Fuzzy Logic-Based Energy Management System and Optimization for Seamless Integration of Renewable and Grid Sources

The substantial growth of both the industrial sector and the global population has resulted in a noteworthy upswing in worldwide energy consumption. This has underscored the imperative to investigate alternative energy sources and improve access to power. Microgrids present a compelling alternative by incorporating renewable energy sources and decreasing dependence on fossil fuels. This research introduces an Energy Management System (EMS) designed for microgrid applications, utilizing fuzzy logic principles. The goal is to enhance the efficient utilization of energy resources and streamline load management, ultimately contributing to the effective operation of microgrids. In this study, maximum power is extracted from photovoltaic and wind energy conversion systems, respectively, using Perturb and Observe (P&O) based Maximum Power Point Tracking (MPPT) and the Adaptive Neuro-Fuzzy Inference System (ANFIS). The proposed approach enables seamless switching from the grid source to renewable sources within 0.1 ms when sufficient renewable energy surpasses the load demand. Moreover, if the load exceeds the predetermined tolerance value, the management system promptly disconnects the renewable source while ensuring continuous power supply.