• Energy Research
• 13. Climate action close

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).

Nowadays, different schemes and ways are proposed to meet the user's load requirement of energy towards the Demand Side (DS) in order to encapsulate the energy resources. However, this Load Demand (LD) increases day by day. This increase in LD is causing serious energy crises to the utility and DS. As the usage of energy increases with the increase in user's demand respectively, the peak is increased in these hours which affect the customer's in term of high-cost prices. This issue is tackled using some schemes and their proper integration. Two-way communication is done by the utility through Smart Grid (SG) between utility and customers. Customers that show some good behavior and helps the utility to control this LD, can perform a key role here. In this paper, our main focus is to control the Customer Side Management (CSM) by reducing the peak generation from on-peak hours. In our scenario, we focus on saving the cost expenditure of users by giving them comfort and shifting the load of appliances from high LD hours to low LD hours. In this study, we adopt the optimization algorithms, like Bacterial Foraging Optimization Algorithm (BFOA), Flower Pollination Algorithm (FPA) and proposed our Hybrid Bacterial Flower Pollination Algorithm (HBFPA) to optimize the solution of our problem using the famous electricity scheme named as Critical Peak Pricing(CPP) with three different Operational Time intervals (OTIs). Simulations and results show that our scheme reduces the cost and peak to the average ratio by proper shifting the appliances from highly load demanding hours to the low demanding hours with the negligibly small difference between the maximum and minimum 90% of confidence interval.