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Dynamically induced cascading failures in power grids

Author

Listed:
  • Benjamin Schäfer

    (Technical University of Dresden
    Max Planck Institute for Dynamics and Self-Organization (MPIDS))

  • Dirk Witthaut

    (Institute for Energy and Climate Research - Systems Analysis and Technology Evaluation (IEK-STE)
    University of Cologne)

  • Marc Timme

    (Technical University of Dresden
    Max Planck Institute for Dynamics and Self-Organization (MPIDS))

  • Vito Latora

    (Queen Mary University of London
    Università di Catania and INFN)

Abstract

Reliable functioning of infrastructure networks is essential for our modern society. Cascading failures are the cause of most large-scale network outages. Although cascading failures often exhibit dynamical transients, the modeling of cascades has so far mainly focused on the analysis of sequences of steady states. In this article, we focus on electrical transmission networks and introduce a framework that takes into account both the event-based nature of cascades and the essentials of the network dynamics. We find that transients of the order of seconds in the flows of a power grid play a crucial role in the emergence of collective behaviors. We finally propose a forecasting method to identify critical lines and components in advance or during operation. Overall, our work highlights the relevance of dynamically induced failures on the synchronization dynamics of national power grids of different European countries and provides methods to predict and model cascading failures.

Suggested Citation

  • Benjamin Schäfer & Dirk Witthaut & Marc Timme & Vito Latora, 2018. "Dynamically induced cascading failures in power grids," Nature Communications, Nature, vol. 9(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04287-5
    DOI: 10.1038/s41467-018-04287-5
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    Cited by:

    1. Brunner, L.G. & Peer, R.A.M. & Zorn, C. & Paulik, R. & Logan, T.M., 2024. "Understanding cascading risks through real-world interdependent urban infrastructure," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    2. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    3. Zhu, Yanpeng & Chen, Lei & Jia, Chun-Xiao & Meng, Fanyuan & Liu, Run-Ran, 2023. "Non-Markovian node fragility in cascading failures on random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    4. Qu, Junyi & Liu, Ying & Tang, Ming & Guan, Shuguang, 2022. "Identification of the most influential stocks in financial networks," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    5. Benjamin Schäfer & Thiemo Pesch & Debsankha Manik & Julian Gollenstede & Guosong Lin & Hans-Peter Beck & Dirk Witthaut & Marc Timme, 2022. "Understanding Braess’ Paradox in power grids," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    6. Hao Wu & Xiangyi Meng & Michael M. Danziger & Sean P. Cornelius & Hui Tian & Albert-László Barabási, 2022. "Fragmentation of outage clusters during the recovery of power distribution grids," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    7. Sagnik Basumallik & Sara Eftekharnejad & Makan Fardad, 2022. "Controlled Islanding under Complete and Partial False Data Injection Attack Uncertainties against Phasor Measurement Units," Energies, MDPI, vol. 15(15), pages 1-27, August.
    8. Zhang, Kaimin & Bai, Libiao & Xie, Xiaoyan & Wang, Chenshuo, 2023. "Modeling of risk cascading propagation in project portfolio network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 612(C).
    9. Weijun Wang & Weisong Peng & Xin Tan & Haoyue Wang & Chenjun Sun, 2018. "Forecasting the Low-Voltage Line Damage Caused by Typhoons in China Based on the Factor Analysis Method and an Improved Gravitational Search Algorithm-Extreme Learning Machine," Energies, MDPI, vol. 11(9), pages 1-12, September.
    10. Lucas Böttcher & Nino Antulov-Fantulin & Thomas Asikis, 2022. "AI Pontryagin or how artificial neural networks learn to control dynamical systems," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    11. Weber, Juliane & Heinrichs, Heidi Ursula & Gillessen, Bastian & Schumann, Diana & Hörsch, Jonas & Brown, Tom & Witthaut, Dirk, 2019. "Counter-intuitive behaviour of energy system models under CO2 caps and prices," Energy, Elsevier, vol. 170(C), pages 22-30.
    12. Gharebaghi, Sina & Chaudhuri, Nilanjan Ray & He, Ting & La Porta, Thomas, 2023. "An approach for fast cascading failure simulation in dynamic models of power systems," Applied Energy, Elsevier, vol. 332(C).
    13. Liang, Yuanyuan & Xia, Yongxiang & Yang, Xu-Hua, 2022. "Hybrid-radius spatial network model and its robustness analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    14. 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).
    15. Ricciardi, Gianmarco & Montagna, Guido & Caldarelli, Guido & Cimini, Giulio, 2023. "Dimensional reduction of solvency contagion dynamics on financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 630(C).
    16. Huang, Yubo & Dong, Hongli & Zhang, Weidong & Lu, Junguo, 2019. "Stability analysis of nonlinear oscillator networks based on the mechanism of cascading failures," Chaos, Solitons & Fractals, Elsevier, vol. 128(C), pages 5-15.
    17. Frasca, Mattia & Gambuzza, Lucia Valentina, 2021. "Control of cascading failures in dynamical models of power grids," Chaos, Solitons & Fractals, Elsevier, vol. 153(P2).
    18. 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).
    19. Yang, Li-xin & Jiang, Jun & Liu, Xiao-jun, 2019. "Impacts of node arrangements on synchronization of a ring oscillatory power network," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 60-65.
    20. Pei, Jianxin & Liu, Ying & Wang, Wei & Gong, Jie, 2021. "Cascading failures in multiplex network under flow redistribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 583(C).
    21. Wang, Shuliang & Guo, Zhaoyang & Huang, Xiaodi & Zhang, Jianhua, 2024. "A three-stage model of quantifying and analyzing power network resilience based on network theory," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    22. Zhou, Jian & Coit, David W. & Felder, Frank A. & Wang, Dali, 2021. "Resiliency-based restoration optimization for dependent network systems against cascading failures," Reliability Engineering and System Safety, Elsevier, vol. 207(C).
    23. Penny Mealy & Pete Barbrook-Johnson & Matthew C Ives & Sugandha Srivastav & Cameron Hepburn, 2023. "Sensitive intervention points: a strategic approach to climate action," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 39(4), pages 694-710.
    24. Ruizi Ma, 2021. "Adaptive Tolerant State Estimation under Model Uncertainty in Power Systems," Energies, MDPI, vol. 14(8), pages 1-15, April.
    25. Hao, Yucheng & Jia, Limin & Wang, Yanhui, 2020. "Robustness of weighted networks with the harmonic closeness against cascading failures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).

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