Optimal Real-Time Pricing-Based Scheduling in Home Energy Management System Using Genetic Algorithms

Smart grids are advanced technologies that have already demonstrated a great potential to maintain balance between the demand and supply of electricity via implementation of demand response (DR). A home energy management system (HEMS) is projected to enable DR applications for residential consumers by monitoring, managing, and controlling their energy consumption. This paper proposes a Genetic Algorithm (GA) based HEMS that reallocates and shifts appliances away from peak consumption periods and high electricity prices in order to minimize the electricity bill and the peak to average ratio (PAR). The HEMS receives electricity prices, which are based on real-time pricing (RTP) scheme, from a smart meter and finds the schedule that minimizes the cost and PAR. Our proposed model categorizes appliances into three categories: 1- shift-able interruptible appliances and 2- shift-able uninterruptible appliances and finally 3- fixed base appliances. A new formulation of the problem, which neglects redundant information and considerably reduces the search space, was developed and tested. The results show a substantial improvement in the scheduling problem compared to the conventional formulation reported in literature. The proposed system was able to effectively reduce the cost by 8.7% and PAR by 29%.