Publication

Abstract : Contemporary energy systems face unprecedented challenges in achieving sustainabil- ity goals while maintaining operational efficiency and grid stability. The integration of circular economy principles with artificial intelligence-enhanced smart grid systems rep- resents a critical pathway toward achieving net-zero carbon strategies and sustainable re- source management. Current smart grid implementations focus primarily on operational efficiency while overlooking comprehensive lifecycle sustainability metrics and resource circularity considerations. This research addresses the identified gap by developing an in- tegrated framework that combines circular economy resource management with AI-driven optimization algorithms for real-time carbon footprint reduction and waste minimization. The proposed methodology implements machine learning-based predictive algorithms for component lifecycle management, waste-to-energy conversion optimization, and dynamic resource allocation strategies. Experimental validation conducted on a prototype smart microgrid system demonstrates significant improvements in sustainability metrics across multiple operational parameters. Results indicate a 31.7% reduction in carbon footprint, 43.2% improvement in material recovery rates, 21.4% enhancement in energy efficiency, and 41.5% advancement in waste reduction compared to conventional grid management approaches. The framework achieves 92.3% accuracy in predictive maintenance schedul- ing while maintaining sub-second response times for real-time optimization decisions. These findings establish a foundation for sustainable smart grid development that inte- grates environmental stewardship with technological advancement, contributing to global sustainability goals and climate action initiatives.