Electric Vehicle Orderly Charging Strategy Based on Improved Elite Genetic Algorithm

To mitigate the impact of large-scale EV charging on grid stability, this paper proposes an optimal EV charging strategy based on an improved elite genetic algorithm. Considering users' travel patterns, an optimization model is formulated with the objectives of minimizing EV charging costs and the peak-valley difference ratio of load. The model is subject to constraints of power supply load and EV battery state-of-charge. Simulation results demonstrate that the improved elite genetic algorithm outperforms the conventional one in convergence speed and acceleration. Compared to uncoordinated charging, the proposed strategy reduces the peak-valley difference ratio by 23.87% and charging costs by 38.59%. This approach effectively lessens the adverse effects of EV integration on grid stability and facilitates peak shaving and valley filling.