混合量子-经典算法的配电网灾后拓扑重构方法

To promptly and effectively devise topological reconfiguration strategies for distribution networks to boost rapid load recovery capabilities, an elastic post-disaster topological reconfiguration method for distribution networks using a hybrid quantum-classical (HQC) algorithm is introduced, with a specific focus on the advantages of quantum computing. Firstly, a post-disaster topology reconfiguration model for distribution network based on HQC algorithm is established to facilitate interactive processes among real-world scenarios, optimization problems, and embedded algorithm modules in both quantum and classical computing environments. Then, the topological reconfiguration problem for distribution networks is structured into discrete unconstrained optimization sub-problems and continuous constrained optimization sub-problems. A quantum annealing-embedded alternating direction method of multipliers (QA-ADMM) algorithm is proposed, which maps diserete sub-problems into quantum-interpretable Ising models. This algorithm is implemented on the D-Wave quantum annealing computer and iteratively solved on classical computers for continuous sub-problems. An adaptive penalty factor adjustment mechanism is utilized to hasten algorithm convergence. Through analyses of various distribution systems, including IEEE 14, 33. 69, 123 and an enhanced 205-node distribution system, the effectiveness, stability, and scalability of the QA-ADMM algorithm are validated. The findings suggest that penalty factors, penalty term coefficients, and quantum annealing sampling read times influence the accuracy and convergence speed of the algorithm. The computational benefits of the hybrid quantum-classical algorithm become more pronounced with larger optimization problem scales. In the case of a 205-node distribution system, computational efficiency using the hybrid approach can be boosted by around 34.% compared to classical computing.