Optimal Charging and Discharging Control for Hybrid Energy Storage System based on Reinforcement Learning

摘要

Systems and methods are disclosed to manage a microgrid with a hybrid energy storage system (HESS) includes deriving a dynamic model of a bidirectional-power-converter (BPC)-interfaced HESS; applying a first neural network (NN) to estimate a system dynamic; and applying a second NN to calculate an optimal control input for the HESS through online learning based on the estimated system dynamics.