IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v482y2017icp532-543.html
   My bibliography  Save this article

Power system cascading risk assessment based on complex network theory

Author

Listed:
  • Wang, Zhuoyang
  • Hill, David J.
  • Chen, Guo
  • Dong, Zhao Yang

Abstract

When a single failure occurs in a vulnerable part of a power system, this may cause a large area cascading event. Therefore, an advanced method that can assess the risks during cascading events is needed. In this paper, an improved complex network model for power system risk assessment is proposed. Risk is defined by consequence and probability of the failures in this model, which are affected by both power factors and network structure. Compared with existing risk assessment models, the proposed one can evaluate the risk of the system comprehensively during a cascading event by combining the topological and electrical information. A new cascading event simulation module is adopted to identify the power grid cascading chain from a system-level view. In addition, simulations are investigated on the IEEE 14 bus system and IEEE 39 bus system respectively to illustrate the performance of the proposed module. The simulation results demonstrate that the proposed method is effective in a power grid risk assessment during cascading event.

Suggested Citation

  • Wang, Zhuoyang & Hill, David J. & Chen, Guo & Dong, Zhao Yang, 2017. "Power system cascading risk assessment based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 532-543.
    • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:532-543
    DOI: 10.1016/j.physa.2017.04.031
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437117303345
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2017.04.031?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. Wang, Zhuoyang & Chen, Guo & Hill, David J. & Dong, Zhao Yang, 2016. "A power flow based model for the analysis of vulnerability in power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 105-115.
    2. Levitin, Gregory & Xie, Min & Zhang, Tieling, 2007. "Reliability of fault-tolerant systems with parallel task processing," European Journal of Operational Research, Elsevier, vol. 177(1), pages 420-430, February.
    3. Chen, Guo & Dong, Zhao Yang & Hill, David J. & Zhang, Guo Hua, 2009. "An improved model for structural vulnerability analysis of power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4259-4266.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. Tu, Haicheng & Xia, Yongxiang & Wu, Jiajing & Zhou, Xiang, 2019. "Robustness assessment of cyber–physical systems with weak interdependency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 9-17.
    3. Feng, Jian Rui & Zhao, Meng-ke & Lu, Shou-xiang, 2024. "Accident spread and risk propagation mechanism in complex industrial system network," Reliability Engineering and System Safety, Elsevier, vol. 244(C).
    4. 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).
    5. Jiang, Yuan & Yan, Yuwei & Hong, Cheng & Yang, Songqing & Yu, Rongbin & Dai, Jiyang, 2022. "Multidirectional recovery strategy against failure," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    6. Gaogeng Zhu & Guoming Chen & Jingyu Zhu & Xiangkun Meng & Xinhong Li, 2022. "Modeling the Evolution of Major Storm-Disaster-Induced Accidents in the Offshore Oil and Gas Industry," IJERPH, MDPI, vol. 19(12), pages 1-27, June.
    7. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    8. Wang, Zhuoyang & Chen, Guo & Liu, Long & Hill, David J., 2020. "Cascading risk assessment in power-communication interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    9. Jing, Ke & Du, Xinru & Shen, Lixin & Tang, Liang, 2019. "Robustness of complex networks: Cascading failure mechanism by considering the characteristics of time delay and recovery strategy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).

    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. Wang, Zhuoyang & Chen, Guo & Hill, David J. & Dong, Zhao Yang, 2016. "A power flow based model for the analysis of vulnerability in power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 105-115.
    2. Abedi, Amin & Gaudard, Ludovic & Romerio, Franco, 2019. "Review of major approaches to analyze vulnerability in power system," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 153-172.
    3. 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).
    4. Mahmoud Saleh & Yusef Esa & Ahmed Mohamed, 2018. "Applications of Complex Network Analysis in Electric Power Systems," Energies, MDPI, vol. 11(6), pages 1-16, May.
    5. 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).
    6. Wang, Yong & Li, Lin & Huang, Shuhong & Chang, Qing, 2012. "Reliability and covariance estimation of weighted k-out-of-n multi-state systems," European Journal of Operational Research, Elsevier, vol. 221(1), pages 138-147.
    7. Ren, Hai-Peng & Song, Jihong & Yang, Rong & Baptista, Murilo S. & Grebogi, Celso, 2016. "Cascade failure analysis of power grid using new load distribution law and node removal rule," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 442(C), pages 239-251.
    8. 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.
    9. Nasiruzzaman, A.B.M. & Pota, H.R. & Akter, Most. Nahida, 2014. "Vulnerability of the large-scale future smart electric power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 413(C), pages 11-24.
    10. Fei Xue & Yingyu Xu & Huaiying Zhu & Shaofeng Lu & Tao Huang & Jinling Zhang, 2017. "Structural Evaluation for Distribution Networks with Distributed Generation Based on Complex Network," Complexity, Hindawi, vol. 2017, pages 1-10, October.
    11. Ma, Tian-Lin & Yao, Jian-Xi & Qi, Cheng & Zhu, Hong-Lu & Sun, Yu-Shu, 2013. "Non-monotonic increase of robustness with capacity tolerance in power grids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(21), pages 5516-5524.
    12. Wang, Jing & Zuo, Wangda & Rhode-Barbarigos, Landolf & Lu, Xing & Wang, Jianhui & Lin, Yanling, 2019. "Literature review on modeling and simulation of energy infrastructures from a resilience perspective," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 360-373.
    13. Youba Nait Belaid & Patrick Coudray & José Sanchez-Torres & Yi-Ping Fang & Zhiguo Zeng & Anne Barros, 2021. "Resilience Quantification of Smart Distribution Networks—A Bird’s Eye View Perspective," Energies, MDPI, vol. 14(10), pages 1-29, May.
    14. 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.
    15. 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.
    16. Koç, Yakup & Warnier, Martijn & Mieghem, Piet Van & Kooij, Robert E. & Brazier, Frances M.T., 2014. "The impact of the topology on cascading failures in a power grid model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 402(C), pages 169-179.
    17. Chen, Chong & Zhou, Xuan & Li, Zhuo & He, Zhiheng & Li, Zhengtian & Lin, Xiangning, 2018. "Novel complex network model and its application in identifying critical components of power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 316-329.
    18. 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).
    19. Zhang, Yun & Fang, Zhihui & Xu, Zhengguo, 2019. "An optimal design of multi-protocol label switching networks achieving reliability requirements," Reliability Engineering and System Safety, Elsevier, vol. 182(C), pages 133-141.
    20. Jian-Yong Wang & Zhen Tian & Xu Zhu & Naif D. Alotaibi, 2017. "Finite-Time Consensus with a Time-Varying Reference State and Switching Topology," Complexity, Hindawi, vol. 2017, pages 1-9, June.

    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:phsmap:v:482:y:2017:i:c:p:532-543. 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/physica-a-statistical-mechpplications/ .

    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.