AI-assisted Optimal Energy Management in Microgrids

Optimal energy management in a microgrid is studied by an energy management system supported by artificial intelligence (AI). Two stage optimization process is organized. First, a bicriterion optimization model is proposed to determine the optimal charging/discharging schedule of the energy storage system. Historical data is used for the optimization. Second, a nonlinear Reinforcement learning (RL) model is proposed to optimize the MPPT (Maximum Power Point Tracking) mode of the photovoltaic system and the wind turbines in a real-time. Artificial intelligence uses a Soft ActorCritic (SAC) agent to perform the energy flows optimization. The agent is trained offline. Finally, it is used in a real-time to continuously learn (self-tuning) and optimize tracking. The simulation and optimization are implemented in the MATLAB environment.