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Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function

Author

Listed:
  • Jingyeong Park

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Jeonghyeon Choi

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Hyeondeok Jo

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Daisuke Kodaira

    (Department of Electrical Engineering, Energy and Environment, University of Tsukuba, Tsukuba 305-8577, Ibaraki, Japan)

  • Sekyung Han

    (School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
    Department of Electrical Engineering, Kyungpook National University, Daegu 41566, Korea)

  • Moses Amoasi Acquah

    (Department of Electrical Energy Engineering, Keimyung University, 1095 Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea)

Abstract

Frequency regulation (FR) using a battery energy storage system (BESS) has been expanding because of the growth of renewable energy. This study introduces the wear density function, which considers battery degradation factors such as the rate of current, temperature, and depth of discharge (DOD) to provide a precise lifespan prediction. Furthermore, an equivalent system model is developed to evaluate the FR performance of the BESS for various operating parameters. Finally, a quantitative tradeoff relationship between performance and battery lifecycle is derived from the analysis using operational data of the actual BESS for FR.

Suggested Citation

  • Jingyeong Park & Jeonghyeon Choi & Hyeondeok Jo & Daisuke Kodaira & Sekyung Han & Moses Amoasi Acquah, 2022. "Life Evaluation of Battery Energy System for Frequency Regulation Using Wear Density Function," Energies, MDPI, vol. 15(21), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8071-:d:958308
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    References listed on IDEAS

    as
    1. Han, Sekyung & Han, Soohee & Aki, Hirohisa, 2014. "A practical battery wear model for electric vehicle charging applications," Applied Energy, Elsevier, vol. 113(C), pages 1100-1108.
    2. Moses Amoasi Acquah & Daisuke Kodaira & Sekyung Han, 2018. "Real-Time Demand Side Management Algorithm Using Stochastic Optimization," Energies, MDPI, vol. 11(5), pages 1-14, May.
    3. Alejandro Gismero & Erik Schaltz & Daniel-Ioan Stroe, 2020. "Recursive State of Charge and State of Health Estimation Method for Lithium-Ion Batteries Based on Coulomb Counting and Open Circuit Voltage," Energies, MDPI, vol. 13(7), pages 1-11, April.
    4. 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.
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    Cited by:

    1. Bing-Kuei Chiu & Kuei-Yen Lee & Yuan-Yih Hsu, 2023. "Battery Energy Storage System Damper Design for a Microgrid with Wind Generators Participating in Frequency Regulation," Energies, MDPI, vol. 16(21), pages 1-26, November.
    2. Lin, Yu-Hsiu & Shen, Ting-Yu, 2023. "Novel cell screening and prognosing based on neurocomputing-based multiday-ahead time-series forecasting for predictive maintenance of battery modules in frequency regulation-energy storage systems," Applied Energy, Elsevier, vol. 351(C).

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