Brain Modeling for Microgrid Control and Protection: State of the Art, Challenges, and Future Trends

Microgrids (MGs) are building blocks of smart power systems formed by integrating local power generation resources, energy storage systems, and power-consuming units. While MGs offer many benefits, including increased resilience and flexibility, there remains a need for improved control and protection techniques that can ensure their performance and automatic restoration in response to dynamic operating conditions and failure events. Recently, researchers have explored model-free emotional-learning adaptive strategies based on the emotional response of human brains to control MGs. These model-free control strategies are well-suited for handling the complexity, nonlinearity, and uncertainty present in MGs and offer several advantages over traditional approaches. This paper provides an overview of different emotional learning techniques applied to MG control and protection, their challenges, and future trends. In addition, we draw parallels between the hierarchical control architecture of MGs and the emotional learning process in the human brain, discussing their operational strategies and key areas of research. Finally, the future implementations of brain emotional learning in the control and protection of MGs are discussed, and concluding remarks on the potential of this approach are provided.