Electric Power Grid Invulnerability Under Intentional Edge-Based Attacks

  • Yixia Li
  • Shudong LiEmail author
  • Yanshan Chen
  • Peiyan He
  • Xiaobo Wu
  • Weihong HanEmail author
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1123)


Power Grid as a kind of complex network is particularly important for every country, even brings huge losses if the power grid suffered from natural or even artificial attacks. Therefore, how to investigate the vulnerable edges of the power grid with under attacks has become an important proposition. In this paper, taking the US power grid as an example, by deliberately deleting some percent of edges according to different strategies which represents different attacks apparently, we calculate the collapse degree of the attacked network by three metrics (The largest connected component G, efficiency E, and average distance L). We found that, under intentional attack on the edges with higher betweenness centrality and the ones with larger multiplication of node betweenness centrality, the US power grid is inferior in invulnerability. The methods used in this paper could be used to identify the vulnerable edges of complex networks, especially for the key infrastructures.


Power grid Invulnerability Edge-based attack Betweenness centrality 



This research was funded by NSFC (No. 61672020, U1803263, U1636215), (No.18-163-15-ZD-002-003-01), National Key Research and Development Program of China (No. 2019QY1406), Key R&D Program of Guangdong Province(No. 2019B010136003, 2019B010137004), A Project of Shandong Province Higher Educational Science and Technology Program (No. J16LN61), and the National Key research and Development Plan (No. 2018YFB1800701, No. 2018YFB0803504, and No. 2018YEB1004003).


  1. 1.
    Arianos, S., Bompard, E., Carbone, A., et al.: Power grids vulnerability: a complex network approach. Chaos 19(1), 175 (2009)CrossRefGoogle Scholar
  2. 2.
    Koç, Y., Warnier, M., Van Mieghem, P., et al.: The impact of the topology on cascading failures in electric power grids. Comput. Sci. (2013)Google Scholar
  3. 3.
    Kadloor, S., Santhi, N.: Understanding cascading failures in power grids. Comput. Sci. 28(5), 24–30 (2012)Google Scholar
  4. 4.
    Wang, X., Koc, Y., Robert, E., et al: A network approach for power grid robustness against cascading failures. In: 2015 7th International Workshop on Reliable Networks Design and Modeling (RNDM). IEEE (2015)Google Scholar
  5. 5.
    Simonsen, I., Buzna, L., Peters, K., et al.: Transient dynamics increasing network vulnerability to cascading failures. Phys. Rev. Lett. 100(21), 218701 (2008)CrossRefGoogle Scholar
  6. 6.
    Buldyrev, S.V., Parshani, R., Paul, G., et al.: Catastrophic cascade of failures in interdependent networks. Nature 464, 1025–1028 (2010)Google Scholar
  7. 7.
    Schaub, M.T., Lehmann, J., Yaliraki, S.N., et al.: Structure of complex networks: quantifying edge-to-edge relations by failure-induced flow redistribution. Netw. Sci. 2(01), 66–89 (2014)Google Scholar
  8. 8.
    Yong, Y., Yu, F.: Case study on survivability of urban rail transit network. Logistics Technol. 37(12), 58–62 (2018)Google Scholar
  9. 9.
    Sun, Y., Yang, D., Meng, L., et al.: Universal framework for vulnerability assessment of power grid based on complex networks. In: The 30th Chinese Control and Decision Conference (2018)Google Scholar
  10. 10.
    Runze, W., Wanxu, W., Li, L., Bing, F., Liangrui, T.: Topology diagnosis of power communication network based on node influence. Power Syst. Prot. Control 47(10), 147–155 (2019)Google Scholar
  11. 11.
    Riondato, M., Kornaropoulos, E.M.: Fast approximation of betweenness centrality through sampling. Data Min. Knowl. Discov. 30(2), 438–475 (2016). (S1384-5810)Google Scholar
  12. 12.
    Segarra, S., Ribeiro, A.: Stability and continuity of centrality measures in weighted graphs. IEEE Trans. Sig. Process. 64(3), 543–555 (2016)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yun, L.: Node importance rank by attribute reduction set evaluation and application. Shandong Normal University (2018)Google Scholar
  14. 14.
    Li, S., Wu, X., Zhu, C., Li, A., Li, L., Jia, Y.: Vulnerability of complex networks under multiple node-based attacks. In: IET International Conference on Information & Communications Technologies (2013)Google Scholar
  15. 15.
    Ruan, Y., Lao, S.-Y., Wang, J., Bai, L., Chen, L.-D.: Node importance measurement based on neighborhood similarity in complex network. Acta Phys. Sin. 66(03), 371–379 (2017)Google Scholar
  16. 16.
    Li, C., Wei, L., Lu, T., Gao, W.: Invulnerability simulation analysis of compound traffic network in urban agglomeration. J. Syst. Simul. 30(02), 489–496 (2018)Google Scholar
  17. 17.
    Sun, K., Han, Z.X., Cao, Y.J.: Review on models of cascading failures in complex power grid. Power Syst. Technol. 13, 1–9 (2005)Google Scholar
  18. 18.
    Watts, D.J., Strogatz, S.H.: Collective dynamics of ‘small-world’ networks. Nature 393(6684), 440 (1998)Google Scholar
  19. 19.
    Yuejin, T., Xin, L., Jun, W., Hongzhong, D.: Main scientific problems for the invulnerability research of complex networks. In: The 15th Chinese Congress of Systems Science and Systems Engineering Proceeding. Systems Engineering Society of China (2008)Google Scholar
  20. 20.
    Verma, T., Ellens, W., Kooij, R.E.: Context-independent centrality measures underestimate the vulnerability of power grids. Int. J. Crit. Infrastruct. 11(1), 62 (2013)Google Scholar
  21. 21.
    Tian, Z., et al.: Real time lateral movement detection based on evidence reasoning network for edge computing environment. IEEE Trans. Ind. Inform. 15(7), 4285–4294 (2019)Google Scholar
  22. 22.
    Tian, Z., Su, S., Shi, W., Du, X., Guizani, M., Yu, X.: A data-driven method for future internet route decision modeling. Future Gener. Comput. Syst. 95, 212–220 (2019)Google Scholar
  23. 23.
    Li, S., Li, L., Yang, Y., Luo, Q.: Revealing the process of edge-based-attack cascading failures. Nonlinear Dyn. 69(3), 837–845 (2012)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Li, S., Li, L., Jia, Y., Liu, X., Yang, Y.: Identifying vulnerable nodes of complex networks in cascading failures induced by node-based attacks. Math. Probl. Eng. 2013, 938 (2013)Google Scholar
  25. 25.
    Zhao, D., Li, L., Peng, H., Luo, Q., Yang, Y.: Multiple routes transmitted epidemics on multiplex networks. Phys. Lett. A 378, 770–776 (2014)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.School of Economics and StatisticsGuangzhou UniversityGuangzhouChina
  2. 2.Cyberspace Institute of Advance TechnologyGuangzhou UniversityGuangzhouChina
  3. 3.School of Computer Science and Cyber EngineeringGuangzhou UniversityGuangzhouChina

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