A Hybrid Genetic Based on Harmony Search Method to Schedule Electric Tasks in Smart Home
In the residential area, high electricity demand of power-consuming household tasks has become a crucial issue. Thus, the key objectives of home energy management system (HEMS) are scheduling power-consuming household tasks to minimize electricity cost and maximize consumer’s comfort. The many utilities offer residential demand response (DR) program to shift residential customer electricity consumption during the peak time period to match demand and supply. In this paper, we propose optimal load scheduling algorithm a hybrid genetic based on harmony search (HGHS) for HEMS to schedule power-consuming household tasks. The new optimal load scheduling algorithm HGHS gives optimal solution to schedule power-consuming household tasks based on real time pricing (RTP) electricity tariff within electricity task time window during the day in order to minimize electricity cost, reduce peak-to-average ratio (PAR) and maximize user comfort. The proposed model implemented in a single smart home and simulation results of proposed HGHS algorithm are compared with genetic algorithm (GA) and harmony search algorithm (HAS) and it provides better results in reducing the daily electricity cost and PAR by reducing load at peak hours. Simulation results shown that the trade-off between electricity cost reduction and user comfort exist in two conflicting objectives.
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