Abstract: With the emerging of smart grid, residential consumers have the opportunity to reduce their electricity cost (EC) and peak-to-average ratio (PAR) through scheduling their power consumption. In this paper, first we present a novel architecture of home EMS and automated DR framework for scheduling of various household appliances in a smart home, and then propose a genetic algorithm (GA) based approach to solve this optimization problem. The primary aim is to provide consumers with a simple smart controller which can result in maximum benefits and cost reduction with respect to consumer preferences and convenience level. The utility companies profit is also considered via diminishing the PAR which would lead to improving the stability of the entire power system. The real-time price (RTP) model in spite of its privileges, has the tendency to accumulate a lot of loads at a pretty low electricity price time. Therefore, in this paper we use the combination of RTP with the inclining block rate (IBR) model which has the capability to remarkably decrease the PAR and eliminate rebound peak during low price periods. We present three different case studies with diverse power consumption patterns to evaluate the performance of our proposed approach for home EMS. The simulation results demonstrate the terrific impact of this method for any household load shape.

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