Abstract
ABSTRACT A customized design for an efficient home energy management system for peak power limiting and energy routing-based demand response strategy is proposed in this paper. The proposed system encompasses acquisition of historic data of residential load, photovoltaic (PV) generation and electric vehicle (EV) details, followed by load and solar power generation data forecast using the regression tree model. Gaussian distribution is used to model EV-related information, viz. arrival and departure schedule, SoC of EV batteries, etc. The predicted profiles are used in the adopted optimization process to estimate future energy balances. The presented optimization framework is a sequential process using whale optimization algorithm and fuzzy logic to schedule the connection time of residential appliances and knapsack algorithm for energy routing from additional energy sources, like rooftop solar photovoltaic systems and electric vehicles. Finally, the methodology is tested for its robustness and flexibility considering the actual data of a residential community having a variety of consumers. Promising results obtained validate the effectiveness of the proposed work with an average 27% reduction in peak power drawn, 30.69% in peak-to-average ratio, and 6.5% in the consumer electricity bill in the community.
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