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A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids

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  • Romain Mannini

    (PROMES-CNRS (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
    Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France)

  • Julien Eynard

    (PROMES-CNRS (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
    Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France)

  • Stéphane Grieu

    (PROMES-CNRS (UPR 8521), Rambla de la Thermodynamique, Tecnosud, 66100 Perpignan, France
    Physical and Engineering Sciences Department, University of Perpignan Via Domitia, 52 Avenue Paul Alduy, 66860 Perpignan, France)

Abstract

Microgrids (MGs) and networked (interconnected) microgrids (NMGs) are emerging as an efficient way for integrating distributed energy resources (DERs) into power distribution systems. MGs and NMGs can disconnect from the main grid and operate autonomously, strengthen grid resilience, and help mitigate grid disturbances and maintain power quality. In addition, when supported by sophisticated and efficient management strategies, MGs and NMGs have the ability to enhance power supply reliability. However, their deployment comes with many challenges, in particular regarding the efficient management of DERs. That is why a survey of recent advances in the smart management—the term refers to a variety of planning and control tasks—of MGs and NMGs is presented in this paper. It aims at establishing a picture of strategies and identifying trends in methods. The reader is provided with an in-depth analysis of a variety of papers recently published in peer-reviewed journals: the way the methods are used and the common issues addressed by the scientific community are discussed. Following this analysis, one can especially observe that (1) model-based predictive control (MPC) is emerging as a competitive alternative to conventional methods, in particular in voltage and frequency regulation and DER management (2) due to their ability to handle complex tasks, data-driven strategies are getting more and more attention from the scientific community (3) game theory (GT) is a very good candidate for efficient management of complex systems as NMGs (4) MPC and artificial intelligence are increasingly being used for proper MG islanded operation or to manage electric vehicles (EVs) efficiently.

Suggested Citation

  • Romain Mannini & Julien Eynard & Stéphane Grieu, 2022. "A Survey of Recent Advances in the Smart Management of Microgrids and Networked Microgrids," Energies, MDPI, vol. 15(19), pages 1-37, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7009-:d:923920
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    References listed on IDEAS

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    2. Darville, Joshua & Yavuz, Abdurrahman & Runsewe, Temitope & Celik, Nurcin, 2023. "Effective sampling for drift mitigation in machine learning using scenario selection: A microgrid case study," Applied Energy, Elsevier, vol. 341(C).
    3. Angelos Patsidis & Adam Dyśko & Campbell Booth & Anastasios Oulis Rousis & Polyxeni Kalliga & Dimitrios Tzelepis, 2023. "Digital Architecture for Monitoring and Operational Analytics of Multi-Vector Microgrids Utilizing Cloud Computing, Advanced Virtualization Techniques, and Data Analytics Methods," Energies, MDPI, vol. 16(16), pages 1-19, August.
    4. Erdal Irmak & Ersan Kabalci & Yasin Kabalci, 2023. "Digital Transformation of Microgrids: A Review of Design, Operation, Optimization, and Cybersecurity," Energies, MDPI, vol. 16(12), pages 1-58, June.

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