Cooperative Neural Fitted Learning for Distributed Energy Management in Microgrids via Wireless Networks

With the proliferation of renewable energy sources and the elevation of environmental concerns, it is expected that microgrids will become one of the major means for residential energy supply. However, the distributed nature of microgrid operation brings new technical challenges to energy management. Endowing wireless communication capability to the distributed generation (DG) units and energy storage (ES) devices in a microgrid is beneficial for their cooperation without a centralized controller. Yet, how to establish distributed energy management without \emph{a priori} statistical information for all the DG units and loads still requires extensive research. In this paper, a reinforcement learning algorithm with cooperative neural fitting iteration is proposed for distributed energy management in microgrids via wireless networks. The reinforcement learning algorithm leverages a distributed actor- critic structure to adopt the continuous states and action spaces of a microgrid. A diffusion strategy is incorporated in the reinforcement learning algorithm to coordinate the actions of DG units and ES devices by exchanging their evaluations and decisions via a wireless network. Simulation results based on realistic renewable power generation and load data are presented to evaluate the performance of the proposed algorithm.