Locating electric vehicle charging stations with service capacity using the improved whale optimization algorithm. (August 2019) - Zhang, Hao; Tang, Lei; Yang, Chen; Lan, Shulin
Highlights: This paper presents an improved whale optimization algorithm (IWOA). On the basis of the whale optimization algorithm (WOA), this paper improves the algorithm by introducing a Gaussian operator, and meanwhile, mixing the differential evolution algorithm and the idea of crowding factor in the rear-end behavior of artificial fish swarm algorithm. It can be seen in the testing result that the IWOA significantly improve precision and computing speed of WOA. In the base of the traditional study about EVs charging station siting, this paper takes service capacity factors into consideration, building charging stations with proper service capacity. In this model charging stations can be established with proper service capacity according to the local requirements, to reduce the variable costs and operating costs of setting charging stations in overall network. This model considers secondary transport, namely power plant to charging station, and charging station to user's power transportation; the power transmission loss between the power plant and the charging station is taken into account. The applicability of this model has great significance for selecting the station location of EV charging station, making the charging station's location to be more reasonable and conform to the reality. Abstract: This study proposes an Improved Whale Optimization Algorithm (IWOA) that, on the basis of Whale Optimization Algorithm (WOA) designed by Mirjalili and Lewis (2016), introduces Gaussian mutation operator, differential evolution operator, and crowding degree factor to the algorithm framework. Test results with nine classic examples show that IWOA significantly improves WOA's precision and computing speed. We also model the locating problem of Electric Vehicle (EV) charging stations with service risk constraints and apply IWOA to solve it. This paper introduces service risk factors, which include the risk of service capacity and user anxiety, establishing the EV charging station site selection model considering service risk. Computational results based on a large-scale problem instance suggest that both the model and the algorithm are effective to apply in practical locating planning projects and help reduce social costs.