Smart home load scheduling system with solar photovoltaic generation and demand response in the smart grid - Hua, Lyu-Guang; Ali Shah, S. Haseeb; Alghamdi, Baheej; Hafeez, Ghulam; Ullah, Safeer; Murawwat, Sadia; Ali, Sajjad; Khan, Muhammad Iftikhar (Frontiers in Energy Research, 2024)
This study introduced a smart home load scheduling system aimed at addressing concerns related to energy conservation and environmental preservation. A comprehensive demand response (DR) model is proposed, incorporating an energy consumption scheduler (ECS) designed to optimize the operation of smart appliances. The ECS utilizes various optimization algorithms, including particle swarm optimization (PSO), genetic optimization algorithm (GOA), wind-driven optimization (WDO), and developed hybrid genetic wind-driven optimization (HGWDO) algorithm. These algorithms work together to schedule smart home appliance operations effectively under real-time price-based demand response (RTDR). The efficient integration of renewable energy into smart grids is challenging due to its time-varying and intermittent nature. To address this, batteries are employed in this study to mitigate fluctuations in renewable generation. Simulation results validate the effectiveness of our proposed approach in optimally addressing the Smart Home Load Scheduling problem with photovoltaic generation and demand response. The system achieves minimization of utility bills, pollution emissions, and the peak-to-average demand ratio (PADR) compared to existing models. Through this study, 1 Lyu-Guang Hua et al. Smart home load scheduling system using demand response in smart grid we provide a practical and effective solution to enhance the efficiency of smart home energy management, contributing to sustainable practices and reducing environmental impact.