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Correlation analysis of different vulnerability metrics on power grids

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  • Ouyang, Min
  • Pan, Zhezhe
  • Hong, Liu
  • Zhao, Lijing

Abstract

Many scholars have used different metrics to quantify power grid vulnerability in the literature, but how correlated these metrics are is an interesting topic. This paper defines vulnerability as the performance drop of a power grid under a disruptive event, and selects six frequently used performance metrics, including efficiency (E), source–demand considered efficiency (SDE), largest component size (LCS), connectivity level (CL), clustering coefficient (CC), and power supply (PS), to respectively quantify power grid vulnerability V under different node or edge failure probabilities fp and then analyzes the correlation of these six vulnerability metrics. Taking the IEEE 300 power grid as an example, the results show that the flow-based metric VPS, which is equivalent to the important load shed metric in power engineering, has mild correlation with source–demand considered topology-based metrics VSDE and VCL, but weak correlation with other topology-based metrics VE, VLCS and VCC, which do not differentiate source–demand nodes. Similar results are also found in other types of failures, other system operation parameters and other power grids. Hence, one should be careful to use topology-based metrics to quantify the real vulnerability of power grids.

Suggested Citation

  • Ouyang, Min & Pan, Zhezhe & Hong, Liu & Zhao, Lijing, 2014. "Correlation analysis of different vulnerability metrics on power grids," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 396(C), pages 204-211.
  • Handle: RePEc:eee:phsmap:v:396:y:2014:i:c:p:204-211
    DOI: 10.1016/j.physa.2013.10.041
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    References listed on IDEAS

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    1. 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.
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    Cited by:

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    3. Forsberg, Samuel & Thomas, Karin & Bergkvist, Mikael, 2023. "Power grid vulnerability analysis using complex network theory: A topological study of the Nordic transmission grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    4. Espejo, Rafael & Lumbreras, Sara & Ramos, Andres, 2018. "Analysis of transmission-power-grid topology and scalability, the European case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 383-395.
    5. Wang, Shuliang & Guo, Zhaoyang & Huang, Xiaodi & Zhang, Jianhua, 2024. "A three-stage model of quantifying and analyzing power network resilience based on network theory," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    6. Wu, Di & Ma, Feng & Javadi, Milad & Thulasiraman, Krishnaiya & Bompard, Ettore & Jiang, John N., 2017. "A study of the impacts of flow direction and electrical constraints on vulnerability assessment of power grid using electrical betweenness measures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 295-309.
    7. Nicholson, Charles D. & Barker, Kash & Ramirez-Marquez, Jose E., 2016. "Flow-based vulnerability measures for network component importance: Experimentation with preparedness planning," Reliability Engineering and System Safety, Elsevier, vol. 145(C), pages 62-73.
    8. Wang, Shuliang & Gu, Xifeng & Luan, Shengyang & Zhao, Mingwei, 2021. "Resilience analysis of interdependent critical infrastructure systems considering deep learning and network theory," International Journal of Critical Infrastructure Protection, Elsevier, vol. 35(C).
    9. 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.

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