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Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation

Author

Listed:
  • Sung-Min Cho

    (Korea Electric Power Research Institute (KEPRI), Korea Electric Power Company (KEPCO), 105 Munji-Ro, Yuseong-gu, Daejeon 34056, Korea)

  • Jin-Su Kim

    (Department of Electrical Engineering, Soongsil University, 369, Sangdo-ro Dongjak-gu, Seoul 06978, Korea)

  • Jae-Chul Kim

    (Department of Electrical Engineering, Soongsil University, 369, Sangdo-ro Dongjak-gu, Seoul 06978, Korea)

Abstract

This study proposes a method for optimally selecting the operating parameters of an energy storage system (ESS) for frequency regulation (FR) in an electric power system. First, the method allows the optimal objective function of the selected parameters to be set in a flexible manner according to the electric market environment. The objective functions are defined so that they could be used under a variety of electricity market conditions. Second, evaluation frequencies are created in order to simulate the overall lifespan of the FR-ESS. Third, calendar and cycle degradation models are applied to the battery degradation, and are incorporated into evaluations of the degradation progress during the entire FR-ESS lifespan to obtain more accurate results. A calendar life limit is set, and the limit is also considered in the objective function evaluations. Fourth, an optimal parameter calculation algorithm, which uses the branch-and-bound method, is proposed to calculate the optimal parameters. A case study analyzes the convergence of the proposed algorithm and the results of the algorithm under various conditions. The results confirmed that the proposed algorithm yields optimal parameters that are appropriate according to the objective function and lifespan conditions. We anticipate that the proposed FR-ESS algorithm will be beneficial in establishing optimal operating strategies.

Suggested Citation

  • Sung-Min Cho & Jin-Su Kim & Jae-Chul Kim, 2019. "Optimal Operation Parameter Estimation of Energy Storage for Frequency Regulation," Energies, MDPI, vol. 12(9), pages 1-21, May.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:9:p:1782-:d:230088
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    References listed on IDEAS

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    1. Houfei Lin & Jianxin Jin & Qidai Lin & Bo Li & Chengzhi Wei & Wenfa Kang & Minyou Chen, 2019. "Distributed Settlement of Frequency Regulation Based on a Battery Energy Storage System," Energies, MDPI, vol. 12(1), pages 1-17, January.
    2. Fabio Massimo Gatta & Alberto Geri & Regina Lamedica & Stefano Lauria & Marco Maccioni & Francesco Palone & Massimo Rebolini & Alessandro Ruvio, 2016. "Application of a LiFePO 4 Battery Energy Storage System to Primary Frequency Control: Simulations and Experimental Results," Energies, MDPI, vol. 9(11), pages 1-16, October.
    3. 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.
    4. Yu Su & Niancheng Zhou & Qianggang Wang & Chao Lei & Jian Fang, 2018. "Optimal Planning Method of On-load Capacity Regulating Distribution Transformers in Urban Distribution Networks after Electric Energy Replacement Considering Uncertainties," Energies, MDPI, vol. 11(6), pages 1-25, June.
    5. Jorge Arias & Maria Calle & Daniel Turizo & Javier Guerrero & John E. Candelo-Becerra, 2019. "Historical Load Balance in Distribution Systems Using the Branch and Bound Algorithm," Energies, MDPI, vol. 12(7), pages 1-14, March.
    6. Jianjian Shen & Xiufei Zhang & Jian Wang & Rui Cao & Sen Wang & Jun Zhang, 2019. "Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions," Energies, MDPI, vol. 12(1), pages 1-19, January.
    7. Sekyung Han & Soohee Han, 2013. "Economic Feasibility of V2G Frequency Regulation in Consideration of Battery Wear," Energies, MDPI, vol. 6(2), pages 1-18, February.
    8. Sung-Min Cho & Sang-Yun Yun, 2017. "Optimal Power Assignment of Energy Storage Systems to Improve the Energy Storage Efficiency for Frequency Regulation," Energies, MDPI, vol. 10(12), pages 1-13, December.
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    Cited by:

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    3. Yingjie Zhou & Qibin Li & Qiang Wang, 2019. "Energy Storage Analysis of UIO-66 and Water Mixed Nanofluids: An Experimental and Theoretical Study," Energies, MDPI, vol. 12(13), pages 1-9, June.

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