IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v162y2018icp853-870.html
   My bibliography  Save this article

Gas supply reliability assessment of natural gas transmission pipeline systems

Author

Listed:
  • Yu, Weichao
  • Song, Shangfei
  • Li, Yichen
  • Min, Yuan
  • Huang, Weihe
  • Wen, Kai
  • Gong, Jing

Abstract

The uncertainty of market demand and dynamic behaviour of the pipeline system are usually ignored in previous gas supply reliability assessments. With the intent of overcoming these deficiencies, a novel methodology to assess the gas supply reliability of natural gas transmission pipeline systems is proposed in this paper. Considering both gas supply capacity and market demand uncertainties, calculations of these two items are integrated into a single Monte Carlo simulation. On each Monte Carlo trial, the hydraulic analysis of unsteady flow is combined with the state transition process simulation to calculate the gas supply capacity. In terms of market demand, the load duration curve technology is employed to predict the amount of demand. Then, the indicator proposed to quantify gas supply reliability is calculated on each trial. Finally, the average gas supply reliability is obtained based on N Monte Carlo trials. Applications of this methodology are demonstrated through a real transmission pipeline system. Thereafter, the method is compared with previous approaches and differences are discussed. Furthermore, the impacts of supply capacity and market demand uncertainties on the gas supply reliability are investigated and suggestions to improve the gas supply reliability are proposed.

Suggested Citation

  • Yu, Weichao & Song, Shangfei & Li, Yichen & Min, Yuan & Huang, Weihe & Wen, Kai & Gong, Jing, 2018. "Gas supply reliability assessment of natural gas transmission pipeline systems," Energy, Elsevier, vol. 162(C), pages 853-870.
  • Handle: RePEc:eee:energy:v:162:y:2018:i:c:p:853-870
    DOI: 10.1016/j.energy.2018.08.039
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544218315597
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2018.08.039?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Rodríguez-Gómez, Nuria & Zaccarelli, Nicola & Bolado-Lavín, Ricardo, 2016. "European ability to cope with a gas crisis. Comparison between 2009 and 2014," Energy Policy, Elsevier, vol. 97(C), pages 461-474.
    2. Mingqi Zhang & Meirong Su & Weiwei Lu & Chunhua Su, 2015. "An Assessment of the Security of China’s Natural Gas Supply System Using Two Network Models," Energies, MDPI, vol. 8(12), pages 1-16, December.
    3. Johansson, Jonas & Hassel, Henrik & Zio, Enrico, 2013. "Reliability and vulnerability analyses of critical infrastructures: Comparing two approaches in the context of power systems," Reliability Engineering and System Safety, Elsevier, vol. 120(C), pages 27-38.
    4. Pambour, Kwabena Addo & Cakir Erdener, Burcin & Bolado-Lavin, Ricardo & Dijkema, Gerard P.J., 2017. "SAInt – A novel quasi-dynamic model for assessing security of supply in coupled gas and electricity transmission networks," Applied Energy, Elsevier, vol. 203(C), pages 829-857.
    5. Szoplik, Jolanta, 2015. "Forecasting of natural gas consumption with artificial neural networks," Energy, Elsevier, vol. 85(C), pages 208-220.
    6. Villada, Juan & Olaya, Yris, 2013. "A simulation approach for analysis of short-term security of natural gas supply in Colombia," Energy Policy, Elsevier, vol. 53(C), pages 11-26.
    7. Yu, Weichao & Wen, Kai & Min, Yuan & He, Lei & Huang, Weihe & Gong, Jing, 2018. "A methodology to quantify the gas supply capacity of natural gas transmission pipeline system using reliability theory," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 128-141.
    8. Chaudry, Modassar & Wu, Jianzhong & Jenkins, Nick, 2013. "A sequential Monte Carlo model of the combined GB gas and electricity network," Energy Policy, Elsevier, vol. 62(C), pages 473-483.
    9. Flouri, Maria & Karakosta, Charikleia & Kladouchou, Charikleia & Psarras, John, 2015. "How does a natural gas supply interruption affect the EU gas security? A Monte Carlo simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 785-796.
    10. Monforti, F. & Szikszai, A., 2010. "A MonteCarlo approach for assessing the adequacy of the European gas transmission system under supply crisis conditions," Energy Policy, Elsevier, vol. 38(5), pages 2486-2498, May.
    11. Praks, Pavel & Kopustinskas, Vytis & Masera, Marcelo, 2015. "Probabilistic modelling of security of supply in gas networks and evaluation of new infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 254-264.
    12. Su, Huai & Zio, Enrico & Zhang, Jinjun & Li, Xueyi, 2018. "A systematic framework of vulnerability analysis of a natural gas pipeline network," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 79-91.
    13. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    14. Shaikh, Faheemullah & Ji, Qiang & Shaikh, Pervez Hameed & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2017. "Forecasting China’s natural gas demand based on optimised nonlinear grey models," Energy, Elsevier, vol. 140(P1), pages 941-951.
    15. Szikszai, A. & Monforti, F., 2011. "GEMFLOW: A time dependent model to assess responses to natural gas supply crises," Energy Policy, Elsevier, vol. 39(9), pages 5129-5136, September.
    16. Thaler, Marko & Grabec, Igor & Poredoš, Alojz, 2005. "Prediction of energy consumption and risk of excess demand in a distribution system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(1), pages 46-53.
    17. Wiskich, Anthony, 2014. "Implementing a load duration curve of electricity demand in a general equilibrium model," Energy Economics, Elsevier, vol. 45(C), pages 373-380.
    18. Ahmadian Behrooz, Hesam & Boozarjomehry, R. Bozorgmehry, 2017. "Dynamic optimization of natural gas networks under customer demand uncertainties," Energy, Elsevier, vol. 134(C), pages 968-983.
    19. Zhou, Dengji & Zhang, Huisheng & Weng, Shilie, 2014. "A novel prognostic model of performance degradation trend for power machinery maintenance," Energy, Elsevier, vol. 78(C), pages 740-746.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yu, Weichao & Gong, Jing & Song, Shangfei & Huang, Weihe & Li, Yichen & Zhang, Jie & Hong, Bingyuan & Zhang, Ye & Wen, Kai & Duan, Xu, 2019. "Gas supply reliability analysis of a natural gas pipeline system considering the effects of underground gas storages," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. 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).
    3. Chen, Qian & Zuo, Lili & Wu, Changchun & Cao, Yankai & Bu, Yaran & Chen, Feng & Sadiq, Rehan, 2021. "Supply reliability assessment of a gas pipeline network under stochastic demands," Reliability Engineering and System Safety, Elsevier, vol. 209(C).
    4. Chen, Qian & Zuo, Lili & Wu, Changchun & Bu, Yaran & Lu, Yifei & Huang, Yanfei & Chen, Feng, 2020. "Short-term supply reliability assessment of a gas pipeline system under demand variations," Reliability Engineering and System Safety, Elsevier, vol. 202(C).
    5. Zhu, Jianhua & Peng, Yan & Gong, Zhuping & Sun, Yanming & Lai, Chaoan & Wang, Qing & Zhu, Xiaojun & Gan, Zhongxue, 2019. "Dynamic analysis of SNG and PNG supply: The stability and robustness view #," Energy, Elsevier, vol. 185(C), pages 717-729.
    6. Xuejie Li & Yuan Xue & Yuxing Li & Qingshan Feng, 2022. "An Optimization Method for a Compressor Standby Scheme Based on Reliability Analysis," Energies, MDPI, vol. 15(21), pages 1-16, November.
    7. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
    8. Corrado lo Storto, 2019. "An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure," Energies, MDPI, vol. 12(23), pages 1-18, December.
    9. Zhaoming Yang & Qi Xiang & Yuxuan He & Shiliang Peng & Michael Havbro Faber & Enrico Zio & Lili Zuo & Huai Su & Jinjun Zhang, 2023. "Resilience of Natural Gas Pipeline System: A Review and Outlook," Energies, MDPI, vol. 16(17), pages 1-19, August.
    10. Yu, Weichao & Wen, Kai & Min, Yuan & He, Lei & Huang, Weihe & Gong, Jing, 2018. "A methodology to quantify the gas supply capacity of natural gas transmission pipeline system using reliability theory," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 128-141.
    11. 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).
    12. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
    13. Wang, Can & Xie, Haipeng & Bie, Zhaohong & Li, Gengfeng & Yan, Chao, 2021. "Fast supply reliability evaluation of integrated power-gas system based on stochastic capacity network model and importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 208(C).
    14. Yassine Rqiq & Jesus Beyza & Jose M. Yusta & Ricardo Bolado-Lavin, 2020. "Assessing the Impact of Investments in Cross-Border Pipelines on the Security of Gas Supply in the EU," Energies, MDPI, vol. 13(11), pages 1-23, June.
    15. Cabrales, Sergio & Valencia, Carlos & Ramírez, Carlos & Ramírez, Andrés & Herrera, Juan & Cadena, Angela, 2022. "Stochastic cost-benefit analysis to assess new infrastructure to improve the reliability of the natural gas supply," Energy, Elsevier, vol. 246(C).
    16. Senderov, S.M. & Edelev, A.V., 2019. "Formation of a list of critical facilities in the gas transportation system of Russia in terms of energy security," Energy, Elsevier, vol. 184(C), pages 105-112.
    17. Beyza, Jesus & Ruiz-Paredes, Hector F. & Garcia-Paricio, Eduardo & Yusta, Jose M., 2020. "Assessing the criticality of interdependent power and gas systems using complex networks and load flow techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    18. Olfati, Mohammad & Bahiraei, Mehdi & Veysi, Farzad, 2019. "A novel modification on preheating process of natural gas in pressure reduction stations to improve energy consumption, exergy destruction and CO2 emission: Preheating based on real demand," Energy, Elsevier, vol. 173(C), pages 598-609.
    19. 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).
    20. Zhou, Dengji & Jia, Xingyun & Ma, Shixi & Shao, Tiemin & Huang, Dawen & Hao, Jiarui & Li, Taotao, 2022. "Dynamic simulation of natural gas pipeline network based on interpretable machine learning model," Energy, Elsevier, vol. 253(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:162:y:2018:i:c:p:853-870. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.