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Power Grid Structure Performance Evaluation Based on Complex Network Cascade Failure Analysis

Author

Listed:
  • Di Zhang

    (State Key Laboratory of Rail Transit Control and Safety, Beijing Jiaotong University, Beijing 102206, China)

  • Limin Jia

    (State Key Laboratory of Rail Transit Control and Safety, Beijing Jiaotong University, Beijing 102206, China
    China Institute of Energy and Transportation Integration Development, Beijing 102206, China)

  • Jin Ning

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Yujiang Ye

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Hao Sun

    (School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

  • Ruifeng Shi

    (China Institute of Energy and Transportation Integration Development, Beijing 102206, China
    School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

A safe and stable operation power system is very important for the maintenance of national industrial security and social economy. However, with the increasing complexity of the power grid topology and its operation, new challenges in estimating and evaluating the grid structure performance have received significant attention. Complex network theory transfers the power grid to a network with nodes and links, which helps evaluate the system conveniently with a global view. In this paper, we employ the complex network method to address the cascade failure process and grid structure performance assessment simultaneously. Firstly, a grid cascade failure model based on network topology and power system characteristics is constructed. Then, a set of performance evaluation indicators, including invulnerability, reliability, and vulnerability, is proposed based on the actual functional properties of the grid by renewing the power-weighted degree, medium, and clustering coefficients according to the network cascade failure. Finally, a comprehensive network performance evaluation index, which combines the invulnerability, reliability, and vulnerability indicators with an entropy-based objective weighting method, is put forward in this study. In order to confirm the approach’s efficacy, an IEEE-30 bus system is employed for a case study. Numerical results show that the weighted integrated index with a functional network could better evaluate the power grid performance than the unweighted index with a topology network, which demonstrates and validates the effectiveness of the method proposed in this paper.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:2:p:990-:d:1037091
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    References listed on IDEAS

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    1. Yushu Sun & Xisheng Tang & Guowei Zhang & Fufeng Miao & Ping Wang, 2017. "Dynamic Power Flow Cascading Failure Analysis of Wind Power Integration with Complex Network Theory," Energies, MDPI, vol. 11(1), pages 1-15, December.
    2. Peng, Xingzhao & Yao, Hong & Du, Jun & Wang, Zhe & Ding, Chao, 2015. "Invulnerability of scale-free network against critical node failures based on a renewed cascading failure model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 69-77.
    3. Peng, Hao & Kan, Zhe & Zhao, Dandan & Han, Jianmin & Lu, Jianfeng & Hu, Zhaolong, 2018. "Reliability analysis in interdependent smart grid systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 50-59.
    4. Azzolin, Alberto & Dueñas-Osorio, Leonardo & Cadini, Francesco & Zio, Enrico, 2018. "Electrical and topological drivers of the cascading failure dynamics in power transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 196-206.
    5. 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.
    6. Li, Jian & Shi, Congling & Chen, Changkun & Dueñas-Osorio, Leonardo, 2018. "A cascading failure model based on AC optimal power flow: Case study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 313-323.
    7. Li, Kaiwen & Liu, Kai & Wang, Ming, 2021. "Robustness of the Chinese power grid to cascading failures under attack and defense strategies," International Journal of Critical Infrastructure Protection, Elsevier, vol. 33(C).
    8. Xiao, Feng & Li, Jin & Wei, Bo, 2022. "Cascading failure analysis and critical node identification in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    9. Jun Guo & Tao Feng & Zelin Cai & Xianglong Lian & Wenhu Tang, 2020. "Vulnerability Assessment for Power Transmission Lines under Typhoon Weather Based on a Cascading Failure State Transition Diagram," Energies, MDPI, vol. 13(14), pages 1-15, July.
    10. Jiang, Zhong-Yuan & Liu, Zhi-Quan & He, Xuan & Ma, Jian-Feng, 2018. "Cascade phenomenon against subsequent failures in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 499(C), pages 472-480.
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