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Reliability analysis on energy storage system combining GO-FLOW methodology with GERT network

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  • Li, Jingkui
  • Liu, Xiaona
  • Lu, Yuze
  • Wang, Hanzheng

Abstract

Energy storage systems are widely used in various industrial areas, playing a crucial role in improving system reliability. In the energy storage system, the batteries serve as standbys for generators and have an auxiliary regulation function, so the complex relationship between them is unable to be accurately described by the basic GO-FLOW operators. To address this issue, this paper proposes a new operator for simulating the energy storage system, employing the GERT network for modelling and calculation. In this approach, the energy storage system operator is created to represent the energy storage system with ‘n generators with l degradation states, m batteries’. Firstly, each degradation state of generators is determined. Secondly, each macro state of the energy storage system is determined by combining the states of the batteries. Finally, based on the relationships between these states, the steady-state availability of the energy storage system is obtained using the GERT algorithm. A numerical example of a repairable power supply system is employed to validate the feasibility and effectiveness of the model and algorithm. And this method holds great significance for the reliability analysis of multi-state complex systems.

Suggested Citation

  • Li, Jingkui & Liu, Xiaona & Lu, Yuze & Wang, Hanzheng, 2024. "Reliability analysis on energy storage system combining GO-FLOW methodology with GERT network," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
  • Handle: RePEc:eee:reensy:v:243:y:2024:i:c:s0951832023007743
    DOI: 10.1016/j.ress.2023.109860
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    References listed on IDEAS

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    1. Yousefi, Nooshin & Coit, David W. & Song, Sanling, 2020. "Reliability analysis of systems considering clusters of dependent degrading components," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    2. Davila-Frias, Alex & Yodo, Nita & Le, Trung & Yadav, Om Prakash, 2023. "A deep neural network and Bayesian method based framework for all-terminal network reliability estimation considering degradation," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
    3. Li, Jingkui & Lu, Yuze & Liu, Xiaona & Jiang, Xiuhong, 2023. "Reliability analysis of cold-standby phased-mission system based on GO-FLOW methodology and the universal generating function," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    4. Tao, Liangyan & Wu, Desheng & Liu, Sifeng & Lambert, James H., 2017. "Schedule risk analysis for new-product development: The GERT method extended by a characteristic function," Reliability Engineering and System Safety, Elsevier, vol. 167(C), pages 464-473.
    5. Song, Sanling & Coit, David W. & Feng, Qianmei, 2014. "Reliability for systems of degrading components with distinct component shock sets," Reliability Engineering and System Safety, Elsevier, vol. 132(C), pages 115-124.
    6. Matsuoka, Takeshi, 2009. "An exact method for solving logical loops in reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 94(8), pages 1282-1288.
    7. Liang, Qingzhu & Yang, Yinghao & Peng, Changhong, 2023. "A reliability model for systems subject to mutually dependent degradation processes and random shocks under dynamic environments," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
    8. Nelson, Richard Graham & Azaron, Amir & Aref, Samin, 2016. "The use of a GERT based method to model concurrent product development processes," European Journal of Operational Research, Elsevier, vol. 250(2), pages 566-578.
    9. Zeng, Jing & Liu, Sifeng, 2023. "Forecasting the sustainable classified recycling of used lithium batteries by gray Graphical Evaluation and Review Technique," Renewable Energy, Elsevier, vol. 202(C), pages 602-612.
    10. Matsuoka, Takeshi, 2023. "Reliability analysis of a BWR plant system at startup stage  - analysis by the GO-FLOW methodology with consideration of loop structures and phased mission problem -," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    11. Chang, Miaoxin & Huang, Xianzhen & Coolen, Frank P.A. & Coolen-Maturi, Tahani, 2021. "Reliability analysis for systems based on degradation rates and hard failure thresholds changing with degradation levels," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    12. Liu, Di & Wang, Shaoping, 2020. "A degradation modeling and reliability estimation method based on Wiener process and evidential variable," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    13. Liu, Di & Wang, Shaoping & Zhang, Chao & Tomovic, Mileta, 2018. "Bayesian model averaging based reliability analysis method for monotonic degradation dataset based on inverse Gaussian process and Gamma process," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 25-38.
    14. Saberzadeh, Zahra & Razmkhah, Mostafa, 2022. "Reliability of degrading complex systems with two dependent components per element," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    15. Billinton, Roy & Bagen,, 2006. "Generating capacity adequacy evaluation of small stand-alone power systems containing solar energy," Reliability Engineering and System Safety, Elsevier, vol. 91(4), pages 438-443.
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