Smart Energy Management for a Microgrid System Based on Echo State Networks─Gray Wolf Optimizer Hybrid Algorithm

This work present a smart energy management system for a microgrid based on a hybrid intelligent adaptive algorithm composed of an echo state network and gray wolf optimizer techniques. The microgrid connected to the grid is composed of renewable sources such as photovoltaic and wind conversion associated with a hybrid battery hydrogen fuel generation storage system. An adaptive intelligent control unit is developed to manage the power flow between the hybrid power sources and other devices in the system. The controller chooses the optimal operating mode of power sources ensuring the continuous supply of the injecting power in the grid and maintaining the battery state of charge at acceptable levels as the hydrogen tank level. Complete mathematical modeling for the proposed system is implemented in MATLAB/Simulink to track the microgrid performance. Numerical and graphical results are presented to show the effectiveness of the proposed control system