基于改进NSGA-Ⅱ的电动汽车多目标充电调度优化OA

To address the challenge of simultaneously optimizing temporal efficiency,economic cost,and load smoothness in electric vehicle charging scheduling,a multi-objective charging scheduling optimization model is constructed.Under constraints that ensure vehicle charging demands,charger pile capacity,and to-tal system power limits are satisfied,the model is formulated to minimize the average time cost of vehicles,the average charging cost,and the fluctuation of the total load within the station.To address the limitations of the standard Non-dominated Sorting Genetic Algorithm II(NSGA-Ⅱ)in discrete scheduling prob-lems,such as insufficient search capability and weak adaptivity,an improved algorithm named ALD-NSGA-Ⅱ is proposed.Within the NSGA-Ⅱ framework,the proposed algorithm integrates continuous latent-space search,adaptive parameter adjustment,leader-guided differential evolution,and a local intensifica-tion mechanism to enhance convergence performance and Pareto front quality.Simulation results show that,compared with NSGA-Ⅱ,MODE-RMO,and MOPSO,ALD-NSGA-Ⅱ achieves superior perform-ance in terms of Pareto front distribution,inverted generational distance(IGD),hypervolume,the average IGD convergence curve,and the load scheduling performance of the compromise solution.The results dem-onstrate that the proposed model and the improved algorithm can effectively realize multi-objective opti-mization for electric vehicle charging scheduling,and exhibit promising potential for improving overall scheduling performance and enhancing load smoothing.