A new approach to identify the optimum frequency ranges of the constituent storage devices of a hybrid energy storage system using the empirical mode decomposition technique

Abstract

Empirical mode decomposition technique is used to extract the implicit mode components contained in the net power of a photovoltaic-powered nanogrid embedded within a microgrid. Statistical analysis of the magnitudes, instantaneous frequencies and energy contents of the components shows the dominance of the daily and yearly cyclic modes. Taking into consideration the frequency range of the modes, smoothing of the high- and low-frequency mode components is then realized through the respective buffering actions of a supercapacitor and a battery in a hybrid energy storage system. The optimum frequency ranges of the supercapacitor and the battery to achieve the lowest cost per kWh storage capacity for the designed hybrid energy storage system are then determined in a direct and closed form. The proposed approach requires much reduced computational efforts, in comparison to existing methods which involve iterative search procedures.