Federated Multi-Agent Reinforcement Learning Method for Energy Management of Microgrids

In the past few years, energy management strategies based on multi-agent reinforcement learning (MARL) have been an active research topic. However, existing MARL algorithms require a massive amount of data, making it challenging to ensure data privacy. To address the problem, this paper investigates FL-MARL that combines federated learning (FL) with MARL. FL allows each agent to train based on local data and only share model parameters, which means that the actual data are not shared among agents. The framework is in conjunction with a MARL algorithm: independent proximal policy optimization (IPPO). The proposed algorithm has two advantages: 1) It can protect data privacy of each agent and 2) It can adapt to large-scale and decentralized data scenarios. Finally, the performance of the algorithm is verified by simulation.