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Robustness analysis of complex networks with power decentralization strategy via flow-sensitive centrality against cascading failures

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  • Guo, Wenzhang
  • Wang, Hao
  • Wu, Zhengping

Abstract

Most existing cascading failure mitigation strategy of power grids based on complex network ignores the impact of electrical characteristics on dynamic performance. In this paper, the robustness of the power grid under a power decentralization strategy is analysed through cascading failure simulation based on AC flow theory. The flow-sensitive (FS) centrality is introduced by integrating topological features and electrical properties to help determine the siting of the generation nodes. The simulation results of the IEEE-bus systems show that the flow-sensitive centrality method is a more stable and accurate approach and can enhance the robustness of the network remarkably. Through the study of the optimal flow-sensitive centrality selection for different networks, we find that the robustness of the network with obvious small-world effect depends more on contribution of the generation nodes detected by community structure, otherwise, contribution of the generation nodes with important influence on power flow is more critical. In addition, community structure plays a significant role in balancing the power flow distribution and further slowing the propagation of failures. These results are useful in power grid planning and cascading failure prevention.

Suggested Citation

  • Guo, Wenzhang & Wang, Hao & Wu, Zhengping, 2018. "Robustness analysis of complex networks with power decentralization strategy via flow-sensitive centrality against cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 186-199.
    • Handle: RePEc:eee:phsmap:v:494:y:2018:i:c:p:186-199
    DOI: 10.1016/j.physa.2017.12.002
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    References listed on IDEAS

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    Cited by:

    1. Di Zhang & Limin Jia & Jin Ning & Yujiang Ye & Hao Sun & Ruifeng Shi, 2023. "Power Grid Structure Performance Evaluation Based on Complex Network Cascade Failure Analysis," Energies, MDPI, vol. 16(2), pages 1-15, January.
    2. Salcedo-Sanz, S. & Cuadra, L., 2019. "Quasi scale-free geographically embedded networks over DLA-generated aggregates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1286-1305.
    3. Wang, Shuliang & Zhang, Jianhua & Yue, Xin, 2018. "Multiple robustness assessment method for understanding structural and functional characteristics of the power network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 510(C), pages 261-270.
    4. Ma, Xiangyu & Zhou, Huijie & Li, Zhiyi, 2021. "On the resilience of modern power systems: A complex network perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    5. Shen, Yi & Song, Guohao & Xu, Huangliang & Xie, Yuancheng, 2020. "Model of node traffic recovery behavior and cascading congestion analysis in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).

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