used to decompose the MP into local master problems (LMPs). This approach not only enhances the minimum eigenenergy gap of the system Hamiltonian during quantum annealing and improves the computational efficiency, but also reduces the qubit overhead and addresses the partitioning requirements. Extensive experiments under various UC scenarios validate the performance of the abovementioned hybrid algorithms. Compared to the classical solver Gurobi, D-CIQGBD demonstrates a speed advantage in solving the security-constrained UC problem on the IEEE RTS 24-bus system. These results provide new perspectives on leveraging quantum computing for the distributed optimization of power systems.

External IDs:dblp:journals/qip/GaoHZDYGP25