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Research on the Structural Characteristics of Transmission Grid Based on Complex Network Theory

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  • Jinli Zhao
  • Hongshan Zhou
  • Bo Chen
  • Peng Li

Abstract

Reasonable and strong structure is an important foundation for the smart transmission grid. For vigorously promoting construction of the smart grid, it is of great significance to have a thorough understanding of the complex structural characteristics of the power grid. The structural characteristics of several actual large-scale power grids of China are studied in this paper based on the complex network theory. Firstly, the topology-based network model of power grid is recalled for analyzing the statistical characteristic parameters. The result demonstrated that although some statistical characteristic parameters could reflect the topological characteristics of power grid from different ways, they have certain limitation in representing the electrical characteristics of power grid. Subsequently, the network model based on the electrical distance is established considering the limitation of topology-based model, which reflects that current and voltage distribution in the power grid are subject to Ohm's Law and Kirchhoff's Law. Comparing with the topology-based model, the electrical distance-based model performs better in reflecting the natural electrical characteristic structure of power grid, especially intuitive and effective in analyzing clustering characteristics and agglomeration characteristics of power grid. These two models could complement each other.

Suggested Citation

  • Jinli Zhao & Hongshan Zhou & Bo Chen & Peng Li, 2014. "Research on the Structural Characteristics of Transmission Grid Based on Complex Network Theory," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-12, April.
  • Handle: RePEc:hin:jnljam:261798
    DOI: 10.1155/2014/261798
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    Cited by:

    1. Corrado lo Storto, 2019. "An SNA-DEA Prioritization Framework to Identify Critical Nodes of Gas Networks: The Case of the US Interstate Gas Infrastructure," Energies, MDPI, vol. 12(23), pages 1-18, December.

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