Sensorless sequential charging guidance and control for multiple types of electric vehicles with ordered piles based on bi-objective hierarchical optimization

In response to the difficulty in comprehensively analyzing the charging demand of multiple types of electric vehicles when orderly pile charging is ineffective, and the challenge in balancing the dual objectives of grid load variance and user charging costs, this study proposes a novel guidance and control method for non-sensitive charging of ordered piles and for charging multiple types of electric vehicles based on dual-objective hierarchical optimization. The Monte Carlo method is employed to accurately calculate the charging load requirements of various types of electric vehicles, providing a robust foundation for subsequent charging guidance and control. Moreover, this study integrates the variance of power grid load and user charging costs into a unified optimization framework and develops a dual-objective hierarchical optimization model for charging guidance and control, achieving an effective balance between the two objectives. To address this complex problem, an improved genetic algorithm was implemented, which effectively determines the distribution scheme of charging stations, charging times, charging power, and other parameters, thereby enhancing the efficiency of the solution. The experimental results demonstrate that this method can effectively guide users of multiple types of electric vehicles to charge during low load periods, minimizing the variance of grid load while maintaining user satisfaction with the charging cost and method. This illustrates the effectiveness and superiority of the approach in practical applications.