Allocation of Charging Stations for Electric Vehicles Considering Spatial Difference in Urban Land Price and Fixed Budget

Abstract

In this paper, the allocation of charging station (CS) is optimized to alleviate the “range anxiety” of electric vehicle (EV) drivers by reducing the time of medium-to-long distance travel, which is raised due to the potential en-route charging. The problem is defined to explicitly consider the spatial differences in urban land price. Although many works take spatial land price into consideration, few of them notice what the gap of spatial land price bring to the charging system. Our objective function is the expected traveling time under an optimized distribution of urban EV flows, and models of spatial network and CSs allocation are then established. Based on Tabu Search algorithm (TSA), a fixed budget charging resources planning algorithm (FBCRPA) is proposed. The proposed method is compared with methods based on betweeness centrality, and results show that our method can find more effective allocation strategy. It is found that users’ traveling time would decrease with increase in difference in land price. Meanwhile, budget would transfer from central region to other regions and carrying capacity of charging system would improve in the above situation. This paper also finds that increase in budget is beneficial to a reduction in drivers’ time, but the improvement is limited.