Dynamic power management and control for low voltage DC microgrid with hybrid energy storage system using hybrid bat search algorithm and artificial neural network

In this paper, a novel Hybrid Bat Search and Artificial Neural Network (HBSANN) based power management strategy (PMS) is proposed for control of DC microgrids with hybrid energy storage systems (HESS). The proposed control strategy aims to improve the power-sharing among batteries and supercapacitor (SC) to address the demand-generation disparity, maintain state-of-charge (SOC) within boundaries in addition to regulation of dc bus voltage. A low voltage DC (LVDC) microgrid incorporating a photovoltaic system, HESS composed of a battery and SC, DC load, and AC load has been considered. The proposed strategy results in improved battery life due to the transfer of unwaged battery currents with high-frequency components to the supercapacitor. In addition, total harmonic distortion (THD) of the AC output voltage is also analyzed. Furthermore, the effectiveness of the proposed strategy in comparison with recent conventional control strategy based results for performance in terms of voltage overshoot and settling time. Experimental investigations using hardware-in-loop (HIL) setup on an FPGA based real-time simulator validates the results.