Study on Electric Vehicle Two-stage Charging and Discharging Optimization Strategy Considering Demand Response

In order to alleviate the power supply pressure of the grid and solve the problem of peaks and valleys overlap caused by charging electric vehicles (EV) under TOU, a two-stage dynamic optimization method is proposed. Considering the total load of the grid in each dispatching period, the constraints of the grid, owners, batteries and other parties are considered. The real-time scheduling model of electric vehicle cluster based on V2G mode is established. According to the base load and the sum of real-time virtual power of EV in the current grid, the charging and discharging parameters are modified. By establishing an objective function that takes into account the mean square error of minimum charge load, comprehensive charge cost and off-grid battery power, the adaptive particle swarm optimization algorithm is used to optimize EV charging and discharging behavior, adjust the proportion of three parameters in the objective function and the proportion of user demand response, and formulate an optimal scheduling mode for EV in the current power grid. The simulation results show that compared with the traditional charging strategy, the optimization strategy can significantly reduce the charging cost of users, alleviate load fluctuations, and reduce the peak-valley difference and mean square error of load curve.