Real-Time Scheduling Strategy of Electric Vehicle Load Aggregation Quotient Based on Incentive and Price of Demand Response

Abstract

With the increase in the number of electric vehicles (EVs) connected to the power grid and the continuous increase in battery capacity, the disorderly charging of EVs will bring enormous pressure to the power grid, and even affect the operation of the power grid. Combining incentive-based and price-based demand response measures, this paper proposes two load poly and commercial (LPC) scheduling strategies for EVs: incentive-based electricity pricing and dynamic electricity pricing. First, according to different scheduling strategies, optimization is carried out for different EV groups. Second, the demand response measures are used as the dispatching channel between the load aggregator and the EV, and the load aggregator scheduling model is established with the goal of maximizing the net income of the load aggregator and balancing the load fluctuation. Simulation and verification are carried out through a numerical example, and the results show that compared with the traditional disordered charging mode, the scheduling strategy proposed in this paper can effectively improve the net income of the load aggregator while reducing the load fluctuation.