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In this paper, a demand-side energy management scheme is proposed to minimize the electricity cost and the peak-to-average ratio. A particle swarm optimization strategy is integrated with a game-theoretical approach in order to solve a nonconvex problem of appliance scheduling. The unknowns of the optimization problem are modeled from the user perspective taking in consideration the real-world constraints of appliances, which impose an almost constant power consumption during predefined working time periods and within time windows constrained by the users. The performance of the proposed method has been assessed with different user time constraints. The obtained results outperform a state of the art solution based on convex programming, especially when stringent user requirements are imposed. The peak-to-average ratio is reduced more than twice respect to the convex programming technique.
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Viani et al. (2017) studied this question.
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