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Cascading Failure Based on Load Redistribution of a Smart Grid with Different Coupling Modes

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10860)

Abstract

As one of the most important properties of the power grid, the voltage load plays an important role in the cascading failure of the smart grid and load redistribution can accelerate the speed of the failure by triggering more nodes to overload and fail. The subnet structure and different coupling modes also affect the robustness of the smart grid. However, the research on the effect of load, subnet structure and coupling mode on the cascading failure of the smart grid is still rare. In this paper, the smart grid with two-way coupling link consists of a power grid with small world topology and a communication network with scale-free topology. An improved load-capacity model is applied to overload-induced failure in the power grid and node importance (NI) is used as an evaluation index to assess the effect of nodes on the power grid and communication network. We propose three kinds of coupling modes based on NI of nodes between the cyber and physical subnets, i.e., Random Coupling in Subnets (RCIS), Assortative Coupling in Subnets (ACIS) and Disassortative Coupling in Subnets (DCIS). In order to improve the robustness of the smart grid, a cascading failure model based on load redistribution is proposed to analyze the influence of different coupling modes on the cascading failure of the smart grid under both a targeted attack and random attack. Some findings are summarized as: (I) The robustness of the smart grid is improved by increasing the tolerance \(\alpha \). (II) ACIS applied to the bottom-up coupling link is more beneficial in enhancing the robustness of the smart grid than DCIS and RCIS, regardless of a targeted attack or random attack.

Keywords

Cascading failure Load redistribution algorithm Node importance Two-way coupling relationship 

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of ComputerNational University of Defense TechnologyChangshaChina
  2. 2.Department of Electronic Information and Electrical EngineeringChangsha UniversityChangshaChina

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