Optimal Scheduling Strategy for Electric Vehicle Charging Based on an Improved CLM-MOPSO Algorithm

Abstract

With the rapid development of the electric vehicle (EV) industry, large-scale integration of EVs into the power grid has led to increasingly prominent problems such as low charging efficiency, intensified load fluctuations, and reduced economic benefits for users. To address these issues, an optimization model is constructed with charging time, load fluctuation, and user charging cost as the objectives, comprehensively considering uncertainties including renewable energy output, user charging behavior, and electricity price fluctuations. An uncertainty-aware multi-objective scheduling strategy based on an improved chaotic Lévy flight multi-objective particle swarm optimization (CLM-MOPSO) algorithm is proposed. Specifically, Weibull and Beta distributions are adopted to generate scenarios for wind and photovoltaic power output, while Poisson and normal distributions are used to characterize the uncertainty of user charging behavior. In addition, a stochastic electricity price process and load uncertainty sets are introduced to establish a robust optimization framework based on multi-scenario stochastic programming. On this basis, an improved CLM-MOPSO algorithm is designed, in which Tent chaotic mapping is utilized for high-quality population initialization, Lévy flight mutation is introduced to enhance the global search capability, and adaptive parameter adjustment together with an external archive mechanism is incorporated to improve the search efficiency while maintaining good convergence and diversity of the Pareto solution set. Finally, simulation studies based on real road network and power grid operation data are conducted, and the results verify the effectiveness of the proposed method. The results demonstrate that the proposed method significantly reduces charging time, mitigates load fluctuations, and lowers user charging costs, while also exhibiting strong robustness and potential for practical engineering applications.