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User Comfort Oriented Residential Power Scheduling for Smart Homes
Abstract and Figures
Smart grid (SG) is an emerging technology which is considered as an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of SG, which aims at provision of demand side management (DSM) mechanisms, such as demand response (DR). In this thesis, we propose teacher learning genetic optimization (TLGO) technique by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspect which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power flexible appliances on consumers' bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
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