Reliability and Optimization of Electric Vehicle Photovoltaic Charging Scheduling Based on Stochastic Production Simulation

Abstract

To solve the problems of low resource utilization and poor grid stability in current photovoltaic electric vehicle charging, this study uses the K-means clustering algorithm to cluster data in a Monte Carlo random production simulation. This reduces subsequent solving time and allows for the construction of an optimization scheduling model for photovoltaic electric vehicle charging. Afterwards, the particle swarm optimization algorithm with strong global search ability and fast convergence speed is used to solve the model and obtain the optimal scheduling strategy. The study analyzed the random simulation method and found that the simulation accuracy was higher than 96% in different scenarios. When the particle swarm algorithm was used to solve the optimization scheduling model, its solving time during peak hours, daytime dispersion time, and nighttime trough time was 2.4 s, 1.9 s, and 1.4 s, respectively. The calculation time was less than 2.5 s, and the calculation errors in the three scenarios were 2.3%, 1.4%, and 1.2%, respectively, indicating a relatively low calculation error. After optimizing the scheduling strategy, the resource utilization rates of electric vehicle photovoltaic charging in three scenarios were 98.6%, 97.4%, and 97.3%, respectively, which can improve the power supply reliability and equipment reliability of the photovoltaic system during electric vehicle charging. From the above results, it can be concluded that the proposed photovoltaic charging optimization scheduling model for electric vehicles based on stochastic production simulation can improve the resource utilization efficiency and charging reliability during charging.