Meta-heuristic-based home energy management system for optimizing smart appliance scheduling and electricity cost reduction in residential complexes

With the evolution of technology and substantial advancements in smart devices, managing and controlling energy consumption in households has become increasingly crucial. Smart grid technology serves as an enabler, empowering consumers to effectively manage their energy consumption. This necessitates the role of smart appliance scheduling. In this study, an effective home energy management (HEM) system is introduced, utilizing a new proposed approach called the gradient-based Runge–Kutta optimizer (GRUN). The proposed GRUN approach aims to optimize energy consumption by employing the Runge–Kutta optimizer (RUN) which based on the Local Escaping Operator (LEO) from gradient-based optimizer (GBO). It utilizes multiple knapsacks to maintain electricity demand below a predefined threshold during peak hours. Firstly, the validation of the proposed GRUN technique is confirmed by seven benchmark functions and compared its results with those obtained by other well-known optimization algorithms, including artificial ecosystem-based optimization, hunter prey optimization, GBO, and the original RUN algorithm. Then, the performance of the GRUN technique is checked for the optimization of smart HEM. This study ensures that power remains within specified limits and that power consumption remains constant before and after scheduling, without affecting the operation time of each device. Additionally, it determines the cost of both scheduled and unscheduled loads, providing information about their operating times and the current status of each device. The developed approach yields significant benefits in reducing electricity costs and the peak-to-average ratio. The reduction in electricity bill ranges from 60% for a single home to 20% for 100 homes throughout the day. Moreover, a decrease in electricity consumption per hour leads to a peak reduction of 50% for one house and 25% for 100 houses during the day. Evaluated through MATLAB simulations in a residential complex with multiple smart homes, the GRUN system demonstrates substantial advantages over the RUN system in reducing electricity costs. This study offers an effective perspective on household energy management, achieving a better balance between energy consumption and its associated costs.