An adaptive NSGA-Ⅱ for electric vehicle routing problem with charging/discharging based on time-of-use electricity pricing and diverse charging stations

Abstract

Current research on the electric vehicle routing problem (EVRP) predominantly focuses on customer characteristics or the diversity of charging mechanisms, while relatively insufficient attention is paid to the influence of energy interactions facilitated by vehicle-to-grid (V2G) technology on route planning. This study presents a novel approach to EVRP with charging/discharging based on time-of-use (TOU) electricity pricing and diverse charging stations. The proposed method enables electric vehicles to select charging stations for charging or discharging en route, depending on electricity price fluctuations, thus offering opportunities for cost reduction and profit enhancement in logistics distribution. A tailored adaptive non-dominated sorting genetic algorithm-Ⅱ (ANSGA-Ⅱ) is developed to address the problem, which integrates adaptive probability calculation, hybrid population generation, and neighborhood search operators. Testing on benchmark instances demonstrates that the proposed ANSGA-Ⅱ effectively addresses the problem, exhibiting strong convergence. The optimized routing allows vehicles to efficiently engage in vehicle-grid interactions, incentivized by TOU pricing, yielding significant profits for logistics companies, amounting to approximately 20.82 % of total logistics costs. This approach provides a new strategic avenue for optimizing logistics operations. Ultimately, sensitivity analysis elucidates the correlation among TOU electricity pricing, logistics costs, and discharging profits.