Genetic Algorithm and Earthworm Optimization Algorithm for Energy Management in Smart Grid

In smart grid several scheduling techniques have been proposed for load management in commercial, industrial and residential areas to minimize electricity cost, Peak to Average ratio (PAR) and provide user comfort maximization. Demand Side Management (DSM) is necessary for optimized results. Smart grid is a digital technology with two-way communication between the utility company and electricity consumers. Energy Management Controller (EMC) are used to maintain record of all appliances, operation time of appliances and cost which we have to pay for it. Smart grid motivates users to shift the load in Off Peak Hours (OPH) form Peak Hours (PH) through providing incentive in OPH. By this act consumers save money against load shifting from high price hours to low price hours. In this paper, Genetic Algorithm (GA) and Earthworm Optimization Algorithm (EWA) based schemes is proposed to minimize electricity cost and Peak to Average Ratio (PAR) while maximizing User Comfort (UC) via appliances scheduling.