A Load Control User Combinatorial Optimization Method Considering Electric Vehicle and Temperature-Controlled Load Clusters

As the construction of the new power system continues to deepen, the power system faces such problems as large peak-to-valley difference and high volatility, and the use of user-side resources to participate in load control is one of the important initiatives to solve the above-said problems. In this paper, a load control user combinatorial optimization method considering electric vehicle (EV) and temperature-controlled load clusters is proposed. Firstly, a hierarchical control method is used to aggregate individual EVs and temperature-controlled load clusters, and the aggregated clusters are divided into peak load shifting type and peak load shedding type according to their willingness to participate in load control types, and their respective user load control models are established. Secondly, a three-stage rebound load model is constructed to solve the load rebound problem after peak load shifting users participate in load control. And then, a load control influence function is established with consideration of the influence degree of users participating in load control. Finally, the composition of user groups participating in peak load shifting and shedding and the adjustment amount of user load are optimized with the minimum load control influence, minimum network loss and minimum load fluctuation as multi-objectives. While meeting the demand of load control, the proposed method can effectively inhibit the new peak load caused by the rebound of load after users participating in load control, as a result, realizing the good interaction of supply and demand between distributed load resources and the power system.