IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i3p715-d1332022.html
   My bibliography  Save this article

Networked Microgrids: A Review on Configuration, Operation, and Control Strategies

Author

Listed:
  • Mohammad Javad Bordbari

    (Department of Building, Civil, and Environmental Engineering (BCEE), Concordia University, Montreal, QC H3G 2W1, Canada)

  • Fuzhan Nasiri

    (Department of Building, Civil, and Environmental Engineering (BCEE), Concordia University, Montreal, QC H3G 2W1, Canada)

Abstract

The increasing impact of climate change and rising occurrences of natural disasters pose substantial threats to power systems. Strengthening resilience against these low-probability, high-impact events is crucial. The proposition of reconfiguring traditional power systems into advanced networked microgrids (NMGs) emerges as a promising solution. Consequently, a growing body of research has focused on NMG-based techniques to achieve a more resilient power system. This paper provides an updated, comprehensive review of the literature, particularly emphasizing two main categories: networked microgrids’ configuration and networked microgrids’ control. The study explores key facets of NMG configurations, covering formation, power distribution, and operational considerations. Additionally, it delves into NMG control features, examining their architecture, modes, and schemes. Each aspect is reviewed based on problem modeling/formulation, constraints, and objectives. The review examines findings and highlights the research gaps, focusing on key elements such as frequency and voltage stability, reliability, costs associated with remote switches and communication technologies, and the overall resilience of the network. On that basis, a unified problem-solving approach addressing both the configuration and control aspects of stable and reliable NMGs is proposed. The article concludes by outlining potential future trends, offering valuable insights for researchers in the field.

Suggested Citation

  • Mohammad Javad Bordbari & Fuzhan Nasiri, 2024. "Networked Microgrids: A Review on Configuration, Operation, and Control Strategies," Energies, MDPI, vol. 17(3), pages 1-28, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:715-:d:1332022
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/3/715/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/3/715/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ge, Pudong & Teng, Fei & Konstantinou, Charalambos & Hu, Shiyan, 2022. "A resilience-oriented centralised-to-decentralised framework for networked microgrids management," Applied Energy, Elsevier, vol. 308(C).
    2. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    3. Dhanasekaran Boopathi & Kaliannan Jagatheesan & Baskaran Anand & Sourav Samanta & Nilanjan Dey, 2023. "Frequency Regulation of Interlinked Microgrid System Using Mayfly Algorithm-Based PID Controller," Sustainability, MDPI, vol. 15(11), pages 1-19, May.
    4. Mei, Jie & Chen, Chen & Wang, Jianhui & Kirtley, James L., 2019. "Coalitional game theory based local power exchange algorithm for networked microgrids," Applied Energy, Elsevier, vol. 239(C), pages 133-141.
    5. Oh, Seok Hwa & Yoon, Yong Tae & Kim, Seung Wan, 2020. "Online reconfiguration scheme of self-sufficient distribution network based on a reinforcement learning approach," Applied Energy, Elsevier, vol. 280(C).
    6. Wang, Y. & Rousis, A. Oulis & Strbac, G., 2022. "Resilience-driven optimal sizing and pre-positioning of mobile energy storage systems in decentralized networked microgrids," Applied Energy, Elsevier, vol. 305(C).
    7. Dong, Yuchen & Zheng, Weibo & Cao, Xiaoyu & Sun, Xunhang & He, Zhengwen, 2023. "Co-planning of hydrogen-based microgrids and fuel-cell bus operation centers under low-carbon and resilience considerations," Applied Energy, Elsevier, vol. 336(C).
    8. Mohseni, Shayan & Pishvaee, Mir Saman, 2023. "Energy trading and scheduling in networked microgrids using fuzzy bargaining game theory and distributionally robust optimization," Applied Energy, Elsevier, vol. 350(C).
    9. Alizadeh, Ali & Kamwa, Innocent & Moeini, Ali & Mohseni-Bonab, Seyed Masoud, 2023. "Energy management in microgrids using transactive energy control concept under high penetration of Renewables; A survey and case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 176(C).
    10. Maen Z. Kreishan & Ahmed F. Zobaa, 2021. "Optimal Allocation and Operation of Droop-Controlled Islanded Microgrids: A Review," Energies, MDPI, vol. 14(15), pages 1-45, July.
    11. Wu, Tao & Wang, Jianhui & Lu, Xiaonan & Du, Yuhua, 2022. "AC/DC hybrid distribution network reconfiguration with microgrid formation using multi-agent soft actor-critic," Applied Energy, Elsevier, vol. 307(C).
    12. Guodong Liu & Maximiliano F. Ferrari & Thomas B. Ollis & Aditya Sundararajan & Mohammed Olama & Yang Chen, 2023. "Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation," Energies, MDPI, vol. 16(7), pages 1-27, March.
    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. Zhou, Yuekuan & Lund, Peter D., 2023. "Peer-to-peer energy sharing and trading of renewable energy in smart communities ─ trading pricing models, decision-making and agent-based collaboration," Renewable Energy, Elsevier, vol. 207(C), pages 177-193.
    3. Zhao, Yincheng & Zhang, Guozhou & Hu, Weihao & Huang, Qi & Chen, Zhe & Blaabjerg, Frede, 2023. "Meta-learning based voltage control strategy for emergency faults of active distribution networks," Applied Energy, Elsevier, vol. 349(C).
    4. Mohammad Sadeghi & Shahram Mollahasani & Melike Erol-Kantarci, 2021. "Cost-Optimized Microgrid Coalitions Using Bayesian Reinforcement Learning," Energies, MDPI, vol. 14(22), pages 1-20, November.
    5. Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    6. Chen, Yang & Park, Byungkwon & Kou, Xiao & Hu, Mengqi & Dong, Jin & Li, Fangxing & Amasyali, Kadir & Olama, Mohammed, 2020. "A comparison study on trading behavior and profit distribution in local energy transaction games," Applied Energy, Elsevier, vol. 280(C).
    7. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    8. Fernando García-Muñoz & Miguel Alfaro & Guillermo Fuertes & Manuel Vargas, 2022. "DC Optimal Power Flow Model to Assess the Irradiance Effect on the Sizing and Profitability of the PV-Battery System," Energies, MDPI, vol. 15(12), pages 1-16, June.
    9. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    10. Ning Xin & Laijun Chen & Linrui Ma & Yang Si, 2022. "A Rolling Horizon Optimization Framework for Resilient Restoration of Active Distribution Systems," Energies, MDPI, vol. 15(9), pages 1-14, April.
    11. Md Masud Rana & Mohamed Atef & Md Rasel Sarkar & Moslem Uddin & GM Shafiullah, 2022. "A Review on Peak Load Shaving in Microgrid—Potential Benefits, Challenges, and Future Trend," Energies, MDPI, vol. 15(6), pages 1-17, March.
    12. Upasana Lakhina & Nasreen Badruddin & Irraivan Elamvazuthi & Ajay Jangra & Truong Hoang Bao Huy & Josep M. Guerrero, 2023. "An Enhanced Multi-Objective Optimizer for Stochastic Generation Optimization in Islanded Renewable Energy Microgrids," Mathematics, MDPI, vol. 11(9), pages 1-24, April.
    13. Rahimi Sadegh, Ainollah & Setayesh Nazar, Mehrdad & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal resilient allocation of mobile energy storages considering coordinated microgrids biddings," Applied Energy, Elsevier, vol. 328(C).
    14. Xie, Haipeng & Tang, Lingfeng & Zhu, Hao & Cheng, Xiaofeng & Bie, Zhaohong, 2023. "Robustness assessment and enhancement of deep reinforcement learning-enabled load restoration for distribution systems," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    15. Bio Gassi, Karim & Baysal, Mustafa, 2023. "Improving real-time energy decision-making model with an actor-critic agent in modern microgrids with energy storage devices," Energy, Elsevier, vol. 263(PE).
    16. Yanfang Hou & Hui Tian, 2023. "Research on the Dynamic Characteristics of Photovoltaic Power Production and Sales Based on Game Theory," Sustainability, MDPI, vol. 15(19), pages 1-19, October.
    17. Yousef Asadi & Mohsen Eskandari & Milad Mansouri & Andrey V. Savkin & Erum Pathan, 2022. "Frequency and Voltage Control Techniques through Inverter-Interfaced Distributed Energy Resources in Microgrids: A Review," Energies, MDPI, vol. 15(22), pages 1-29, November.
    18. Sankar, Matta Mani & Chatterjee, Kalyan, 2023. "A posteriori multiobjective approach for techno-economic allocation of PV and BES units in a distribution system hosting PHEVs," Applied Energy, Elsevier, vol. 351(C).
    19. Qiu, Dawei & Wang, Yi & Wang, Junkai & Jiang, Chuanwen & Strbac, Goran, 2023. "Personalized retail pricing design for smart metering consumers in electricity market," Applied Energy, Elsevier, vol. 348(C).
    20. Cao, Di & Zhao, Junbo & Hu, Weihao & Ding, Fei & Yu, Nanpeng & Huang, Qi & Chen, Zhe, 2022. "Model-free voltage control of active distribution system with PVs using surrogate model-based deep reinforcement learning," Applied Energy, Elsevier, vol. 306(PA).

    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:17:y:2024:i:3:p:715-:d:1332022. 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.