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Probabilistic Safety Analysis in Complex Pipeline Systems

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Advanced Intelligent Pipeline Management Technology

Abstract

The sustainable and reliable supply of natural gas has become a critical global worry about economic, political, and technological causes because of the realization of the significance of networks of natural gas pipeline for energy security. However, a number of variables, including stochastic demand fluctuations, unexpected changes in supply capacity, and random pipeline failures, affect the oil and gas pipeline network’s supply capacity. People must therefore evaluate supply security from a probabilistic rather than deterministic standpoint. This chapter introduces a methodical approach for evaluating natural gas pipeline networks’ supply reliability. The created technique combines stochastic processes, graph theory, and thermal–hydraulic modeling while taking complexity and uncertainty into consideration. The case study shows that a network of gas pipelines is taken into account, and the outcomes are thoroughly studied.

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References

  1. O. G. Austvik, “The Energy Union and security-of-gas supply,” Energy Policy, vol. 96, pp. 372–382, 2016.

    Google Scholar 

  2. E. Zio, An Introduction to the Basics of Reliability and Risk Analysis, vol. 13. World Scientific Publishing Company, 2007.

    Google Scholar 

  3. R. Allan and R. Billinton, “Probabilistic assessment of power systems,” Proc. IEEE, vol. 88, no. 2, pp. 140–162, Feb. 2000.

    Google Scholar 

  4. H.-D. Mo, Y.-F. Li, and E. Zio, “A system-of-systems framework for the reliability analysis of distributed generation systems accounting for the impact of degraded communication networks,” Appl. Energy, vol. 183, pp. 805–822, 2016.

    Google Scholar 

  5. N. Bassamzadeh and R. Ghanem, “Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks,” Appl. Energy, vol. 193, pp. 369–380, 2017.

    Google Scholar 

  6. H. Verdejo, A. Awerkin, E. Saavedra, W. Kliemann, and L. Vargas, “Stochastic modeling to represent wind power generation and demand in electric power system based on real data,” Appl. Energy, vol. 173, pp. 283–295, 2016.

    Google Scholar 

  7. S. Garshasbi, J. Kurnitski, and Y. Mohammadi, “A hybrid Genetic Algorithm and Monte Carlo simulation approach to predict hourly energy consumption and generation by a cluster of Net Zero Energy Buildings,” Appl. Energy, vol. 179, pp. 626–637, 2016.

    Google Scholar 

  8. C. Hu, S.-Y. Lu, and Y.-H. Chen, “Stochastic–multiobjective market equilibrium analysis of a demand response program in energy market under uncertainty,” Appl. Energy, vol. 182, pp. 500–506, 2016.

    Google Scholar 

  9. M. Marseguerra and E. Zio, “Monte Carlo approach to PSA for dynamic process systems,” Reliab. Eng. Syst. Saf., vol. 52, no. 3, pp. 227–241, 1996.

    Google Scholar 

  10. X. Fu, H. Sun, Q. Guo, Z. Pan, X. Zhang, and S. Zeng, “Probabilistic power flow analysis considering the dependence between power and heat,” Appl. Energy, vol. 191, pp. 582–592, 2017.

    Google Scholar 

  11. A. Szikszai and F. Monforti, “GEMFLOW: A time dependent model to assess responses to natural gas supply crises,” Energy Policy, vol. 39, no. 9, pp. 5129–5136, 2011.

    Google Scholar 

  12. M. Flouri, C. Karakosta, C. Kladouchou, and J. Psarras, “How does a natural gas supply interruption affect the EU gas security? A Monte Carlo simulation,” Renew. Sustain. Energy Rev., vol. 44, pp. 785–796, 2015.

    Google Scholar 

  13. E. G. P. I. D. G. EGIG, “9th Report of the European Gas Pipeline Incident Data Group (1970–2013),” no. 1, p. 23, 2015.

    Google Scholar 

  14. S. Zhang and W. Zhou, “System reliability of corroding pipelines considering stochastic process-based models for defect growth and internal pressure,” Int. J. Press. Vessel. Pip., vol. 111, pp. 120–130, 2013.

    Google Scholar 

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Correspondence to Zhaoming Yang .

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Yang, Z., Su, H. (2023). Probabilistic Safety Analysis in Complex Pipeline Systems. In: Su, H., Liao, Q., Zhang, H., Zio, E. (eds) Advanced Intelligent Pipeline Management Technology. Springer, Singapore. https://doi.org/10.1007/978-981-19-9899-7_8

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