Empc-based V2G scheduling strategy for multi-attribute EVs aggregator - Haoyang Tang; Zhilu Liu; Lin Zheng; Jianfeng Zheng; Hao Hu; Jinpei Lu; Zhijian Hu (International Journal of Electrical Power & Energy Systems)
Vehicle to grid (V2G) technology can mitigate uncertainty in distribution network caused by distributed energy resources. The electric vehicle aggregator (EVA) participating in V2G systems includes diverse electric vehicle (EV) types. Consequently, it is imperative to address the real-time aggregation and control of EVs with differing attributes. This paper proposes a V2G strategy for EVA encompassing charging and swapping stations with multiple attributes of EVs. First, a multi-attribute EVs aggregation model is established based on Markov chains. The batteries in the battery swapping station are considered in this model Second, an economic model predictive control (EMPC) algorithm is proposed to solve the aggregation model. The objective is to minimize the total cost of EVA, which consists of two components: the operating cost and the charging cost. To address potential optimal control issues in the EMPC method, an auxiliary function is developed to determine the optimal control sequence. Third, to verify the effectiveness of the proposed method, based on real distribution network data, the proposed EMPC method is compared with the distribution network load demand under dual-layer MPC, real-time pricing, and disordered charging strategies. The optimization effect under different levels of user participation is also evaluated. The results show that compared with other strategies, the proposed EMPC algorithm can achieve 4–47.4 % reduction in charging costs, significantly reduce the peak valley difference and variance of load, and improve the load curve.