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

Sijia Li (), Arman Oshnoei, Frede Blaabjerg and Amjad Anvari-Moghaddam
Additional contact information
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

Sustainability, 2023, vol. 15, issue 11, 1-22

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.

Keywords: microgrids; hierarchical control; machine learning; reinforcement learning; communication links (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/11/8952/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/11/8952/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2023:i:11:p:8952-:d:1162039

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().