Comprehensive Study on Microgrid Energy Management: Machine Learning and Deep Learning Innovations with Use Cases

In recent years, energy management in microgrid has become increasingly crucial, especially with the integration of renewable energy sources such as solar and wind, which exhibit intermittent and uncertain behavior. Machine Learning (ML) and Deep Learning (DL) methods have extensively been applied to these problems by providing better forecasts, scheduling optimization, and dynamic control of microgrid components to ensure stability, reliability, and sustainability, also to manage production, storage and demand. In addition to contributing to increased renewable energy generation and reduced dependence upon the main grid, these methods also help in minimizing cost and peak load reductions. However, ML and DL in these systems are typically computationally expensive, which demand large quality dataset and have limited ability to scale across operating conditions, also face integration issues while attempting to use in real-time hardware. This survey systematically examined the current state of ML and DL models and algorithms, and surveyed the literature regarding their application for microgrid energy management. Practical examples of ML and DL application for microgrid energy management were discussed, and there were identified as areas for future research to concentrate on developing scalable and resilient solutions.