Participate in the multi-source energy supply cooperative control algorithm of electric vehicles under load response optimization

The charging behavior of electric vehicles is highly uncertain due to factors such as user habits and driving modes, making load forecasting difficult. It is necessary to improve the prediction accuracy through big data analysis and behavior modeling. To this end, a multi-source energy supply cooperative control algorithm for electric vehicles participating in load response optimization is designed. When the electric vehicle is parked, based on the frequency signal of the power system, the dynamic switching of three control modes, namely normal mode, V1G mode, and V2G mode, is realized. Given the uncertain characteristics of the grid connection time and charging demand of the electric vehicle, an orderly charging plan for each vehicle is formulated. Regarding the braking energy feedback process of the electric vehicle, according to the state of the supercapacitor and the magnitude of the feedback energy, different strategies for the supercapacitor and the battery to absorb braking energy were formulated. To minimize the peak-valley difference, the peak-valley period optimization model was established and solved by using the genetic algorithm. The experimental results show that the load of the proposed algorithm reduces the power output by 1.345 MW at 5:00. Currently, the purpose of power scheduling is achieved by activating the load. The output voltage fluctuation is small and the response speed is fast. The valley value is 655.24 KW and the peak-valley difference is 268.92 KW, which can optimize the charging mode of electric vehicles more effectively.