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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Yan Bao & Yu Luo & Weige Zhang & Mei Huang & Le Yi Wang & Jiuchun Jiang, 2018. "A Bi-Level Optimization Approach to Charging Load Regulation of Electric Vehicle Fast Charging Stations Based on a Battery Energy Storage System," Energies, MDPI, vol. 11(1), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:229-:d:127561
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    References listed on IDEAS

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    1. Jinil Han & Jongyoon Park & Kyungsik Lee, 2017. "Optimal Scheduling for Electric Vehicle Charging under Variable Maximum Charging Power," Energies, MDPI, vol. 10(7), pages 1-15, July.
    2. Madina, Carlos & Zamora, Inmaculada & Zabala, Eduardo, 2016. "Methodology for assessing electric vehicle charging infrastructure business models," Energy Policy, Elsevier, vol. 89(C), pages 284-293.
    3. Xu, Zhiwei & Hu, Zechun & Song, Yonghua & Zhao, Wei & Zhang, Yongwang, 2014. "Coordination of PEVs charging across multiple aggregators," Applied Energy, Elsevier, vol. 136(C), pages 582-589.
    4. He, Lifu & Yang, Jun & Yan, Jun & Tang, Yufei & He, Haibo, 2016. "A bi-layer optimization based temporal and spatial scheduling for large-scale electric vehicles," Applied Energy, Elsevier, vol. 168(C), pages 179-192.
    5. Weige Zhang & Di Zhang & Biqiang Mu & Le Yi Wang & Yan Bao & Jiuchun Jiang & Hugo Morais, 2017. "Decentralized Electric Vehicle Charging Strategies for Reduced Load Variation and Guaranteed Charge Completion in Regional Distribution Grids," Energies, MDPI, vol. 10(2), pages 1-19, January.
    6. Xiong, Rui & Yu, Quanqing & Wang, Le Yi & Lin, Cheng, 2017. "A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H infinity filter," Applied Energy, Elsevier, vol. 207(C), pages 346-353.
    7. Serradilla, Javier & Wardle, Josey & Blythe, Phil & Gibbon, Jane, 2017. "An evidence-based approach for investment in rapid-charging infrastructure," Energy Policy, Elsevier, vol. 106(C), pages 514-524.
    8. Rui Xiong & Hongwen He & Fengchun Sun & Kai Zhao, 2012. "Online Estimation of Peak Power Capability of Li-Ion Batteries in Electric Vehicles by a Hardware-in-Loop Approach," Energies, MDPI, vol. 5(5), pages 1-15, May.
    9. Morrissey, Patrick & Weldon, Peter & O’Mahony, Margaret, 2016. "Future standard and fast charging infrastructure planning: An analysis of electric vehicle charging behaviour," Energy Policy, Elsevier, vol. 89(C), pages 257-270.
    10. Xiong, Rui & Tian, Jinpeng & Mu, Hao & Wang, Chun, 2017. "A systematic model-based degradation behavior recognition and health monitoring method for lithium-ion batteries," Applied Energy, Elsevier, vol. 207(C), pages 372-383.
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    Cited by:

    1. Rui Xiong & Suleiman M. Sharkh & Xi Zhang, 2018. "Research Progress on Electric and Intelligent Vehicles," Energies, MDPI, vol. 11(7), pages 1-5, July.
    2. Zou, Wenke & Sun, Yongjun & Gao, Dian-ce & Zhang, Xu & Liu, Junyao, 2023. "A review on integration of surging plug-in electric vehicles charging in energy-flexible buildings: Impacts analysis, collaborative management technologies, and future perspective," Applied Energy, Elsevier, vol. 331(C).
    3. Akhtar Hussain & Van-Hai Bui & Ju-Won Baek & Hak-Man Kim, 2019. "Stationary Energy Storage System for Fast EV Charging Stations: Simultaneous Sizing of Battery and Converter," Energies, MDPI, vol. 12(23), pages 1-17, November.
    4. Yian Yan & Huang Wang & Jiuchun Jiang & Weige Zhang & Yan Bao & Mei Huang, 2019. "Research on Configuration Methods of Battery Energy Storage System for Pure Electric Bus Fast Charging Station," Energies, MDPI, vol. 12(3), pages 1-17, February.
    5. Yajing Gao & Shixiao Guo & Jiafeng Ren & Zheng Zhao & Ali Ehsan & Yanan Zheng, 2018. "An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors," Energies, MDPI, vol. 11(8), pages 1-17, August.
    6. Akhtar Hussain & Van-Hai Bui & Ju-Won Baek & Hak-Man Kim, 2020. "Stationary Energy Storage System for Fast EV Charging Stations: Optimality Analysis and Results Validation," Energies, MDPI, vol. 13(1), pages 1-18, January.
    7. Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.

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