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Supply reliability analysis of natural gas pipeline network based on demand-side economic loss risk

Author

Listed:
  • Yang, Kai
  • Hou, Lei
  • Man, Jianfeng
  • Yu, Qiaoyan
  • Li, Yu
  • Zhang, Xinru
  • Liu, Jiaquan

Abstract

Most studies on the natural gas supply reliability evaluation have reflected on whether the volume of gas supply to users is sufficient. However, different types of users suffer from the same natural gas shortage, the consequences of loss are different. The abundance of the supplied gas volumes does not reflect the actual losses incurred by users. Moreover, when a gas shortage occurs, the supply flow of different types of users is redistributed according to the supply order. Therefore, this study proposes an evaluation method for the gas supply reliability based on demand-side economic risk. The user's supply sequence is integrated into the cost matrix to optimize the distribution of the user's demand flow under gas shortage. A calculation model of the economic loss cost is established using alternative energy sources. Finally, a reliability index is established based on economic risk to evaluate the degree of supply security. The results showed that the reliability based on the economic risk assessment was slightly higher than that based on the gas quantity assessment. The reliabilities were 99.988% and 99.991%, respectively. The economic risk is reduced and the reliability is improved for the important users after considering the influence of the demand-side supply order.

Suggested Citation

  • Yang, Kai & Hou, Lei & Man, Jianfeng & Yu, Qiaoyan & Li, Yu & Zhang, Xinru & Liu, Jiaquan, 2023. "Supply reliability analysis of natural gas pipeline network based on demand-side economic loss risk," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
    • Handle: RePEc:eee:reensy:v:230:y:2023:i:c:s0951832022005762
    DOI: 10.1016/j.ress.2022.108961
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    References listed on IDEAS

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    1. Kim, Kyungmee O. & Zuo, Ming J., 2018. "Optimal allocation of reliability improvement target based on the failure risk and improvement cost," Reliability Engineering and System Safety, Elsevier, vol. 180(C), pages 104-110.
    2. Wang, Yanzhong & Xie, Bin & E, Shiyuan, 2022. "Adaptive relevance vector machine combined with Markov-chain-based importance sampling for reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 220(C).
    3. Medeiros, Cristina Pereira & da Silva, Lucas Borges Leal & Alencar, Marcelo Hazin & de Almeida, Adiel Teixeira, 2021. "A new method for managing multidimensional risks in Natural Gas Pipelines based on non-Expected Utility," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    4. Yang, Yang & Li, Suzhen & Zhang, Pengcheng, 2022. "Data-driven accident consequence assessment on urban gas pipeline network based on machine learning," Reliability Engineering and System Safety, Elsevier, vol. 219(C).
    5. Forghani-elahabad, Majid & Yeh, Wei-Chang, 2022. "An improved algorithm for reliability evaluation of flow networks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    6. Wang, WuChang & Zhang, Yi & Li, YuXing & Hu, Qihui & Liu, Chengsong & Liu, Cuiwei, 2022. "Vulnerability analysis method based on risk assessment for gas transmission capabilities of natural gas pipeline networks," Reliability Engineering and System Safety, Elsevier, vol. 218(PB).
    7. Yu, Weichao & Huang, Weihe & Wen, Yunhao & Li, Yichen & Liu, Hongfei & Wen, Kai & Gong, Jing & Lu, Yanan, 2021. "An integrated gas supply reliability evaluation method of the large-scale and complex natural gas pipeline network based on demand-side analysis," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    8. Senderov, Sergey M. & Vorobev, Sergey V. & Smirnova, Elena M., 2022. "Peak underground gas storage efficiency in reducing the vulnerability of gas supply to consumers in an extensive gas transmission system," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    9. Ding-Zhu Du & Panos Pardalos & Xiaodong Hu & Weili Wu, 2022. "Primal-Dual Methods and Minimum Cost Flow," Springer Optimization and Its Applications, in: Introduction to Combinatorial Optimization, chapter 0, pages 175-198, Springer.
    10. Bao, Minglei & Ding, Yi & Yin, Xunhu & Shao, Changzheng & Ye, Chenjin, 2021. "Definitions and Reliability Evaluation of Multi-state Systems Considering State Transition Process and its Application for Gas Systems," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    11. Liu, Aihua & Chen, Ke & Huang, Xiaofei & Li, Didi & Zhang, Xiaochun, 2021. "Dynamic risk assessment model of buried gas pipelines based on system dynamics," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    12. Ruud van der Linden & Ryvo Octaviano & Huib Blokland & Tom Busking, 2021. "Security of Supply in Gas and Hybrid Energy Networks," Energies, MDPI, vol. 14(4), pages 1-15, February.
    13. Fan, Lin & Su, Huai & Wang, Wei & Zio, Enrico & Zhang, Li & Yang, Zhaoming & Peng, Shiliang & Yu, Weichao & Zuo, Lili & Zhang, Jinjun, 2022. "A systematic method for the optimization of gas supply reliability in natural gas pipeline network based on Bayesian networks and deep reinforcement learning," Reliability Engineering and System Safety, Elsevier, vol. 225(C).
    14. Chen, Qian & Zuo, Lili & Wu, Changchun & Li, Yun & Hua, Kaixun & Mehrtash, Mahdi & Cao, Yankai, 2022. "Optimization of compressor standby schemes for gas transmission pipeline systems based on gas delivery reliability," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
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