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Optimal intentional islanding to enhance the robustness of power grid networks

Author

Listed:
  • Pahwa, S.
  • Youssef, M.
  • Schumm, P.
  • Scoglio, C.
  • Schulz, N.

Abstract

Intentional islanding of a power system can be an emergency response for isolating failures that might propagate and lead to major disturbances. Some of the islanding techniques suggested previously do not consider the power flow model; others are designed to minimize load shedding only within the islands. Often these techniques are computationally expensive. We aim to find approaches to partition power grids into islands to minimize the load shedding not only in the region where the failures start, but also in the topological complement of the region. We propose a new constraint programming formulation for optimal islanding in power grid networks. This technique works efficiently for small networks but becomes expensive as size increases. To address the scalability problem, we propose two grid partitioning methods based on modularity, properly modified to take into account the power flow model. They are modifications of the Fast Greedy algorithm and the Bloom algorithm, and are polynomial in running time. We tested these methods on the available IEEE test systems. The Bloom type method is faster than the Fast Greedy type, and can potentially provide results in networks with thousands of nodes. Our methods provide solutions which retain at least 40–50% of the system load. Overall, our methods efficiently balance load shedding and scalability.

Suggested Citation

  • Pahwa, S. & Youssef, M. & Schumm, P. & Scoglio, C. & Schulz, N., 2013. "Optimal intentional islanding to enhance the robustness of power grid networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3741-3754.
  • Handle: RePEc:eee:phsmap:v:392:y:2013:i:17:p:3741-3754
    DOI: 10.1016/j.physa.2013.03.029
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    Citations

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    Cited by:

    1. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    2. Li, Xin & Wu, Haotian & Scoglio, Caterina & Gruenbacher, Don, 2015. "Robust allocation of weighted dependency links in cyber–physical networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 433(C), pages 316-327.
    3. Kong, Hanzhang & Kang, Qinma & Li, Wenquan & Liu, Chao & Kang, Yunfan & He, Hong, 2019. "A hybrid iterated carousel greedy algorithm for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 536(C).
    4. Deng, Zheng-Hong & Qiao, Hong-Hai & Song, Qun & Gao, Li, 2019. "A complex network community detection algorithm based on label propagation and fuzzy C-means," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 519(C), pages 217-226.
    5. Sybil Derrible, 2017. "Urban infrastructure is not a tree: Integrating and decentralizing urban infrastructure systems," Environment and Planning B, , vol. 44(3), pages 553-569, May.
    6. Claudio M. Rocco & Kash Barker & Jose Moronta, 2022. "Determining the best algorithm to detect community structures in networks: application to power systems," Environment Systems and Decisions, Springer, vol. 42(2), pages 251-264, June.
    7. Wang, Jianwei & Li, Yun & Zheng, Qiaofang, 2015. "Cascading load model in interdependent networks with coupled strength," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 430(C), pages 242-253.
    8. Zhang, Kai & Li, Jingzhi & He, Zhubin & Yan, Wanfeng, 2018. "Microgrid energy dispatching for industrial zones with renewable generations and electric vehicles via stochastic optimization and learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 501(C), pages 356-369.
    9. Lucas Cuadra & Sancho Salcedo-Sanz & Javier Del Ser & Silvia Jiménez-Fernández & Zong Woo Geem, 2015. "A Critical Review of Robustness in Power Grids Using Complex Networks Concepts," Energies, MDPI, vol. 8(9), pages 1-55, August.
    10. Woodard, Mark & Marashi, Koosha & Sedigh Sarvestani, Sahra & Hurson, Ali R., 2021. "Survivability evaluation and importance analysis for cyber–physical smart grids," Reliability Engineering and System Safety, Elsevier, vol. 210(C).

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