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Multiobjective optimization for carbon market scheduling based on behavior learning

Li, Dan, Hua, Weiqi, Sun, Hongjian and Chiu, Wei-Yu 2017. Multiobjective optimization for carbon market scheduling based on behavior learning. Presented at: 9th International Conference on Applied Energy (ICAE2017), Cardiff, Wales, 21-24 August 2017. Elsevier, pp. 2089-2094. 10.1016/j.egypro.2017.12.581

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With advances of smart grid, the responsibility of carbon emission reduction can be fairly allocated to each participant in power networks through bidirectional communications. This paper proposes a hierarchical carbon market scheduling model to effectively realize carbon emission reduction. The policy makers in the upper level aim to maximize the effects of carbon emission reduction. They set out appropriate monetary incentives and emission allowances for both customers and generators. Considering restrictions from policy makers, both generators and customers in lower levels seek to minimize their operational costs and payment bills, respectively. To achieve these objectives, a multiobjective problem is formulated by forecasting market trends from a behavior learning model. The simulation results demonstrate that through the proposed approach the renewable penetration increases and the carbon emissions decrease. The benefits for each participant are analyzed as well.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Engineering
Publisher: Elsevier
ISSN: 1876-6102
Related URLs:
Date of First Compliant Deposit: 12 January 2021
Date of Acceptance: 1 December 2017
Last Modified: 12 Jan 2021 16:30

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