Lightweight Federated Reinforcement Learning for Independent Request Scheduling in Microgrids

With the development of technology and society, the traditional energy system has become difficult to meet the demand. Applying Deep Reinforcement Learning (DRL) to solve scheduling problems in microgrid cluster edge-cloud collaborative architecture provides a new solution. However, there is no research work has been completed to address the independent training, deployment, and inference of DRL in the microgrid cluster scenario. In this paper, we propose a federated DRL-based request scheduling algorithm for distributed microgrid cluster scenarios with the goal of maximizing the long-term utility of the system. In addition, we prune the DRL model to make it more applicable to resource-constrained edge nodes. The experimental results show that the proposed algorithm has more stable performance and better adaptability to the dynamic system environment compared to the traditional centralized training. In addition, the pruning of the model compresses the size of the model to 50.4% with a 4% loss of accuracy.