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Use of a second-order reliability method to estimate the failure probability of an integrated energy system

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  • Fu, Xueqian
  • Li, Gengyin
  • Wang, Huaizhi

Abstract

A shortage of gas supply renders gas expensive and may even cause power outages. Estimation of the failure probability of gas supply is an essential component of an integrated energy system. To ensure that failure probability estimation is relevant to an actual project, energy network constraints should be fully considered in calculations. Here, this paper develops a second-order reliability estimation method to cope with the nonlinearity caused by network constraints. Under conditions of integrated energy supply, correlations exist between the extremes of wind power, the heat loads, and failure of the natural gas supply. This paper illustrates the proposed method by comparing the results to those of other methods. The proposed method is efficient in terms of both accuracy and computational time. Compared to a mixed algorithm, which required 1101.1 s to simulate tens of thousands of samples, the proposed method takes 3.5 s to obtain a failure probability. Also, the proposed method improves accuracy by at least 10-fold compared to that of a first-order reliability method.

Suggested Citation

  • Fu, Xueqian & Li, Gengyin & Wang, Huaizhi, 2018. "Use of a second-order reliability method to estimate the failure probability of an integrated energy system," Energy, Elsevier, vol. 161(C), pages 425-434.
    • Handle: RePEc:eee:energy:v:161:y:2018:i:c:p:425-434
    DOI: 10.1016/j.energy.2018.07.153
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    3. Ameryan, Ala & Ghalehnovi, Mansour & Rashki, Mohsen, 2022. "AK-SESC: a novel reliability procedure based on the integration of active learning kriging and sequential space conversion method," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    4. Gao, Haifeng & Wang, Anjenq & Zio, Enrico & Bai, Guangchen, 2020. "An integrated reliability approach with improved importance sampling for low-cycle fatigue damage prediction of turbine disks," Reliability Engineering and System Safety, Elsevier, vol. 199(C).
    5. Chi, Lixun & Su, Huai & Zio, Enrico & Qadrdan, Meysam & Li, Xueyi & Zhang, Li & Fan, Lin & Zhou, Jing & Yang, Zhaoming & Zhang, Jinjun, 2021. "Data-driven reliability assessment method of Integrated Energy Systems based on probabilistic deep learning and Gaussian mixture Model-Hidden Markov Model," Renewable Energy, Elsevier, vol. 174(C), pages 952-970.
    6. Kou, Yu & Bie, Zhaohong & Li, Gengfeng & Liu, Fan & Jiang, Jiangfeng, 2021. "Reliability evaluation of multi-agent integrated energy systems with fully distributed communication," Energy, Elsevier, vol. 224(C).
    7. Fu, Xueqian & Zhang, Xiurong & Qiao, Zheng & Li, Gengyin, 2019. "Estimating the failure probability in an integrated energy system considering correlations among failure patterns," Energy, Elsevier, vol. 178(C), pages 656-666.

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