An optimization model for electric vehicle charging infrastructure planning considering queuing behavior with finite queue length. (June 2020) - Xiao, Dan; An, Shi; Cai, Hua; Wang, Jian; Cai, Haiming
Highlights: An optimization model was proposed to deploy the optimal locations and capacities of charging infrastructure under charging queuing behavior with finite queue length. The M / M / S / N queuing model was adopted to restrict finite queue length and finite charging service capacity of each charging station. The quantity of chargers allocated at each station was set as variable. The sensitivity analysis was conducted to illustrate the impact of key parameters on various cost components and optimal results. Abstract: As clean energy vehicles, electric vehicles (EVs) have been paid unprecedented attention in dealing with serious energy crises and heavy tailpipe emission in recent years. Due to its limited battery range and long charging time, it's significant to reasonably determine the locations and capacities of EV charging infrastructure. There are two research gaps in existing researches: unrealistically assuming the infinite queuing length based on the M / M /1 or M / M / S queuing model and lacking the research on variable quantities of chargers allocated at different charging stations. To fill up these gaps, we propose an optimal location model to determine the optimal locations and capacities of EV charging infrastructure to minimize the comprehensive total cost, which considers the charging queuing behavior with finite queue length and various siting constrains. And the results show that (1) the proposed model has a good performance in determining the optimal locations and capacities of EV charging infrastructure (i.e. the optimal locations of charging stations, the optimal quantities of chargers installed at each charging station, the optimal allowable maximum queue length and maximum capacity of each charging station); (2) the quantity of chargers and allowable maximum queue length at each charging station are consistent with the distribution densities of existing charging stations at these locations; (3) the two parameters of unit value of time and unit distance cost have a more significant impact on the total cost. Therefore, the total cost can be effectively reduced by appropriately increasing the quantity of chargers at each charging station and the distribution density of charging stations.