IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i6p1355-d149073.html
   My bibliography  Save this article

Critical Lines Identification for Skeleton-Network of Power Systems under Extreme Weather Conditions Based on the Modified VIKOR Method

Author

Listed:
  • Chang Han

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yuxuan Zhao

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Zhenzhi Lin

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Yi Ding

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Li Yang

    (School of Electrical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Guanqiang Lin

    (Electric Power Dispatching and Control Center, Huizhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Huizhou 516000, China)

  • Tianwen Mo

    (Electric Power Dispatching and Control Center, Huizhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Huizhou 516000, China)

  • Xiaojun Ye

    (Electric Power Dispatching and Control Center, Huizhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Huizhou 516000, China)

Abstract

Identifying and preferentially reinforcing critical lines for skeleton-network of power systems is significant in improving the secure and stable operation of power systems under extreme weather conditions. Under this background, in this paper, six indexes are first presented for identifying critical lines for skeleton-network with the power elements’ parameters and the impact of extreme weather conditions, the network topology and the operation state of power systems considered. Then, the modified Vise Kriterijumska Optimizacija I Kompromisno Resenje in Serbian (VIKOR) method, in which the synthetic weights of indexes determined by the combination weighting method are adopted, is utilized to identify the importance degrees of lines in a given power system. Both the overall performance and the outstanding individual performance of lines are considered, which is beneficial for the critical lines identification for skeleton-network. Finally, the proposed multi-indexes and methods are applied to part of the actual Guangdong power system in China. The numerical results are compared with those obtained by single-attribute and multi-attribute evaluation methods and other evaluation methods.

Suggested Citation

  • Chang Han & Yuxuan Zhao & Zhenzhi Lin & Yi Ding & Li Yang & Guanqiang Lin & Tianwen Mo & Xiaojun Ye, 2018. "Critical Lines Identification for Skeleton-Network of Power Systems under Extreme Weather Conditions Based on the Modified VIKOR Method," Energies, MDPI, vol. 11(6), pages 1-18, May.
  • Handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1355-:d:149073
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/6/1355/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/6/1355/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jun Dong & Rong Li & Hui Huang, 2018. "Performance Evaluation of Residential Demand Response Based on a Modified Fuzzy VIKOR and Scalable Computing Method," Energies, MDPI, vol. 11(5), pages 1-27, April.
    2. R. Kinney & P. Crucitti & R. Albert & V. Latora, 2005. "Modeling cascading failures in the North American power grid," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 46(1), pages 101-107, July.
    3. Jianxi Gao & Xueming Liu & Daqing Li & Shlomo Havlin, 2015. "Recent Progress on the Resilience of Complex Networks," Energies, MDPI, vol. 8(10), pages 1-24, October.
    4. Ziqi Wang & Jinghan He & Alexandru Nechifor & Dahai Zhang & Peter Crossley, 2017. "Identification of Critical Transmission Lines in Complex Power Networks," Energies, MDPI, vol. 10(9), pages 1-19, August.
    5. Nayyar Hussain Mirjat & Mohammad Aslam Uqaili & Khanji Harijan & Mohd Wazir Mustafa & Md. Mizanur Rahman & M. Waris Ali Khan, 2018. "Multi-Criteria Analysis of Electricity Generation Scenarios for Sustainable Energy Planning in Pakistan," Energies, MDPI, vol. 11(4), pages 1-33, March.
    6. Huiru Zhao & Nana Li, 2016. "Optimal Siting of Charging Stations for Electric Vehicles Based on Fuzzy Delphi and Hybrid Multi-Criteria Decision Making Approaches from an Extended Sustainability Perspective," Energies, MDPI, vol. 9(4), pages 1-22, April.
    7. Lin He & Chang-Ling Li & Qing-Yun Nie & Yan Men & Hai Shao & Jiang Zhu, 2017. "Core Abilities Evaluation Index System Exploration and Empirical Study on Distributed PV-Generation Projects," Energies, MDPI, vol. 10(12), pages 1-18, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhenzhi Lin & Yuxuan Zhao & Shengyuan Liu & Fushuan Wen & Yi Ding & Li Yang & Chang Han & Hao Zhou & Hongwei Wu, 2018. "A New Indicator of Transient Stability for Controlled Islanding of Power Systems: Critical Islanding Time," Energies, MDPI, vol. 11(11), pages 1-10, November.
    2. Jun Dong & Dongxue Wang & Dongran Liu & Palidan Ainiwaer & Linpeng Nie, 2019. "Operation Health Assessment of Power Market Based on Improved Matter-Element Extension Cloud Model," Sustainability, MDPI, vol. 11(19), pages 1-25, October.
    3. Mir Sayed Shah Danish & Tomonobu Senjyu & Sayed Mir Shah Danish & Najib Rahman Sabory & Narayanan K & Paras Mandal, 2019. "A Recap of Voltage Stability Indices in the Past Three Decades," Energies, MDPI, vol. 12(8), pages 1-18, April.
    4. Hongbo Shao & Yubin Mao & Yongmin Liu & Wanxun Liu & Sipei Sun & Peng Jia & Fufeng Miao & Li Yang & Chang Han & Bo Zhang, 2018. "A Three-Stage Procedure for Controlled Islanding to Prevent Wide-Area Blackouts," Energies, MDPI, vol. 11(11), pages 1-15, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Fauzan Hanif Jufri & Jun-Sung Kim & Jaesung Jung, 2017. "Analysis of Determinants of the Impact and the Grid Capability to Evaluate and Improve Grid Resilience from Extreme Weather Event," Energies, MDPI, vol. 10(11), pages 1-17, November.
    2. Kim, Dong Hwan & Eisenberg, Daniel A. & Chun, Yeong Han & Park, Jeryang, 2017. "Network topology and resilience analysis of South Korean power grid," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 13-24.
    3. Champagne, Claudia, 2014. "The international syndicated loan market network: An “unholy trinity”?," Global Finance Journal, Elsevier, vol. 25(2), pages 148-168.
    4. Xiao‐Bing Hu & Hang Li & XiaoMei Guo & Pieter H. A. J. M. van Gelder & Peijun Shi, 2019. "Spatial Vulnerability of Network Systems under Spatially Local Hazards," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 162-179, January.
    5. Panyam, Varuneswara & Huang, Hao & Davis, Katherine & Layton, Astrid, 2019. "Bio-inspired design for robust power grid networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    6. Lei Che & Jiangang Xu & Hong Chen & Dongqi Sun & Bao Wang & Yunuo Zheng & Xuedi Yang & Zhongren Peng, 2022. "Evaluation of the Spatial Effect of Network Resilience in the Yangtze River Delta: An Integrated Framework for Regional Collaboration and Governance under Disruption," Land, MDPI, vol. 11(8), pages 1-20, August.
    7. Danijela Tuljak-Suban & Patricija Bajec, 2022. "A Hybrid DEA Approach for the Upgrade of an Existing Bike-Sharing System with Electric Bikes," Energies, MDPI, vol. 15(21), pages 1-23, October.
    8. Johansson, Jonas & Hassel, Henrik, 2010. "An approach for modelling interdependent infrastructures in the context of vulnerability analysis," Reliability Engineering and System Safety, Elsevier, vol. 95(12), pages 1335-1344.
    9. Mazur, Christoph & Hoegerle, Yannick & Brucoli, Maria & van Dam, Koen & Guo, Miao & Markides, Christos N. & Shah, Nilay, 2019. "A holistic resilience framework development for rural power systems in emerging economies," Applied Energy, Elsevier, vol. 235(C), pages 219-232.
    10. Muhammad Riaz & Wojciech Sałabun & Hafiz Muhammad Athar Farid & Nawazish Ali & Jarosław Wątróbski, 2020. "A Robust q-Rung Orthopair Fuzzy Information Aggregation Using Einstein Operations with Application to Sustainable Energy Planning Decision Management," Energies, MDPI, vol. 13(9), pages 1-39, May.
    11. Muhammad Yaseen Bhutto & Yasir Ali Soomro & Hailan Yang, 2022. "Extending the Theory of Planned Behavior: Predicting Young Consumer Purchase Behavior of Energy-Efficient Appliances (Evidence From Developing Economy)," SAGE Open, , vol. 12(1), pages 21582440221, February.
    12. Yi Liu & Senbin Yu & Chaoyang Zhang & Peiran Zhang & Yang Wang & Liang Gao, 2022. "Critical Percolation on Temporal High-Speed Railway Networks," Mathematics, MDPI, vol. 10(24), pages 1-8, December.
    13. Xia, Yongxiang & Fan, Jin & Hill, David, 2010. "Cascading failure in Watts–Strogatz small-world networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(6), pages 1281-1285.
    14. Ouyang, Bo & Teng, Zhaosheng & Tang, Qiu, 2016. "Dynamics in local influence cascading models," Chaos, Solitons & Fractals, Elsevier, vol. 93(C), pages 182-186.
    15. Hassan Qudrat-Ullah & Mark McCarthy Akrofi & Aymen Kayal, 2020. "Analyzing Actors’ Engagement in Sustainable Energy Planning at the Local Level in Ghana: An Empirical Study," Energies, MDPI, vol. 13(8), pages 1-20, April.
    16. Cumelles, Joel & Lordan, Oriol & Sallan, Jose M., 2021. "Cascading failures in airport networks," Journal of Air Transport Management, Elsevier, vol. 92(C).
    17. Lobaccaro, G. & Croce, S. & Lindkvist, C. & Munari Probst, M.C. & Scognamiglio, A. & Dahlberg, J. & Lundgren, M. & Wall, M., 2019. "A cross-country perspective on solar energy in urban planning: Lessons learned from international case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 209-237.
    18. Wang, Wei & Li, Wenyao & Lin, Tao & Wu, Tao & Pan, Liming & Liu, Yanbing, 2022. "Generalized k-core percolation on higher-order dependent networks," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    19. Erbaş, Mehmet & Kabak, Mehmet & Özceylan, Eren & Çetinkaya, Cihan, 2018. "Optimal siting of electric vehicle charging stations: A GIS-based fuzzy Multi-Criteria Decision Analysis," Energy, Elsevier, vol. 163(C), pages 1017-1031.
    20. Qingchun Li & Shangjia Dong & Ali Mostafavi, 2019. "Modeling of inter-organizational coordination dynamics in resilience planning of infrastructure systems: A multilayer network simulation framework," PLOS ONE, Public Library of Science, vol. 14(11), pages 1-21, November.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:6:p:1355-:d:149073. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.