Smart Home Appliances’ Scheduling by Two-Stage Optimization with Real-Time Price Model

Abstract

Residential load control focuses on reducing both the monthly electricity expense and the peak demand for electricity. The efficient scheduling of smart home appliances’ operational procedures can accomplish both goals. Because rescheduling appliances to reduce one goal can lead to an increase in the other, these two goals are inherently in conflict with one another. This work proposes an algorithm utilizing artificial intelligence methods to accomplish both goals simultaneously. The proposed method has been successfully tested on real data of energy dynamic pricing options applicable in two utilities, namely Alectra Utilities Corp., Canada, and ComEd Northern Illinois Power Company, serving residential consumers with varying monthly power use. It is also proposed that both utilities use a cost function that is based on real-time prices to mitigate the risks associated with real-time pricing. In addition, this research proposes a novel method for determining the absolute maximum hourly power usage. The suggested algorithm accomplishes its dual goals at once as proof of its efficacy in solving optimization problems.