FLARE: Federated Lightweight Agents for Reinforcement Environments in Microgrids

Ziyi Sang, Zhuoxi Duan, Shihao Shen, Cheng Zhang, Xiaofei Wang, Tao Luo, Sheng Chen, Xinying Wang, Guoliang Wu

Abstract: With the development of smart grid, the significant role of artificial intelligence technologies in microgrid state analysis and energy scheduling is becoming increasingly evident. The microgrids constantly generate a massive amount of computational requests. However, the distributed architecture of microgrids poses challenges for processing computing requests, such as limited resources at single nodes and load imbalance. In this paper, we propose FLARE, an end-edge-cloud collaboration computing architecture in microgrids, to optimize request scheduling. The optimization objective of the scheduling algorithm is to maximize long-term comprehensive utility considering factors such as delay and energy consumption. We utilize Deep Reinforcement Learning (DRL) to tackle this NP-hard problem. To address the deployment and update issues of distributed schedulers on end devices, we introduce model pruning and Federated Learning (FL). Experimental results demonstrate that FLARE outperforms state-of-the-art heuristic and centralized algorithms in terms of performance and scalability.

External IDs:dblp:journals/tnse/SangDSZWLCWW26