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In this paper, a detailed analysis of the impact of a day-ahead residential demand response model on the winter season of Jordan’s power sector is presented and discussed. The model used is based on a... deep neural network that was trained on four years of Jordan’s electrical demand data and a profit-based day-ahead demand response optimization. The day-ahead demand response model was established based on the predicted day-ahead demand and a demand response model conducted by Jordan’s Grid operator (GO) being NEPCO to reduce its energy costs from the power Generator (PGs) by applying a day-ahead peak period pricing scheme on the service providers (SPs). The results of applying the DR model on the winter season showed that a potential peak reduction of 4.49% to 8.19% could be achieved as well as a cost reduction of 64,263$ to 265,411$ per day.続きを見る
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