Trade-Off Planning of Electric Vehicle Charging Stations Based on Improved Whale Optimization Algorithm

The surge in electric vehicles poses new challenges to charging stations, and a reasonable layout plan will promote the development of the electric vehicle industry. In the context of charging demand affected by seasonality, daily variations, we propose a method for the layout of electric vehicle charging stations calculated in granularity of hours. Firstly, based on the demand points after Monte Carlo simulation, we propose a dynamic time-varying charging station planning model with the optimization objectives of maximizing the annual revenue and minimizing user losses, under considering distance constraints, power constraints, queuing time constraints. To address this issue, we propose a two-stage search algorithm that combines random search with Whale Optimization Algorithm (RS-WOA) to find optimized solutions at both coarse and fine granularity levels. Finally, numerical simulation experiments were conducted in an example of the Optics Valley region in Wuhan, China. The experimental results show that our proposed algorithm achieved an annualized profit of 17.8399 million, which improved 5.3% and 37.7% compared to the RS algorithm and WOA algorithm, respectively.