AI-Driven Energy Management Systems for Microgrids: Optimizing Renewable Energy Integration and Load Balancing - Bhardwaj Diwakar; M Shalini; Kanmani Jebaseeli S.; Jadhav Swati; Alabdeli Haider; Sutar Vasundhara; Senthil Kumar R. (E3S Web of Conferences)
The increasing adoption of microgrids, particularly with renewable energy sources, necessitates advanced energy management systems (EMS) that can efficiently handle dynamic power demands and supply fluctuations. This paper proposes an AI-driven EMS model specifically designed for optimizing energy distribution and load balancing within microgrids. The system leverages machine learning algorithms to predict energy demand and adapt the power allocation in real-time, ensuring efficient integration of renewable resources while maintaining grid stability. A simulation of the proposed system demonstrates significant improvements in energy efficiency and stability when compared to traditional EMS approaches. This research highlights the importance of intelligent systems in achieving sustainable and reliable microgrid operations.