Site selection, capacity determination, and reconstruction optimization strategy for electric vehicle charging/swapping stations
Summary: In recent years, influenced by factors such as policy support, increased environmental awareness, and continuous advancements in electric vehicle technology, the number of electric vehicles has experienced rapid growth.However, when people choose electric vehicles, they often encounter range anxiety, primarily worrying about the remaining driving range being insufficient to reach their destination, thus risking being stranded along the way.Additionally, there is a growing demand for public charging/swapping infrastructure, with users hoping for the same convenience in accessing charging or swapping services throughout the city as they do when refueling at traditional gas stations.Simultaneously, there is a noticeable trend of traditional fuel cars gradually phasing out of urban transportation systems, potentially leading to the gradual elimination of accompanying gas stations.Furthermore, existing parking lots should not only serve the purpose of parking vehicles.In existing parking lots, installing charging piles can transform them into electric vehicle charging stations, effectively catering to both charging and parking needs.To efficiently utilize existing resources while enhancing user acceptance of electric vehicles, a strategy of reconstructing existing facilities to establish public charging/swapping stations is proposed.This involves using existing gas stations and parking lots as candidate sites for constructing electric vehicle charging/swapping stations.This study utilizes an M/M/a queuing system to construct a mathematical model for the location-capacity of charging/swapping stations, with the optimization goal of minimizing the total construction cost of swapping stations, the total construction cost of charging stations, the total penalty cost for unmet demand, and the total cost of user queue time.However, since the site selection model for charging stations is an NP-hard problem, exact algorithms' computational time would be excessively long as the model scale expands.Therefore, this study employs genetic algorithms for simulation and solution, integrating demand allocation and station capacity determination methods into the algorithm process.A case study is conducted in the Wuchang District of Wuhan City, Hubei Province.All demand points are aggregated into a single point, representing the estimated demand generated within each community in Wuchang District.Twenty gas stations and fifty-eight parking lots in Wuchang District are selected as candidate sites for constructing swapping and charging stations, respectively.Additionally, one hundred and thirty-three communities within Wuchang District are chosen as demand points, and the daily demand is estimated based on population data.The genetic algorithm is then employed to determine the optimal location-capacity plan.Finally, sensitivity analysis is conducted on unit penalty cost, charging pile power, coverage range, and unit construction cost of swapping stations.The results indicate that prioritizing user satisfaction leads to increased costs and broader demand coverage within the location-capacity plan.Additionally, larger power charging piles are not necessarily better within public charging stations, as economic efficiency and applicability should be considered.Coverage range is found to be a highly sensitive factor in the site selection problem of electric vehicle charging/swapping stations under the background of reconstructing existing facilities.Moreover, the government can encourage companies to build swapping stations through subsidies, reducing the high costs associated with their construction and promoting the popularization of swapping modes to meet the continuously growing demand for swapping among users.
electric vehiclesite selection and capacity determination modelgenetic algorithmreconstructioncharging/swapping station
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