Cost Optimization in Home Energy Management System Using Genetic Algorithm, Bat Algorithm and Hybrid Bat Genetic Algorithm

Abstract

Home energy management systems are widely used to cope up with the increasing demand for energy. They help to reduce carbon pollutants generated by excessive burning of fuel and natural resources required for energy generation. They also save the budget needed for installing new power plants. Price based automatic demand response (DR) techniques incorporated in these systems shift appliances from high price hours to low price hours to reduce electricity bills and peak to average ratio (PAR). In this paper, electricity load of home is categorized into three types: base load, shift-able interruptible load and shiftable non-interruptible load. In literature many metaheuristic optimization techniques have been implemented for scheduling of appliances. In this work for the optimization of energy usage genetic algorithm (GA) and bat algorithm (BA) are implemented with time of use (TOU) pricing scheme to schedule appliances to reduce electricity bills, the peak to average ratio and appliance delay time. A new technique bat genetic algorithm (BGA) has been proposed. It is hybrid of GA and BA. It outperforms GA and BA in terms of cost reduction and peak to average ratio for single home scenario as well as multiple home scenario. Operation time internals (OTIs) 15 minutes, 30 minutes and 1 hour have been considered to check their effect on cost reduction, PAR and user comfort (UC).