Abstract
Demand-side management (DSM) is performing an important role in the future of smart grid, by managing and monitoring the loads in a smart and optimal way. In the literature, several works contribute to the development of optimization methods to deal with DSM in residential sectors. Three common challenges are presented: (1) reducing the electricity payment, (2) reducing the peak-to-average ratio, and (3) maximizing the users’ comfort. A great number of publications carry out with the energy management in a smart house, which aimed at minimizing the electricity cost and maximizing the users’ comfort. However, those that integrate renewable energy sources (RESs) have not received much attention. To deal with this issue, this study presents a multiobjective optimization technique for residential DSM over a day. The main contributions of the present work are: first, a novel approach to control residential load, taking into consideration that maximization of the users’ comfort and minimization of the electricity cost, is established. Second, RESs and battery storage systems are introduced to reduce further the electricity payment and the discomfort. Third, a mixed-integer linear programming algorithm is used to optimally allocate the electrical power according to the objective function. Constraints, including daily energy requirements, user preferences and energy availability from solar panels and batteries are considered. Three scenarios are proposed: the first one adopts the main grid as the only existing source of energy. The second one combines the main grid and solar panels. And the last one is a combination of the main grid, solar panels, and energy storage system. The results obtained for the different scenarios reveal that the proposed method offers a maximum satisfaction of the user with a minimum cost and demonstrate an optimal use of solar panels and battery storage. Finally, the daily cost and waiting time are compared for the previously proposed scenarios.
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