In the airport area, the charging behavior of new energy special vehicles has great randomness, and the charging conditions of different types of special vehicles are different, which results in a large difference in the utilization rate of each charging pile in the airport area, affecting the healthy operation of electric distribution network of the airport. In response to the above phenomenon, this paper designed a two-stage collaborative charging scheduling strategy for eleven types of special vehicles. In the first stage, this paper analyzed the service process of different vehicles for flights, with the goal of minimizing the difference value in the start time of service for adjacent flights by the same vehicle, and generated a sequence of vehicles with charging needs. In the second stage, the strategy aimed to reduce the variance of time utilization of charging piles and vehicle charging queue time in each zone of the airport area. Based on sequence of vehicles in the previous stage, this paper used an improved adaptive mutation particle swarm optimization algorithm to solve this model, and validated the strategy by comparing it with actual vehicle charging data of a domestic hub airport. The experiment shows that this algorithm can achieve a 93.5% reduction in the waiting time for vehicle charging, and it reduces the overall variance of time utilization of charging piles in the airport area by 88.7%. Finally, this strategy achieves the goal of balanced use of charging piles.

[2024-01-31] Accepted Paper /doc/2023.12.0566/v1