Integrated energy optimization of smart home appliances with cost-effective energy management system (Case Study: Mid-Size Residential Home at Hawassa City

Abstract

With the development of smart grid technology, residents have the opportunity to schedule their power consumption pattern in their home by themselves for the purpose of minimizing electricity expense, reducing the peak-to-average ratio (PAR) and peak load demand. The two way flow of information between electric utilities and consumers in smart grid opened new areas of applications. In this thesis, a general architecture of home energy management system (HEMS) would be introduced in a home area network (HAN) based on the smart grid scenario and then an efficient scheduling method for home power usage would be proposed. The energy management controller (EMC) receives the demand response (DR) information indicating the Time-of use electricity price (TOUP) through the home gateway (HG). With the DR signal, the EMC achieves an optimal power scheduling scheme that can be delivered to each electric appliance by the HG. The rooftop photovoltaic system used as local generation micro grid in the home and can be integrated to the national grid. These results in reduction of electricity bill and when solar energy production is more than the demand of the home, the excess energy could be exported to the national grid. Accordingly, all appliances in the home operate automatically in the most cost-effective way. Detail explanation and discussion of four scheduling techniques would be designed under TOUP. To calculate the fitness function; two powerful swarm intelligence based Meta-heuristic algorithms i.e. Grey wolf optimizer (GWO) and Particle swarm optimization (PSO) algorithms had been used. Finally, detail comparison and analysis between proposed methods and algorithms could be carried out. To display the result, MAT LAB 16(a) software environment would be used