Enhanced Differential Evolution and Crow Search Algorithm Based Home Energy Management in Smart Grid

Abstract

In this paper, we used two techniques: Enhanced Differential Evolution (EDE) and Crow Search Algorithm (CSA), in order to evaluate the performance of Home Energy Management System (HEMS). The total load is categorized into three groups based on their energy consumption pattern, and time of use of appliances. Critical Peak Pricing (CPP) scheme is used to calculate electricity bill. Our goals are electricity cost reduction, energy consumption minimization, Peak to Average Ratio (PAR) minimization, and user comfort maximization. However, there is trade-off between multiple objectives (goals). The simulation results show that, there is trade-off between PAR and total cost, and there is trade-off as well between PAR and waiting time. The simulation results also show that CSA performs better in terms of total cost and user comfort than EDE and unscheduled.