IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i15p9189-d872849.html
   My bibliography  Save this article

Control Strategy and Performance Analysis of Electrochemical Energy Storage Station Participating in Power System Frequency Regulation: A Case Study of the Jiangsu Power Grid

Author

Listed:
  • Jicheng Fang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Yifei Wang

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Zhen Lei

    (State Grid Jiangsu Electric Power Co., Ltd., Nanjing 210024, China)

  • Qingshan Xu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China
    Nanjing Center for Applied Mathematics, Nanjing 211135, China)

Abstract

Electrochemical energy storage stations (EESSs) have been demonstrated as a promising solution to mitigate power imbalances by participating in peak shaving, load frequency control (LFC), etc. This paper mainly analyzes the effectiveness and advantages of control strategies for eight EESSs with a total capacity of 101 MW/202 MWh in the automatic generation control (AGC) in the power system of the Jiangsu power grid. Firstly, an adaptive tracking strategy for electricity quantity that considers the state of charge (SOC) of EESSs is proposed to calculate the baseline power of the EESS participating in AGC. This strategy can simultaneously coordinate different time scale application requirements, such as peak shaving and LFC. Then, an adaptive strategy for regulation requirement allocation among AGC control groups with EESSs that considers different area regulation requirements (ARRs) is proposed to calculate the regulation power of EESS participating in AGC. This strategy can realize the balance transfer of fast and slow regulation capacity, ensure the complementary advantages of various frequency regulation resources and improve the dynamic regulation performance of the control area. Finally, the proposed strategy is validated via a test system to confirm its effectiveness and advantages, as well as via a quantitative analysis on the improvement of the control performance standard (CPS) of the Jiangsu power grid with the participation of EESSs in AGC.

Suggested Citation

  • Jicheng Fang & Yifei Wang & Zhen Lei & Qingshan Xu, 2022. "Control Strategy and Performance Analysis of Electrochemical Energy Storage Station Participating in Power System Frequency Regulation: A Case Study of the Jiangsu Power Grid," Sustainability, MDPI, vol. 14(15), pages 1-31, July.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9189-:d:872849
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/15/9189/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/14/15/9189/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wu, Wei & Lin, Boqiang, 2018. "Application value of energy storage in power grid: A special case of China electricity market," Energy, Elsevier, vol. 165(PB), pages 1191-1199.
    2. Hau, Lee Cheun & Lim, Yun Seng & Liew, Serena Miao San, 2020. "A novel spontaneous self-adjusting controller of energy storage system for maximum demand reductions under penetration of photovoltaic system," Applied Energy, Elsevier, vol. 260(C).
    3. Zhong, Jin & He, Lina & Li, Canbing & Cao, Yijia & Wang, Jianhui & Fang, Baling & Zeng, Long & Xiao, Guoxuan, 2014. "Coordinated control for large-scale EV charging facilities and energy storage devices participating in frequency regulation," Applied Energy, Elsevier, vol. 123(C), pages 253-262.
    4. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
    5. Eric Pareis & Eric Hittinger, 2021. "Emissions Effects of Energy Storage for Frequency Regulation: Comparing Battery and Flywheel Storage to Natural Gas," Energies, MDPI, vol. 14(3), pages 1-19, January.
    6. Horesh, Noah & Quinn, Casey & Wang, Hongjie & Zane, Regan & Ferry, Mike & Tong, Shijie & Quinn, Jason C., 2021. "Driving to the future of energy storage: Techno-economic analysis of a novel method to recondition second life electric vehicle batteries," Applied Energy, Elsevier, vol. 295(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gaurav Chaudhary & Jacob J. Lamb & Odne S. Burheim & Bjørn Austbø, 2021. "Review of Energy Storage and Energy Management System Control Strategies in Microgrids," Energies, MDPI, vol. 14(16), pages 1-26, August.
    2. Gui, Yonghao & Wei, Baoze & Li, Mingshen & Guerrero, Josep M. & Vasquez, Juan C., 2018. "Passivity-based coordinated control for islanded AC microgrid," Applied Energy, Elsevier, vol. 229(C), pages 551-561.
    3. Johannes Dahlke & Kristina Bogner & Matthias Mueller & Thomas Berger & Andreas Pyka & Bernd Ebersberger, 2020. "Is the Juice Worth the Squeeze? Machine Learning (ML) In and For Agent-Based Modelling (ABM)," Papers 2003.11985, arXiv.org.
    4. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    5. Xiaohan Fang & Jinkuan Wang & Guanru Song & Yinghua Han & Qiang Zhao & Zhiao Cao, 2019. "Multi-Agent Reinforcement Learning Approach for Residential Microgrid Energy Scheduling," Energies, MDPI, vol. 13(1), pages 1-26, December.
    6. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    7. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    8. Lefeng, Shi & Shengnan, Lv & Chunxiu, Liu & Yue, Zhou & Cipcigan, Liana & Acker, Thomas L., 2020. "A framework for electric vehicle power supply chain development," Utilities Policy, Elsevier, vol. 64(C).
    9. García-Triviño, Pablo & Torreglosa, Juan P. & Fernández-Ramírez, Luis M. & Jurado, Francisco, 2016. "Control and operation of power sources in a medium-voltage direct-current microgrid for an electric vehicle fast charging station with a photovoltaic and a battery energy storage system," Energy, Elsevier, vol. 115(P1), pages 38-48.
    10. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    11. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    12. Harrold, Daniel J.B. & Cao, Jun & Fan, Zhong, 2022. "Data-driven battery operation for energy arbitrage using rainbow deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    13. Esmaeil Ahmadi & Benjamin McLellan & Behnam Mohammadi-Ivatloo & Tetsuo Tezuka, 2020. "The Role of Renewable Energy Resources in Sustainability of Water Desalination as a Potential Fresh-Water Source: An Updated Review," Sustainability, MDPI, vol. 12(13), pages 1-31, June.
    14. Lin, Boqiang & Wu, Wei, 2021. "The impact of electric vehicle penetration: A recursive dynamic CGE analysis of China," Energy Economics, Elsevier, vol. 94(C).
    15. Mehrdad Tarafdar-Hagh & Kamran Taghizad-Tavana & Mohsen Ghanbari-Ghalehjoughi & Sayyad Nojavan & Parisa Jafari & Amin Mohammadpour Shotorbani, 2023. "Optimizing Electric Vehicle Operations for a Smart Environment: A Comprehensive Review," Energies, MDPI, vol. 16(11), pages 1-21, May.
    16. Jung, Seunghoon & Jeoung, Jaewon & Kang, Hyuna & Hong, Taehoon, 2021. "Optimal planning of a rooftop PV system using GIS-based reinforcement learning," Applied Energy, Elsevier, vol. 298(C).
    17. Liu, Hui & Huang, Kai & Wang, Ni & Qi, Junjian & Wu, Qiuwei & Ma, Shicong & Li, Canbing, 2019. "Optimal dispatch for participation of electric vehicles in frequency regulation based on area control error and area regulation requirement," Applied Energy, Elsevier, vol. 240(C), pages 46-55.
    18. Chen, Pengzhan & Liu, Mengchao & Chen, Chuanxi & Shang, Xin, 2019. "A battery management strategy in microgrid for personalized customer requirements," Energy, Elsevier, vol. 189(C).
    19. Pedram Asef & Marzia Milan & Andrew Lapthorn & Sanjeevikumar Padmanaban, 2021. "Future Trends and Aging Analysis of Battery Energy Storage Systems for Electric Vehicles," Sustainability, MDPI, vol. 13(24), pages 1-28, December.
    20. Luo, Qingsong & Zhou, Yimin & Hou, Weicheng & Peng, Lei, 2022. "A hierarchical blockchain architecture based V2G market trading system," Applied Energy, Elsevier, vol. 307(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9189-:d:872849. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.