An adaptive approach to household power control using intelligent grid and ISSA for sustainable energy systems

This research introduces an adaptive energy control strategy for households powered by Sustainable Energy Sources (SES), leveraging Intelligent Grid (IG) technologies. The novelty of this work lies in employing the Intelligent Salp Swarm Algorithm (ISSA), which dynamically adjusts control parameters for real-time optimization, outperforming conventional methods. ISSA efficiently manages appliances, energy sources, and demand prioritization, ensuring optimal energy utilization while addressing SES variability. The proposed model incorporates practical constraints such as customer preferences, energy supply limitations, and ecological unpredictability, ensuring real-world applicability. Three scenarios are analyzed: traditional residences without energy management systems, smart homes with basic SES, and intelligent homes integrating advanced SES. Comparative analysis confirms that ISSA surpasses Genetic Algorithms (GAM) and Particle Swarm Stabilization (PSS) in optimizing energy use. The results highlight ISSA's ability to enhance system efficiency, reduce costs, and support sustainability goals. This scalable approach effectively addresses modern energy management challenges, contributing to secure, sustainable, and affordable household power systems.