Research on ordered charging of battery swapping station based on adaptive genetic algorithm

Large-scale electric vehicles (EVs) will have a great impact on the distribution electric network after being disorderly connected to it. If it is coordinated and controlled to transform it into a flexible load of the grid, it can play the role of cutting peaks and filling the valley. This paper, for the battery swapping mode, the objective function is to Minimize peak-to-valley difference of the load, and the redundant model of the battery swapping station with the ordered charging method is established. The Monte Carlo method and the Adaptive Genetic Algorithm (AGA) are used to solve and optimize the EVs charging load. The simulation results shows that the ordered charging mode can suppress load fluctuation, reduce peak-to-valley difference, and increase the redundancy.