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

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.

Automated summary

Microgrids enable efficient utilization of integrated energy systems with renewable energy sources. Managing fluctuating renewable energy generation and sudden load changes is a major challenge in microgrid control and operation. Hierarchical control techniques have received attention, with machine learning-based approaches showing promising features and performance. This paper reviews the application of classical control and machine learning techniques in hierarchical control systems, comparing their methods, advantages, disadvantages, and implementation across different control levels. The paper highlights the potential of machine learning to enhance control accuracy and address system optimization concerns in microgrid hierarchical control, but challenges such as computational intensity, stability analysis, and experimental validation remain to be addressed.