Demand Side Management Using Meta-Heuristic Techniques and ToU in Smart Grid

Abstract

In this paper, we perform performance evaluation of home energy management system (HEMS) for demand side management (DSM) in smart grid. In this work, smart home is equipped with HEMS, smart meter, and smart appliances for two-way communication between utility and consumer. HEMS performs scheduling of smart appliances based on meta-heuristic techniques to balance load for whole day to avoid peak creation in any hour. Smart meter performs electricity cost calculation for consumed energy based on time of use (ToU) pricing signal provided by utility. Our focus is to efficiently handle user demand, reduction in peak-to-average ratio (PAR) and electricity cost minimization. The implemented meta-heuristic techniques in this work are: Enhanced differential evolution (EDE), harmony search algorithm (HSA), bacterial foraging algorithm (BFA), and genetic algorithm (GA). The simulation results show the performance of HEMS based on optimization techniques using ToU.