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Multiobjective Optimization for Demand Side Management Program in Smart Grid
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DOI：10.1109/TII.2017.2776104.png)
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摘要
En 中文
Demand side management (DSM) plays an important role in smart grid for paving the way to a low-carbon future. In this paper, a hierarchical day-ahead DSM model is proposed, where renewable energy sources are integrated. The proposed model consists of three layers: the utility in the upper layer, the demand response (DR) aggregator in the middle layer, and customers in the lower layer. The utility seeks to minimize the operation cost and give part of the revenue to the DR aggregator as a bonus. The DR aggregator acts as an intermediary, receiving bonus from the utility and giving compensation to customers for modifying their energy usage pattern. The aim of the DR aggregator is to maximize its net benefit. Customers desire to maximize the social welfare, i.e., the received compensation minus the dissatisfactory level. To achieve these objectives, a multiobjective problem is formulated. An artificial immune algorithm is used to solve this problem, leading to a Pareto optimal set. Using a selection criterion, a Pareto optimal solution can be selected, which does not favour any particular participant to ensure the overall fairness. Simulation results confirm the feasibility of the proposed method: The utility can reduce the operation cost and the peak to average ratio; the DR aggregator can make a profit for providing DSM services; and customers can reduce their bill.
Keyword:
Artificial immune algorithm (AIA)
demand response (DR) aggregator
demand side management (DSM)
multiobjective problem (MOP)
pareto optimality
renewable energy sources (RESs)
smart grid
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