Federated Learning-Based Energy Management Framework for Decentralized Microgrids

Decentralized microgrids play a critical role in the transition to sustainable energy systems, but optimizing their energy management remains a significant challenge due to privacy concerns, heterogeneity, and the dynamic nature of energy demand and supply. This paper proposes a Federated LearningBased Energy Management Framework (FLEMF) to optimize energy dispatch while preserving data privacy across distributed energy resources (DERs). Unlike centralized approaches, FLEMF enables collaborative training of machine learning models without sharing raw data, ensuring enhanced security and scalability. The proposed framework is evaluated through simulations on a decentralized microgrid system comprising 20 DERs. Results demonstrate that FLEMF improves energy dispatch efficiency by 18.7 % compared to traditional centralized optimization methods, while reducing energy wastage by 14.2 %. Additionally, privacy risks are minimized by up to 42.3 %, as quantified using a differential privacy leakage metric. The framework also achieves a 24.08 % improvement in computational efficiency, making it suitable for real-time applications. The findings underscore the potential of federated learning to address the dual challenges of optimization and privacy in decentralized microgrids, paving the way for more secure, efficient, and scalable energy management systems.