Multiobjective Optimization for Demand Side Management in Smart Grid

Li, Dan; Sun, Hongjian; Chiu, Wei-Yu; Poor, Vincent

Abstract

Demand side management (DSM) plays an important role in smart grid. In this paper, a hierarchical day-ahead DSM model is proposed, where renewable energy sources (RESs) 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 maximizing its net benefit. Customers desire to maximize their 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 favor 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 power peak to average ratio; the DR aggregator can make a profit for providing DSM services; and customers can reduce their bill.

Citation

Li, D., Sun, H., Chiu, W., & Poor, V. (2018). Multiobjective Optimization for Demand Side Management in Smart Grid. IEEE Transactions on Industrial Informatics, 14(4), 1482-1490. https://doi.org/10.1109/tii.2017.2776104

• Journal Article Type: Article
• Acceptance Date: Oct 23, 2017
• Online Publication Date: Dec 11, 2017
• Publication Date: Apr 1, 2018
• Deposit Date: Nov 17, 2017
• Publicly Available Date: Jan 12, 2018
• Journal: IEEE Transactions on Industrial Informatics
• Print ISSN: 1551-3203
• Electronic ISSN: 1941-0050
• Publisher: Institute of Electrical and Electronics Engineers
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 4
• Pages: 1482-1490
• DOI: https://doi.org/10.1109/tii.2017.2776104
• Public URL: https://durham-repository.worktribe.com/output/1371055