Optimization scheduling of home appliances in smart home: A model based on a niche technology with sharing mechanism

Abstract

Smart appliances bring convenience to people’s lives, but the high electricity cost and high peak load are still drawbacks. The scheduling optimization of household electricity tasks can effectively alleviate those drawbacks. Therefore, this paper proposes a scheduling optimization model of smart home appliances to reduce the load peak value and electricity cost. Herein, the electricity tasks are defined by the set theory and classified by the operational characteristics of the loads. Then, a hybrid coding genetic algorithm based on the niche technology is proposed for scheduling optimization problems of electricity tasks. Different coding and cross-mutation operation strategies are designed for different electricity tasks. Finally, a tracing Pareto method based on weighted sum approach is proposed to solve the multi-objective optimization problem. The experimental results show that the proposed algorithm has a good performance in reducing load peaks and electricity costs. In cases A, B, and C, the load peak value is reduced by 54.7%, 45.7%, and 20.7%, respectively; the electricity cost is reduced by 5.98%, 12.00%, and 16.12%, respectively. Furthermore, a series of Pareto optimal solutions are also identified, which can help users better understand the trade-off relation between the load peak value and electricity cost.