Considering the Joint Site Selection of Electric Logistics Vehicle Charging and Swapping Stations at Three Efficiency Levels

The growing penetration of electric logistics vehicles (ELVs) poses a significant challenge to electric utility site selection. This paper addresses the problem of joint site selection for electric logistics vehicle charging and swapping stations (CSSs). First, a joint site selection model is introduced to characterize the problem, and an improved genetic algorithm (IGA) is designed to solve this model. Derived from the standard genetic algorithm (SGA), the IGA incorporates local search operations, evolutionary inversion operations, and an elitist preservation strategy to enhance performance. On this basis, small-scale numerical simulations are conducted to determine the optimal parameters, thereby guaranteeing optimal algorithmic efficiency. Subsequently, large-scale numerical simulations are performed, with key indicators recorded including the optimal routing length, battery replenishment frequency, number of stations, number of ELVs, and solution time. Finally, analysis across three efficiency levels demonstrates that joint siting improves distribution efficiency by 39.38%, increases grid electricity sales by 46.89%, and reduces total transportation costs by 26.28%, with the optimization scheme validated across six different numerical scenarios. Overall, the joint site selection proposed in this paper has enhanced the benefits of relevant stakeholders and provided a reference for building a low-carbon transportation chain.