IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i23p6304-d453314.html
   My bibliography  Save this article

Coordinated Control of a Hybrid Energy Storage System for Improving the Capability of Frequency Regulation and State-of-Charge Management

Author

Listed:
  • Thien-An Nguyen-Huu

    (Department of Energy System Engineering, Inje University, Gimhae 50834, Korea)

  • Van Thang Nguyen

    (Department of Energy System Engineering, Inje University, Gimhae 50834, Korea)

  • Kyeon Hur

    (School of Electrical and Electronic Engineering, Yonsei University, Seoul 03722, Korea)

  • Jae Woong Shim

    (Department of Energy System Engineering, Inje University, Gimhae 50834, Korea)

Abstract

The paper proposes a coordinated operation method of two independent storages for managing state-of-charge (SOC) and for providing ancillary service concerning frequency regulation (FR); furthermore, this article also introduces the power allocation scheme between two storages in consideration of the coverage of the frequency band for each storage along with the SOC management scheme of the supercapacitor and battery. We also provide a guideline for the storage sizing on the basis of the smoothing time constant. Additionally, we verify the advantage of the HESS in extending the lifetime of the battery, which is estimated by a real-time state-of-health (SOH) calculation method. The Bode plot of the proposed method is analyzed to observe the power spectrum coverage in the frequency domain through the case studies using PSCAD/EMTDC and MATLAB.

Suggested Citation

  • Thien-An Nguyen-Huu & Van Thang Nguyen & Kyeon Hur & Jae Woong Shim, 2020. "Coordinated Control of a Hybrid Energy Storage System for Improving the Capability of Frequency Regulation and State-of-Charge Management," Energies, MDPI, vol. 13(23), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6304-:d:453314
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/23/6304/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/23/6304/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Greenwood, D.M. & Lim, K.Y. & Patsios, C. & Lyons, P.F. & Lim, Y.S. & Taylor, P.C., 2017. "Frequency response services designed for energy storage," Applied Energy, Elsevier, vol. 203(C), pages 115-127.
    2. Shyh-Chin Huang & Kuo-Hsin Tseng & Jin-Wei Liang & Chung-Liang Chang & Michael G. Pecht, 2017. "An Online SOC and SOH Estimation Model for Lithium-Ion Batteries," Energies, MDPI, vol. 10(4), pages 1-18, April.
    3. Ng, Kong Soon & Moo, Chin-Sien & Chen, Yi-Ping & Hsieh, Yao-Ching, 2009. "Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries," Applied Energy, Elsevier, vol. 86(9), pages 1506-1511, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gustavo Navarro & Jorge Torres & Marcos Blanco & Jorge Nájera & Miguel Santos-Herran & Marcos Lafoz, 2021. "Present and Future of Supercapacitor Technology Applied to Powertrains, Renewable Generation and Grid Connection Applications," Energies, MDPI, vol. 14(11), pages 1-29, May.

    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. Sui, Xin & He, Shan & Vilsen, Søren B. & Meng, Jinhao & Teodorescu, Remus & Stroe, Daniel-Ioan, 2021. "A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery," Applied Energy, Elsevier, vol. 300(C).
    2. Donghoon Shin & Beomjin Yoon & Seungryeol Yoo, 2021. "Compensation Method for Estimating the State of Charge of Li-Polymer Batteries Using Multiple Long Short-Term Memory Networks Based on the Extended Kalman Filter," Energies, MDPI, vol. 14(2), pages 1-19, January.
    3. Shehzar Shahzad Sheikh & Mahnoor Anjum & Muhammad Abdullah Khan & Syed Ali Hassan & Hassan Abdullah Khalid & Adel Gastli & Lazhar Ben-Brahim, 2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach," Energies, MDPI, vol. 13(14), pages 1-16, July.
    4. Panpan Hu & W. F. Tang & C. H. Li & Shu-Lun Mak & C. Y. Li & C. C. Lee, 2023. "Joint State of Charge (SOC) and State of Health (SOH) Estimation for Lithium-Ion Batteries Packs of Electric Vehicles Based on NSSR-LSTM Neural Network," Energies, MDPI, vol. 16(14), pages 1-19, July.
    5. Shuqing Li & Chuankun Ju & Jianliang Li & Ri Fang & Zhifei Tao & Bo Li & Tingting Zhang, 2021. "State-of-Charge Estimation of Lithium-Ion Batteries in the Battery Degradation Process Based on Recurrent Neural Network," Energies, MDPI, vol. 14(2), pages 1-21, January.
    6. Bizhong Xia & Zheng Zhang & Zizhou Lao & Wei Wang & Wei Sun & Yongzhi Lai & Mingwang Wang, 2018. "Strong Tracking of a H-Infinity Filter in Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 11(6), pages 1-20, June.
    7. Fabietti, Luca & Qureshi, Faran A. & Gorecki, Tomasz T. & Salzmann, Christophe & Jones, Colin N., 2018. "Multi-time scale coordination of complementary resources for the provision of ancillary services," Applied Energy, Elsevier, vol. 229(C), pages 1164-1180.
    8. Yang, Duo & Wang, Yujie & Pan, Rui & Chen, Ruiyang & Chen, Zonghai, 2018. "State-of-health estimation for the lithium-ion battery based on support vector regression," Applied Energy, Elsevier, vol. 227(C), pages 273-283.
    9. Wenxian Duan & Chuanxue Song & Silun Peng & Feng Xiao & Yulong Shao & Shixin Song, 2020. "An Improved Gated Recurrent Unit Network Model for State-of-Charge Estimation of Lithium-Ion Battery," Energies, MDPI, vol. 13(23), pages 1-19, December.
    10. Guoqing Jin & Lan Li & Yidan Xu & Minghui Hu & Chunyun Fu & Datong Qin, 2020. "Comparison of SOC Estimation between the Integer-Order Model and Fractional-Order Model Under Different Operating Conditions," Energies, MDPI, vol. 13(7), pages 1-17, April.
    11. Hu, Lin & Hu, Xiaosong & Che, Yunhong & Feng, Fei & Lin, Xianke & Zhang, Zhiyong, 2020. "Reliable state of charge estimation of battery packs using fuzzy adaptive federated filtering," Applied Energy, Elsevier, vol. 262(C).
    12. Parwal, Arvind & Fregelius, Martin & Temiz, Irinia & Göteman, Malin & Oliveira, Janaina G. de & Boström, Cecilia & Leijon, Mats, 2018. "Energy management for a grid-connected wave energy park through a hybrid energy storage system," Applied Energy, Elsevier, vol. 231(C), pages 399-411.
    13. Ozkurt, Celil & Camci, Fatih & Atamuradov, Vepa & Odorry, Christopher, 2016. "Integration of sampling based battery state of health estimation method in electric vehicles," Applied Energy, Elsevier, vol. 175(C), pages 356-367.
    14. Shahjalal, Mohammad & Roy, Probir Kumar & Shams, Tamanna & Fly, Ashley & Chowdhury, Jahedul Islam & Ahmed, Md. Rishad & Liu, Kailong, 2022. "A review on second-life of Li-ion batteries: prospects, challenges, and issues," Energy, Elsevier, vol. 241(C).
    15. Pusceddu, Elian & Zakeri, Behnam & Castagneto Gissey, Giorgio, 2021. "Synergies between energy arbitrage and fast frequency response for battery energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    16. Dario Garozzo & Giuseppe Marco Tina, 2020. "Evaluation of the Effective Active Power Reserve for Fast Frequency Response of PV with BESS Inverters Considering Reactive Power Control," Energies, MDPI, vol. 13(13), pages 1-16, July.
    17. Jae-Won Chang & Gyu-Sub Lee & Hyeon-Jin Moon & Mark B. Glick & Seung-Il Moon, 2019. "Coordinated Frequency and State-of-Charge Control with Multi-Battery Energy Storage Systems and Diesel Generators in an Isolated Microgrid," Energies, MDPI, vol. 12(9), pages 1-16, April.
    18. Guo, Feng & Hu, Guangdi & Xiang, Shun & Zhou, Pengkai & Hong, Ru & Xiong, Neng, 2019. "A multi-scale parameter adaptive method for state of charge and parameter estimation of lithium-ion batteries using dual Kalman filters," Energy, Elsevier, vol. 178(C), pages 79-88.
    19. Panagiotis Eleftheriadis & Spyridon Giazitzis & Sonia Leva & Emanuele Ogliari, 2023. "Data-Driven Methods for the State of Charge Estimation of Lithium-Ion Batteries: An Overview," Forecasting, MDPI, vol. 5(3), pages 1-24, September.
    20. Hyeongpil Bang & Dwi Riana Aryani & Hwachang Song, 2021. "Application of Battery Energy Storage Systems for Relief of Generation Curtailment in Terms of Transient Stability," Energies, MDPI, vol. 14(13), pages 1-14, June.

    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:jeners:v:13:y:2020:i:23:p:6304-:d:453314. 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.