Optimal Scheduling Strategy for Multi-Type Electric Vehicle Charging Based on Improved GA

The use of electric vehicles in transportation has grown dramatically against the backdrop of developments such as global warming and energyconstraints. Electric vehicles have the advantages of being environmentally friendly, energy efficient and can intelligently interact with the grid through V2G technology. However, centralized charging of EVs during peak hours will widen the peak-to-valley difference in grid loads, and their widespread adoption will bring challenges to grid load management. In addition, electric vehicle users face problems such as charging costs and charging time. In this study, electric vehicles are categorized into three types and charging load probability prediction models are established for different types of electric vehicles. On this basis, an optimal scheduling scheme for electric vehicles under multiple objectives, such as minimizing peak-to-valley difference in grid load, minimizing charging cost for vehicle owners, and satisfying requirements for electric vehicle charging, is proposed based on GA. The results show that the peak-to-valley load difference rate of the distribution network after optimization using GA is 58.9%, which is 25.7% less than the 84.6% of disorderly charging. Electric vehicle charging times are shifted toward the load trough, reducing the cost of charging for electric vehicle owners. The results of the study can provide a reference for the cooperative and optimal scheduling of electric vehicles and distribution networks.