Abstract:

This research presents an Improved Whale Optimization Algorithm (IWOA) to solve the problem of poor accuracy and stability in optimizing the locating and sizing of nonconvex and nonlinear electric vehicle (EV) charging stations (CSs). The variability index of the convergence factor, the differential evolution operator, and the antibody affinity are introduced in the algorithm framework. Twelve classical test functions show that IWOA significantly improves the algorithm accuracy and convergence speed compared to WOA. Finally, the Voronoi diagram based on Floyd's shortest path is adopted to decide the service area of charging stations. IWOA is applied to a 45-node transportation network for case study analysis to deliver cost optimization. The results show that the proposed model and algorithm can be effectively applied to locating and sizing and help reduce the cost for the whole society.