Optimizing Smart Home Energy Management with Branch and Bound Method: A Novel Model for Daily and Weekly Scheduling of Smart Appliances

The increasing use of smart homes has led researchers to focus on load management and consumption response in the household sector. This paper proposes a method for minimizing electricity consumption costs in a smart home with programmable appliances that can be controlled. The study examines consumption management and load response in a smart home, taking into account real-time pricing. The proposed models offer a new framework for planning the time of use of appliances, taking into account the limitations and operation of household equipment. The study proposes four mathematical models of the problem, which are of the nonlinear integer programming (NLIP) and are solved using GAMS software. In addition, the Branch and Bound (B&B) algorithm developed in Python is used in the two proposed models for daily scheduling of smart appliances and for scheduling longer time periods, such as a week, a month, or even a year. In both types, one model is connected only to grid power, and the other model is connected to both the grid and photovoltaic sources. Numerical studies for the four different models show the effectiveness of the proposed models in smart home planning. Furthermore, this study investigates the potential of using B&B to solve the proposed models. The results demonstrate the effectiveness of the proposed method in reducing energy costs, while also considering the limitations and performance of household equipment. In addition, by comparing the results obtained from the proposed models, this article examines the amount of investment required to purchase solar panels in different studies.