In this paper, a reinforcement-learning-based online optimal (RL-OPT) control method is proposed for hybrid energy storage system (HESS) in AC/DC microgrids involving photovoltaic (PV) system and diesel generators (DG). Due to the low system inertia, conventional unregulated charging and discharging (C&D) of energy storages in microgrids may introduce disturbances that degrade power quality and system performance, especially in fast C&D situations. Secondary and tertiary control levels can optimize the state of charge (SOC) reference of HESS periodically; however, they are lacking the direct controllability of regulating the transient performance. Additionally, the unknown and time-varying system parameters greatly limit the performance of conventional model-based controllers. In this study, the optimal control theory is used to optimize the C&D profile and to suppress the disturbances caused by integrating HESS. Neural networks (NN) are devised to train the nonlinear dynamics of HESS based on the input/output measurement, and to learn the optimal control input for bidirectional converter interfaced HESS using the estimated system dynamics. Because the proposed RL-OPT method is fully decentralized, which only requires the local measurements, the plug & play capability of HESS can be easily realized. Both islanded and grid-tied modes are considered. Extensive simulations and experiments are conducted to evaluate the effectiveness of proposed method.

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