An Efficient Scheduling of Electrical Appliance in Micro Grid Based on Heuristic Techniques
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Unlike existing centralized grids, smart grids (SGs) have the ability to reduce fossil fuel combustion and carbon emissions up to a significant mark. Smart homes and smart building are becoming more attractable due to low energy consumption and high comfort. Different demand side management (DSM) programs have been proposed to involve users in decision making process of SGs. Power consumption pattern of shiftable home appliances is modified in response of some rebates to achieve certain benefits. In this paper, an energy management model is proposed using genetic algorithm (GA), teaching learning based optimization (TLBO), enhanced differential evolution (EDE) algorithm and our novel proposed EDTLA. The main objectives include: daily electricity bill minimization, peak to average ratio reduction and user comfort maximization. Simulation results validate the performance and applicability of our proposed model.
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