A bi-level optimization model for electric vehicle charging strategy based on regional grid load following. (20th November 2021)

• Record Type: Journal Article
• Authors: Yang, Xiaolong; Niu, Dongxiao; Sun, Lijie; Ji, Zhengsen; Zhou, Jiancheng; Wang, Keke; Siqin, Zhuoya
• Is Part Of: Journal of cleaner production. Volume 325(2021)
• Publication Date: 2021-11-20
• Subjects: Electric vehicle -- Real-time charging price -- Demand response -- Optimal charging strategy -- Peak load regulation; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2021.129313
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Because a greater proportion of large-scale electric vehicles (EVs) are connected to the grid, their stochastic charging load has a significant impact on the power quality and economic operation of the distribution network. However, a random charging load can be transformed into flexible demand response resources through intelligent control of the EV charging process. Therefore, a bi-level optimization model for EV charging is proposed in this study based on the real-time charging price according to the regional grid load, providing more flexible charging strategies for EVs. First, the framework for a bi-level optimization control strategy for EV charging based on the load is developed. Based on the regional grid load, the charging price of each period is optimized in accordance with the load elasticity coefficient of the real-time charging price. Second, an upper-level EV charging price optimization model and a lower-level EV charging load optimization model are established. Third, an optimal control strategy for the scheduling of EV charging is developed, which considers the charging cost of EV users as the objective function and the standard deviation and peak-valley difference of the regional grid load as validation functions. Finally, using a regional power grid in China as the simulation object, the daily operation data of the grid are used to analyze and verify the proposed model. The simulation results demonstrate that the optimized real-time charging price can better respond to the regional grid load, smooth the regional grid load curve, reduce the peak-valley difference, promote clean energy consumption, and further lower the charging cost for EV users. Highlights: A framework of the bi-level optimization for electric vehicle charging is proposed. Load elasticity coefficient of real-time charging price is introduced. Real-time charging price model following the load of the power grid is constructed. Impacts of real-time charging price change on electric vehicle load are analyzed. Flexible charging strategies are provided for electric vehicle users.