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Correlating empirical data and extended topological measures in power grid networks

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  • Lingen Luo
  • Marti Rosas-Casals

Abstract

Power grids are considered complex networks. Their structure and dynamics have been thoroughly studied and many topological measures have been used to classify them, evaluate their behaviour or model their response to malfunctions. Results have been theoretical and correlations between real dynamical behaviour (i.e., major events) and structural measures have not yet been found. New electrically modified topological measures have been recently used to quantify the ability of a network in sustaining its functions. Here, we present a first attempt to correlate these new measures with real malfunction data for some European power transmission grids. Similar behaviour is found, in terms of robustness to selected attacks to buses, between different networks. These behaviours can be correlated with similar probability distributions of major events, identifying similar dynamical response among topologically similar grids. This would raise hopes in finding a more meaningful linkage between structural measures and the real dynamical output of a grid.

Suggested Citation

  • Lingen Luo & Marti Rosas-Casals, 2015. "Correlating empirical data and extended topological measures in power grid networks," International Journal of Critical Infrastructures, Inderscience Enterprises Ltd, vol. 11(1), pages 82-96.
    • Handle: RePEc:ids:ijcist:v:11:y:2015:i:1:p:82-96
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    References listed on IDEAS

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    1. Quheng Deng & Björn Gustafsson & Shi Li, 2013. "Intergenerational Income Persistence in Urban China," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 59(3), pages 416-436, September.
    2. Kuo-Ping Lin & Ching-Lin Lin & Yu-Ming Lu & Ping-Feng Pai, 2013. "Rule Generation Based on Novel Two-Stage Model," Diversity, Technology, and Innovation for Operational Competitiveness: Proceedings of the 2013 International Conference on Technology Innovation and Industrial Management,, ToKnowPress.
    3. 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.
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    Cited by:

    1. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    2. 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.
    3. 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.
    4. 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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