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New centrality measures for assessing smart grid vulnerabilities and predicting brownouts and blackouts

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  • Chopade, Pravin
  • Bikdash, Marwan

Abstract

This paper proposes mathematical models based on the electrical properties of smart grids for conducting vulnerability analyses and predicting brownouts and blackouts. Definitions of pseudo-Laplacian, pseudo-adjacency and pseudo-degree matrices for smart grids are introduced to specify new centrality measures with electrical interpretations. The centrality measures are used to rank the relative importance of nodes (e.g., generating stations or substations) and edges (e.g., transmission lines or buses) of a graph corresponding to a power grid network and to assess the overall vulnerability of the network. The reliability of using the centrality measures to predict brownouts and blackouts is demonstrated in the face of random and targeted attacks. Monte-Carlo simulations are used to analyze attacks on smart grid networks and to assess the performance of the centrality measures. The simulations employ the IEEE 30-bus, IEEE 57-bus and IEEE 300-bus networks as well as the WSCC 4941-bus real power grid. Every scenario in the Monte-Carlo simulations involves the removal of a subset of buses and performing a complete nonlinear Newton–Raphson power flow analysis to compute the power traffic matrix and the corresponding centrality. The Monte-Carlo simulations conclusively demonstrate that electrical centrality measures based on the power traffic matrix are reliable indicators of the total unsatisfied load ratio (i.e., the load taken offline due to an attack divided by the total load demand). A key result is that, if the total centrality score of the removed buses exceeds a threshold estimated via Monte-Carlo simulation, then a sudden and dramatic jump to a blackout situation is ensured.

Suggested Citation

  • Chopade, Pravin & Bikdash, Marwan, 2016. "New centrality measures for assessing smart grid vulnerabilities and predicting brownouts and blackouts," International Journal of Critical Infrastructure Protection, Elsevier, vol. 12(C), pages 29-45.
    • Handle: RePEc:eee:ijocip:v:12:y:2016:i:c:p:29-45
    DOI: 10.1016/j.ijcip.2015.12.001
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    References listed on IDEAS

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    1. Zio, Enrico & Piccinelli, Roberta, 2010. "Randomized flow model and centrality measure for electrical power transmission network analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(4), pages 379-385.
    2. Zio, Enrico & Podofillini, Luca & Zille, Valérie, 2006. "A combination of Monte Carlo simulation and cellular automata for computing the availability of complex network systems," Reliability Engineering and System Safety, Elsevier, vol. 91(2), pages 181-190.
    3. Bompard, Ettore & Napoli, Roberto & Xue, Fei, 2009. "Analysis of structural vulnerabilities in power transmission grids," International Journal of Critical Infrastructure Protection, Elsevier, vol. 2(1), pages 5-12.
    4. Crucitti, Paolo & Latora, Vito & Marchiori, Massimo & Rapisarda, Andrea, 2003. "Efficiency of scale-free networks: error and attack tolerance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 320(C), pages 622-642.
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    Cited by:

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    3. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    4. Zhao, Chen & Li, Nan & Fang, Dongping, 2018. "Criticality assessment of urban interdependent lifeline systems using a biased PageRank algorithm and a multilayer weighted directed network model," International Journal of Critical Infrastructure Protection, Elsevier, vol. 22(C), pages 100-112.

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