An optimal deployment scheme for extremely fast charging stations

Electric vehicles (EVs) are important for most countries and regions that rely on imported energy. Unfortunately, slow recharge and improper deployment at charging stations have limited the widespread use of EVs. To improve the recharging speed of charging stations and optimize their deployment, we propose a Minimized optimization Deployment algorithm based on EV Dynamic Changes (MDDC) to optimize the deployment of eXtremely Fast Charging (XFC) stations. The method selects multiple EV distribution maps to simulate EV movement and utilizes a grid partitioning method to divide the deployment area. To reduce the deployment scope of XFC stations, the grids with small and stable EV numbers variance are excluded from service areas. Then, we design the minimum required XFC station optimization function to achieve coverage for all EVs. Three optimization rules are designed to reduce the overlapping coverage of XFC stations, which minimizes the number of XFC stations. Finally, we use an efficient and accurate method named Minimum dIstance Sum of Unique Public area location (MISUP) in MDDC to redetermine the deployment location of XFC stations. We verify the rationality, effectiveness, and robustness of MDDC through extensive simulations, and the results show that the MDDC outperforms the comparison algorithm by 54.2% and 52.0% when using the Euclidean algorithm and the A-star algorithm, respectively.