IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i19p6918-d1252197.html
   My bibliography  Save this article

Structural Vulnerability Analysis of Interdependent Electric Power and Natural Gas Systems

Author

Listed:
  • Olabode Amusan

    (Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA)

  • Shuomang Shi

    (Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA)

  • Di Wu

    (Department of Electrical and Computer Engineering, North Dakota State University, Fargo, ND 58105, USA)

  • Haitao Liao

    (Department of Industrial Engineering, University of Arkansas, Fayetteville, AR 72701, USA)

Abstract

The growing use of gas-fired power generators and electricity-driven gas compressors and storage has increased the interdependence between electric power infrastructure and natural gas infrastructure. However, the increasing interdependence may spread the failures from one system to the other, causing subsequent failures in an integrated power and gas system (IPGS). This paper investigates the structural vulnerability of a realistic IPGS based on complex network theory. Different from the existing works with a focus on the static vulnerability analysis for an IPGS, this paper considers both static and dynamic vulnerability analysis. The former focuses on vulnerability analysis under random and selective failures without flow redistribution, while the latter concentrates on vulnerability analysis under cascading failures caused by flow redistribution. Also, different from the existing works with a focus on the IPGS as a whole, we not only analyze the vulnerability of the IPGS but also analyze the vulnerability of the power subsystem (PS) and gas subsystem (GS), in order to understand how the vulnerability of the IPGS is affected by its PS and GS. The analysis results show that (1) if the PS and GS are more susceptible to cascading failures than selective and random failures, the IPGS as a whole is also more vulnerable to cascading failures. (2) There are different dominant factors affecting the IPGS vulnerability under cascading failures and selective failures. Under cascading failures, the GS has a more significant impact on the IPGS vulnerability; under selective failures, the PS has a more important impact on the IPGS vulnerability. (3) The IPGS is more vulnerable to failures on the critical nodes, which are identified from the IPGS as a whole rather than from the individual PS or GS. The results provide insights into the design and planning of IPGSs to improve their overall reliability.

Suggested Citation

  • Olabode Amusan & Shuomang Shi & Di Wu & Haitao Liao, 2023. "Structural Vulnerability Analysis of Interdependent Electric Power and Natural Gas Systems," Energies, MDPI, vol. 16(19), pages 1-13, October.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6918-:d:1252197
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/19/6918/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/19/6918/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dokic, Svjetlana B. & Rajakovic, Nikola Lj., 2019. "Security modelling of integrated gas and electrical power systems by analyzing critical situations and potentials for performance optimization," Energy, Elsevier, vol. 184(C), pages 141-150.
    2. Réka Albert & Hawoong Jeong & Albert-László Barabási, 2000. "Error and attack tolerance of complex networks," Nature, Nature, vol. 406(6794), pages 378-382, July.
    3. Lei, Yunkai & Hou, Kai & Wang, Yue & Jia, Hongjie & Zhang, Pei & Mu, Yunfei & Jin, Xiaolong & Sui, Bingyan, 2018. "A new reliability assessment approach for integrated energy systems: Using hierarchical decoupling optimization framework and impact-increment based state enumeration method," Applied Energy, Elsevier, vol. 210(C), pages 1237-1250.
    4. R. Kinney & P. Crucitti & R. Albert & V. Latora, 2005. "Modeling cascading failures in the North American power grid," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 46(1), pages 101-107, July.
    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. Cumelles, Joel & Lordan, Oriol & Sallan, Jose M., 2021. "Cascading failures in airport networks," Journal of Air Transport Management, Elsevier, vol. 92(C).
    2. Kyu-Min Lee & Jae-Suk Yang & Gunn Kim & Jaesung Lee & Kwang-Il Goh & In-mook Kim, 2011. "Impact of the Topology of Global Macroeconomic Network on the Spreading of Economic Crises," PLOS ONE, Public Library of Science, vol. 6(3), pages 1-11, March.
    3. Trivik Verma & Wendy Ellens & Robert E. Kooij, 2015. "Context-independent centrality measures underestimate the vulnerability of power grids," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 11(1), pages 62-81.
    4. H Jönsson & J Johansson & H Johansson, 2008. "Identifying critical components in technical infrastructure networks," Journal of Risk and Reliability, , vol. 222(2), pages 235-243, June.
    5. Wang, Weiping & Yang, Saini & Hu, Fuyu & Stanley, H. Eugene & He, Shuai & Shi, Mimi, 2018. "An approach for cascading effects within critical infrastructure systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 164-177.
    6. Kashin Sugishita & Yasuo Asakura, 2021. "Vulnerability studies in the fields of transportation and complex networks: a citation network analysis," Public Transport, Springer, vol. 13(1), pages 1-34, March.
    7. Kim, Dong Hwan & Eisenberg, Daniel A. & Chun, Yeong Han & Park, Jeryang, 2017. "Network topology and resilience analysis of South Korean power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 13-24.
    8. Kyu-Min Lee & Jae-Suk Yang & Gunn Kim & Jaesung Lee & Kwang-Il Goh & In-mook Kim, 2010. "Impact of the topology of global macroeconomic network on the spreading of economic crises," Papers 1011.4336, arXiv.org, revised Apr 2011.
    9. Juan Eberhard & Jaime F. Lavin & Alejandro Montecinos-Pearce, 2017. "A Network-Based Dynamic Analysis in an Equity Stock Market," Complexity, Hindawi, vol. 2017, pages 1-16, November.
    10. Hasan, Samiul & Ukkusuri, Satish V., 2011. "A threshold model of social contagion process for evacuation decision making," Transportation Research Part B: Methodological, Elsevier, vol. 45(10), pages 1590-1605.
    11. Ma, Xiangyu & Zhou, Huijie & Li, Zhiyi, 2021. "On the resilience of modern power systems: A complex network perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    12. Lucas Cuadra & Sancho Salcedo-Sanz & Javier Del Ser & Silvia Jiménez-Fernández & Zong Woo Geem, 2015. "A Critical Review of Robustness in Power Grids Using Complex Networks Concepts," Energies, MDPI, vol. 8(9), pages 1-55, August.
    13. Sohn, Insoo, 2019. "A robust complex network generation method based on neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 593-601.
    14. Wang, Kai & Zhang, Bu-han & Zhang, Zhe & Yin, Xiang-gen & Wang, Bo, 2011. "An electrical betweenness approach for vulnerability assessment of power grids considering the capacity of generators and load," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4692-4701.
    15. Pagani, Giuliano Andrea & Aiello, Marco, 2013. "The Power Grid as a complex network: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(11), pages 2688-2700.
    16. Champagne, Claudia, 2014. "The international syndicated loan market network: An “unholy trinity”?," Global Finance Journal, Elsevier, vol. 25(2), pages 148-168.
    17. Xinxin Liu & Nan Li & Feng Liu & Hailin Mu & Longxi Li & Xiaoyu Liu, 2021. "Optimal Design on Fossil-to-Renewable Energy Transition of Regional Integrated Energy Systems under CO 2 Emission Abatement Control: A Case Study in Dalian, China," Energies, MDPI, vol. 14(10), pages 1-25, May.
    18. Sanjeev Goyal & Fernando Vega-Redondo, 2000. "Learning, Network Formation and Coordination," Econometric Society World Congress 2000 Contributed Papers 0113, Econometric Society.
    19. Quayle, A.P. & Siddiqui, A.S. & Jones, S.J.M., 2006. "Preferential network perturbation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 371(2), pages 823-840.
    20. Chen, Lei & Yue, Dong & Dou, Chunxia, 2019. "Optimization on vulnerability analysis and redundancy protection in interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1216-1226.

    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:gam:jeners:v:16:y:2023:i:19:p:6918-:d:1252197. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.