An efficient home energy management system for automated residential demand response

Due to 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. On the other hand, it is obviously impossible to integrate a large scale of renewable energy sources (RES) without extensive participation of the demand side. We are looking for a way to provide the system operators with the capability of increasing the penetration of RES besides maintaining the reliability of the power grid via load management and flexibility in the demand side. The primary aim is to provide consumers with a simple smart controller which can result in EC and PAR reduction with respect to consumer preferences and convenience level. 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 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 approach. The simulation results demonstrate the terrific impact of this method for any household load shape.