A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System

Yan Bao, Yu Luo, Weige Zhang, Mei Huang, Le Yi Wang and Jiuchun Jiang
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Yan Bao: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Yu Luo: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Weige Zhang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Mei Huang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Le Yi Wang: Department of Electrical and Computer Engineering, Wayne State University, Detroit, MI 48202, USA
Jiuchun Jiang: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China

Energies, 2018, vol. 11, issue 1, 1-21

Abstract: Fast charging stations enable the high-powered rapid recharging of electric vehicles. However, these stations also face challenges due to power fluctuations, high peak loads, and low load factors, affecting the reliable and economic operation of charging stations and distribution networks. This paper introduces a battery energy storage system (BESS) for charging load control, which is a more user-friendly approach and is more robust to perturbations. With the goals of peak-shaving, total electricity cost reduction, and minimization of variation in the state-of-charge (SOC) range, a BESS-based bi-level optimization strategy for the charging load regulation of fast charging stations is proposed in this paper. At the first level, a day-ahead optimization strategy generates the optimal planned load curve and the deviation band to be used as a reference for ensuring multiple control objectives through linear programming, and even for avoiding control failure caused by insufficient BESS energy. Based on this day-ahead optimal plan, at a second level, real-time rolling optimization converts the control process to a multistage decision-making problem. The predictive control-based real-time rolling optimization strategy in the proposed model was used to achieve the above control objectives and maintain battery life. Finally, through a horizontal comparison of two control approaches in each case study, and a longitudinal comparison of the control robustness against different degrees of load disturbances in three cases, the results indicated that the proposed control strategy was able to significantly improve the charging load characteristics, even with large disturbances. Meanwhile, the proposed approach ensures the least amount of variation in the range of battery SOC and reduces the total electricity cost, which will be of a considerable benefit to station operators.

Keywords: fast charging station; battery energy storage system; real time; rolling optimization; linear programming; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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