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Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods

Author

Listed:
  • Sijia Li

    (Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark)

  • Arman Oshnoei

    (Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark)

  • Frede Blaabjerg

    (Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark)

  • Amjad Anvari-Moghaddam

    (Department of Energy (AAU Energy), Aalborg University, 9220 Aalborg, Denmark)

Abstract

Microgrids create conditions for efficient use of integrated energy systems containing renewable energy sources. One of the major challenges in the control and operation of microgrids is managing the fluctuating renewable energy generation, as well as sudden load changes that can affect system frequency and voltage stability. To solve the above problems, hierarchical control techniques have received wide attention. At present, although some progress has been made in hierarchical control systems using classical control, machine learning-based approaches have shown promising features and performance in the control and operation management of microgrids. This paper reviews not only the application of classical control in hierarchical control systems in the last five years of references, but also the application of machine learning techniques. The survey also provides a comprehensive description of the use of different machine learning algorithms at different control levels, with a comparative analysis for their control methods, advantages and disadvantages, and implementation methods from multiple perspectives. The paper also presents the structure of primary and secondary control applications utilizing machine learning technology. In conclusion, it is highlighted that machine learning in microgrid hierarchical control can enhance control accuracy and address system optimization concerns. However, challenges, such as computational intensity, the need for stability analysis, and experimental validation, remain to be addressed.

Suggested Citation

  • Sijia Li & Arman Oshnoei & Frede Blaabjerg & Amjad Anvari-Moghaddam, 2023. "Hierarchical Control for Microgrids: A Survey on Classical and Machine Learning-Based Methods," Sustainability, MDPI, vol. 15(11), pages 1-22, June.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:8952-:d:1162039
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    References listed on IDEAS

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    1. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
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    3. 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).
    4. Wang, Shuoqi & Lu, Languang & Han, Xuebing & Ouyang, Minggao & Feng, Xuning, 2020. "Virtual-battery based droop control and energy storage system size optimization of a DC microgrid for electric vehicle fast charging station," Applied Energy, Elsevier, vol. 259(C).
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    Cited by:

    1. Pabel Alberto Cárdenas & Maximiliano Martínez & Marcelo Gustavo Molina & Pedro Enrique Mercado, 2023. "Development of Control Techniques for AC Microgrids: A Critical Assessment," Sustainability, MDPI, vol. 15(21), pages 1-28, October.
    2. Smriti Sharma & John O’Donnell & Wencong Su & Richard Mueller & Line Roald & Khurram Rehman & Andrey Bernstein, 2024. "Engineering Microgrids Amid the Evolving Electrical Distribution System," Energies, MDPI, vol. 17(19), pages 1-34, September.

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