Hybrid Digitized-Counterdiabatic Quantum–Classical Benders Decomposition for EV Charging Navigation in Coupled Transportation and Distribution Network

With the increasing integration of Transportation Network (TN) and Distribution Network (DN), the growing prevalence of electric vehicles (EVs) has posed significant challenges to both TN and DN operations, such as congestion at charging stations (CS) and overload in DN. To address these challenges, this paper proposes a bi-layer coupled model that integrates EV route navigation and CS power scheduling. The upper layer of the model optimizes the lowest-cost routes for multiple EVs to reach CS, while the lower layer optimizes the energy-efficient scheduling of CS to meet EV charging demands. For solving the model in the form of Mixed-Integer Linear Programming (MILP), quantum computing has demonstrated significant potential. Therefore, a hybrid quantum-classical algorithm combining the digitized-counterdiabatic Quantum Approximate Optimization Algorithm (DC-QAOA) with Benders Decomposition (DC-Q-BD) is developed. This algorithm leverages the parallelism of quantum computing and the rapid convergence of digitized-counterdiabatic evolution. It also employs collaborative processing between a classical CPU and a quantum processing unit (QPU) to handle continuous and discrete variables, respectively. Results from practical case studies demonstrate that the proposed algorithm significantly enhances solution efficiency and quality. It provides cost-effective charging routes for EVs and notably reduces the adverse impacts of EV charging on CS. This work provides a quantum-based approach for addressing the EV charging navigation problem in coupled TN and DN.