Energy Optimization in Microgrids: A Federated Multi-Task Reinforcement Learning Approach

Microgrid energy management can benefit from using advanced learning frameworks that strike a balance between optimization, sharing resources and keeping data secure. While both Federated Learning (FL) and Multi-Task Learning (MTL) have shown promise in distributed optimization and sharing knowledge. However, their combined application within a Reinforcement Learning (RL) framework remains unexplored. This study introduces a fresh Federated Multitask Reinforcement Learning framework that promotes collaborative learning among decentralized energy nodes while prioritizing data privacy. It also utilizes MTL to boost generalization across different energy management tasks. By leveraging the strengths of both methodologies, this framework aims to enhance energy efficiency, lower emissions, and improve the scalability and resilience of urban microgrid operations, making them better equipped to respond to fluctuating energy demands.