期刊
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
卷 14, 期 4, 页码 1482-1490出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2017.2776104
关键词
Artificial immune algorithm (AIA); demand response (DR) aggregator; demand side management (DSM); multiobjective problem (MOP); pareto optimality; renewable energy sources (RESs); smart grid
类别
资金
- UK EPSRC [EP/P005950/1]
- European Commissions Horizon framework programme [734325]
- Ministry of Science and Technology of Taiwan [106-2221-E-007-127]
- Engineering and Physical Sciences Research Council [EP/P005950/1] Funding Source: researchfish
- EPSRC [EP/P005950/1] Funding Source: UKRI
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.
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