Review of Application and Performance of Recurrent Neural Networks in Microgrid Management and Protection

Microgrids have increasingly integrated renewable energy sources and distributed energy resources, making them difficult to manage and protect by using traditional time-invariant methods because of the in-built generation variability. This review examines the latest developments in the use of Recurrent Neural Networks (RNNs) on energy management and protection of grid-connected microgrids. After systematically analysing the recent peer-reviewed studies, the review demonstrates how RNNs improve forecasting accuracy, optimal energy dispatch, enhance fault detection, and provide adaptive protection features. Identified challenges include computational complexity, dependence on data quality, and hardware limitations. With a special focus on RNN, the review offers not only practical insights on RNN implementations but also discusses future research directions for increased microgrid resilience and efficiency in operations through AI systems.