Design of optimal wavelet-based energy management for hybrid energy storage systems in DC-microgrids to increase the battery lifetime

Considering natural stochastic power fluctuation as well as existing of fast varying local loads, power quality and stability problems are unavoidable in low-voltage microgrid power systems, especially in isolated operating modes. The main goal of this research is to design a power management system based on a wavelet filter, in which the frequency components of excess power, i.e., the difference between the generated power and the consumed power, are separated from each other and the high-frequency components are assigned to the supercapacitor and the low-frequency components to the battery. Using this method, the supercapacitor has the most effective performance in the hybrid energy storage system, which reduces the fluctuation of the battery charge level and increases its lifespan. For this purpose, two optimized PI controllers are used to make batteries and supercapacitors to store generated reference currents by the proposed energy management system. In this regard, the coefficients of the PI controllers have been optimized using the grey wolf optimization method. The proposed algorithm examined in the case study hybrid DC-microgrid system in Simulink/Matlab. The simulation results confirm that the proposed supervisory algorithm improves the microgrid dynamic and static performance under different operating conditions.