Enhanced energy management in IoT-assisted hybrid microgrids through builder optimization algorithm and neural architecture search-guided physics-informed neural network

Abstract

Energy management in Internet of Things-enabled hybrid microgrids plays a vital role in optimizing the coordination of distributed energy resources, including wind turbines, photovoltaic systems, battery energy storage systems, and the main grid. Despite advancements in the Internet of Things improving real-time control and monitoring, the variability of renewable sources presents significant challenges in ensuring consistent energy efficiency and cost minimization. To address these challenges, this study introduces an innovative method that integrates the builder optimization algorithm with a neural architecture search-guided physics-informed neural network. The optimization algorithm determines optimal energy distribution, while the neural framework uses Internet of Things data for accurate forecasting of generation and storage. This integration enables adaptive and intelligent energy management decisions. Implemented in MATLAB, the proposed method significantly outperforms existing models, achieving a total energy cost reduction of $321.06 and an energy efficiency of 99.1%.