GreyWolf Optimization Technique for HEMS Using Day Ahead Pricing Scheme

With the emergence of Smart Grid, users adopt different scheduling methods to reduce their energy consumption with different objectives. In this paper, we implemented a Meta heuristic techniques named as Grey Wolf Optimizer (GWO) and Bacterial Foraging Algorithm (BFA) for Home Energy Management System (HEMS). We implemented these techniques due to the inspiration from the working behavior of GW and B. In GW, wolves are categorized into four forms namely Alpha, Beta, Delta and Omega. There are three major steps in GW for getting their prey which are first searching the prey then encircling the prey and finally attacking the prey. We proposed a generic architecture of Demand Side management (DSM) that incorporates residential area domain. We use Day Ahead Pricing (DAP) to calculate the cost of energy consumption. Results are compared with the BFA. Results show that GWO has better performance as compared to the other Meta heuristic technique BFA. GWO effectively reduces the cost of energy consumption as compared to BFA. Therefore implementation of this technique is useful for both users and utility.