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Multiobjective optimization for carbon market scheduling based on behavior learning.

Li, Dan and Hua, Weiqi and Sun, Hongjian and Chiu, Wei-Yu (2017) 'Multiobjective optimization for carbon market scheduling based on behavior learning.', Energy procedia., 142 . pp. 2089-2094.

Abstract

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:Article
Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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Full text:(VoR) Version of Record
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
Download PDF (Final published version)
(2089Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1016/j.egypro.2017.12.581
Publisher statement:© 2017 The Authors. Published by Elsevier Ltd.
Date accepted:30 May 2017
Date deposited:01 August 2017
Date of first online publication:31 January 2018
Date first made open access:No date available

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