IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v350y2023ics0306261923009704.html
   My bibliography  Save this article

Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus

Author

Listed:
  • Sadeghi, M.
  • Kalantar, M.

Abstract

Multi-agent systems (MAS) are emerging as a promising approach for smart power system restoration. After a fault has been detected and isolated, the maximum out-of-service loads should be restored in a timely manner. This study aims to develop reliable restoration through autonomous, rapid, and stable communication. For this purpose, a multilevel consensus is suggested to coordinate MAS in a fully decentralized fashion. System dynamics emerge in a distributed manner based on the state vectors of the agent and its proximate neighbors. The information matrix (IM) is used by the agents to accumulate and exchange state vectors encompassing common variable interests. In order to weight the influence of agents on voting, an optimized weighting model (Optw) is proposed. The parameters are bound by Lyapunov's assessment to ensure consensus and then optimized for quick convergence. The proposed multilevel topology supports parallel information discovery in local bunches and provides extremely quick convergence. The suggested one-by-one communication architecture facilitates the exchange of information since each agent only interacts with four of its neighbors. The redundant communication prevents a single point of failure. These achievements make it suitable for application in large-scale power systems. The effectiveness of the affirmation method is revealed through numerical simulation in various scenarios.

Suggested Citation

  • Sadeghi, M. & Kalantar, M., 2023. "Fully decentralized multi-agent coordination scheme in smart distribution restoration: Multilevel consensus," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923009704
    DOI: 10.1016/j.apenergy.2023.121606
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923009704
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121606?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ren, Yi & Fan, Dongming & Feng, Qiang & Wang, Zili & Sun, Bo & Yang, Dezhen, 2019. "Agent-based restoration approach for reliability with load balancing on smart grids," Applied Energy, Elsevier, vol. 249(C), pages 46-57.
    2. Ishan Srivastava & Sunil Bhat & B. V. Surya Vardhan & Neeraj Dhanraj Bokde, 2022. "Fault Detection, Isolation and Service Restoration in Modern Power Distribution Systems: A Review," Energies, MDPI, vol. 15(19), pages 1-26, October.
    3. Li, Francis G.N. & Bataille, Chris & Pye, Steve & O'Sullivan, Aidan, 2019. "Prospects for energy economy modelling with big data: Hype, eliminating blind spots, or revolutionising the state of the art?," Applied Energy, Elsevier, vol. 239(C), pages 991-1002.
    4. Wu, Hao & Xie, Yunyun & Xu, Yan & Wu, Qiuwei & Yu, Chen & Sun, Jinsheng, 2022. "Resilient scheduling of MESSs and RCs for distribution system restoration considering the forced cut-off of wind power," Energy, Elsevier, vol. 244(PB).
    5. Patnaik, Bhaskar & Mishra, Manohar & Bansal, Ramesh C. & Jena, Ranjan Kumar, 2020. "AC microgrid protection – A review: Current and future prospective," Applied Energy, Elsevier, vol. 271(C).
    6. Ghasemi, Sasan & Moshtagh, Jamal, 2022. "Distribution system restoration after extreme events considering distributed generators and static energy storage systems with mobile energy storage systems dispatch in transportation systems," Applied Energy, Elsevier, vol. 310(C).
    7. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
    8. Mishra, Dillip Kumar & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Hossain, M.J., 2022. "Active distribution system resilience quantification and enhancement through multi-microgrid and mobile energy storage," Applied Energy, Elsevier, vol. 311(C).
    9. Zidan, Aboelsood & El-Saadany, Ehab F., 2013. "Incorporating load variation and variable wind generation in service restoration plans for distribution systems," Energy, Elsevier, vol. 57(C), pages 682-691.
    10. Nawaz, Arshad & Zhou, Min & Wu, Jing & Long, Chengnian, 2022. "A comprehensive review on energy management, demand response, and coordination schemes utilization in multi-microgrids network," Applied Energy, Elsevier, vol. 323(C).
    11. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
    12. Li, Peng & Ji, Jie & Ji, Haoran & Song, Guanyu & Wang, Chengshan & Wu, Jianzhong, 2020. "Self-healing oriented supply restoration method based on the coordination of multiple SOPs in active distribution networks," Energy, Elsevier, vol. 195(C).
    Full references (including those not matched with items on IDEAS)

    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. Zhang, Lu & Yu, Shunjiang & Zhang, Bo & Li, Gen & Cai, Yongxiang & Tang, Wei, 2023. "Outage management of hybrid AC/DC distribution systems: Co-optimize service restoration with repair crew and mobile energy storage system dispatch," Applied Energy, Elsevier, vol. 335(C).
    2. Jorge De La Cruz & Eduardo Gómez-Luna & Majid Ali & Juan C. Vasquez & Josep M. Guerrero, 2023. "Fault Location for Distribution Smart Grids: Literature Overview, Challenges, Solutions, and Future Trends," Energies, MDPI, vol. 16(5), pages 1-37, February.
    3. Weichang Kong & Fei Qiao & Qidi Wu, 2020. "Real-manufacturing-oriented big data analysis and data value evaluation with domain knowledge," Computational Statistics, Springer, vol. 35(2), pages 515-538, June.
    4. Jimmy Gallegos & Paul Arévalo & Christian Montaleza & Francisco Jurado, 2024. "Sustainable Electrification—Advances and Challenges in Electrical-Distribution Networks: A Review," Sustainability, MDPI, vol. 16(2), pages 1-33, January.
    5. Sulman Shahzad & Muhammad Abbas Abbasi & Hassan Ali & Muhammad Iqbal & Rania Munir & Heybet Kilic, 2023. "Possibilities, Challenges, and Future Opportunities of Microgrids: A Review," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    6. Jihoon Moon & Junhong Kim & Pilsung Kang & Eenjun Hwang, 2020. "Solving the Cold-Start Problem in Short-Term Load Forecasting Using Tree-Based Methods," Energies, MDPI, vol. 13(4), pages 1-37, February.
    7. Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
    8. Jia, Kunqi & Guo, Ge & Xiao, Jucheng & Zhou, Huan & Wang, Zhihua & He, Guangyu, 2019. "Data compression approach for the home energy management system," Applied Energy, Elsevier, vol. 247(C), pages 643-656.
    9. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    10. Čábelková, Inna & Strielkowski, Wadim & Streimikiene, Dalia & Cavallaro, Fausto & Streimikis, Justas, 2021. "The social acceptance of nuclear fusion for decision making towards carbon free circular economy: Evidence from Czech Republic," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    11. Fan, Dongming & Sun, Bo & Dui, Hongyan & Zhong, Jilong & Wang, Ziyao & Ren, Yi & Wang, Zili, 2022. "A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks," Reliability Engineering and System Safety, Elsevier, vol. 221(C).
    12. El-Sharafy, M. Zaki & Farag, Hany E.Z., 2017. "Back-feed power restoration using distributed constraint optimization in smart distribution grids clustered into microgrids," Applied Energy, Elsevier, vol. 206(C), pages 1102-1117.
    13. Sellak, Hamza & Ouhbi, Brahim & Frikh, Bouchra & Palomares, Iván, 2017. "Towards next-generation energy planning decision-making: An expert-based framework for intelligent decision support," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1544-1577.
    14. Stefano Villa & Claudio Sassanelli, 2020. "The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature," Energies, MDPI, vol. 13(24), pages 1-23, December.
    15. Papa, Armando & Mital, Monika & Pisano, Paola & Del Giudice, Manlio, 2020. "E-health and wellbeing monitoring using smart healthcare devices: An empirical investigation," Technological Forecasting and Social Change, Elsevier, vol. 153(C).
    16. Seung-Mo Je & Hyeyoung Ko & Jun-Ho Huh, 2021. "Accurate Demand Forecasting: A Flexible and Balanced Electric Power Production Big Data Virtualization Based on Photovoltaic Power Plant," Energies, MDPI, vol. 14(21), pages 1-31, October.
    17. Moshammed Nishat Tasnim & Tofael Ahmed & Monjila Afrin Dorothi & Shameem Ahmad & G. M. Shafiullah & S. M. Ferdous & Saad Mekhilef, 2023. "Voltage-Oriented Control-Based Three-Phase, Three-Leg Bidirectional AC–DC Converter with Improved Power Quality for Microgrids," Energies, MDPI, vol. 16(17), pages 1-32, August.
    18. Liu, Bo & Hou, Yufan & Luan, Wenpeng & Liu, Zishuai & Chen, Sheng & Yu, Yixin, 2023. "A divide-and-conquer method for compression and reconstruction of smart meter data," Applied Energy, Elsevier, vol. 336(C).
    19. Zhao, Guanjia & Cui, Zhipeng & Xu, Jing & Liu, Wenhao & Ma, Suxia, 2022. "Hybrid modeling-based digital twin for performance optimization with flexible operation in the direct air-cooling power unit," Energy, Elsevier, vol. 254(PC).
    20. Younes Zahraoui & Ibrahim Alhamrouni & Saad Mekhilef & M. Reyasudin Basir Khan & Mehdi Seyedmahmoudian & Alex Stojcevski & Ben Horan, 2021. "Energy Management System in Microgrids: A Comprehensive Review," Sustainability, MDPI, vol. 13(19), pages 1-33, September.

    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:eee:appene:v:350:y:2023:i:c:s0306261923009704. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.