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Power system cascading risk assessment based on complex network theory

Author

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  • Wang, Zhuoyang
  • Hill, David J.
  • Chen, Guo
  • Dong, Zhao Yang

Abstract

When a single failure occurs in a vulnerable part of a power system, this may cause a large area cascading event. Therefore, an advanced method that can assess the risks during cascading events is needed. In this paper, an improved complex network model for power system risk assessment is proposed. Risk is defined by consequence and probability of the failures in this model, which are affected by both power factors and network structure. Compared with existing risk assessment models, the proposed one can evaluate the risk of the system comprehensively during a cascading event by combining the topological and electrical information. A new cascading event simulation module is adopted to identify the power grid cascading chain from a system-level view. In addition, simulations are investigated on the IEEE 14 bus system and IEEE 39 bus system respectively to illustrate the performance of the proposed module. The simulation results demonstrate that the proposed method is effective in a power grid risk assessment during cascading event.

Suggested Citation

  • Wang, Zhuoyang & Hill, David J. & Chen, Guo & Dong, Zhao Yang, 2017. "Power system cascading risk assessment based on complex network theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 532-543.
    • Handle: RePEc:eee:phsmap:v:482:y:2017:i:c:p:532-543
    DOI: 10.1016/j.physa.2017.04.031
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    References listed on IDEAS

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    1. Wang, Zhuoyang & Chen, Guo & Hill, David J. & Dong, Zhao Yang, 2016. "A power flow based model for the analysis of vulnerability in power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 460(C), pages 105-115.
    2. Levitin, Gregory & Xie, Min & Zhang, Tieling, 2007. "Reliability of fault-tolerant systems with parallel task processing," European Journal of Operational Research, Elsevier, vol. 177(1), pages 420-430, February.
    3. Chen, Guo & Dong, Zhao Yang & Hill, David J. & Zhang, Guo Hua, 2009. "An improved model for structural vulnerability analysis of power networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(19), pages 4259-4266.
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    Citations

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    Cited by:

    1. Beyza, Jesus & Ruiz-Paredes, Hector F. & Garcia-Paricio, Eduardo & Yusta, Jose M., 2020. "Assessing the criticality of interdependent power and gas systems using complex networks and load flow techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
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    3. Wang, Zhuoyang & Chen, Guo & Liu, Long & Hill, David J., 2020. "Cascading risk assessment in power-communication interdependent networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
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    5. 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).
    6. Jiang, Yuan & Yan, Yuwei & Hong, Cheng & Yang, Songqing & Yu, Rongbin & Dai, Jiyang, 2022. "Multidirectional recovery strategy against failure," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
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    8. Wang, Shuliang & Lv, Wenzhuo & Zhang, Jianhua & Luan, Shengyang & Chen, Chen & Gu, Xifeng, 2021. "Method of power network critical nodes identification and robustness enhancement based on a cooperative framework," Reliability Engineering and System Safety, Elsevier, vol. 207(C).

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