Satisfaction aware orderly charging strategy for electric vehicles using particle swarm optimization

Abstract

To mitigate grid load fluctuations caused by large-scale electric vehicle (EV) charging while preserving user experience, this paper proposes a satisfaction-aware orderly charging coordination framework. First, based on Kano theory, a comprehensive fitness evaluation model is developed to characterize diverse user charging demands, integrating the grid load peak-to-valley difference, total charging cost, and user satisfaction into a uni-fied scheduling objective. To efficiently solve this complex high-dimensional problem, an improved particle swarm optimization (PSO) approach is employed. By incorporating adaptive inertia weights, asynchronous learning factors, and Lévy flight perturbation, the proposed algorithm enhances global search capability and alleviates premature conver-gence. Simulation results based on the IEEE 33-node distribution system demonstrate the effectiveness of the proposed strategy. Compared with disorderly charging, the proposed method significantly reduces the grid peak-to-valley difference and total charging cost. Meanwhile, the comprehensive user satisfaction is improved and consistently outperforms the conventional PSO method. These results indicate that the proposed strategy effectively balances grid operation and user benefits.