Multiobjective optimization for carbon market scheduling based on behavior learning

Li, Dan; Hua, Weiqi; Sun, Hongjian; Chiu, Wei-Yu

doi:10.1016/j.egypro.2017.12.581

Multiobjective optimization for carbon market scheduling based on behavior learning

Li, Dan; Hua, Weiqi; Sun, Hongjian; Chiu, Wei-Yu

Authors

Dan Li

Weiqi Hua

Professor Hongjian Sun hongjian.sun@durham.ac.uk
Professor

Wei-Yu Chiu

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.

Citation

Li, D., Hua, W., Sun, H., & Chiu, W. (2017). Multiobjective optimization for carbon market scheduling based on behavior learning. . https://doi.org/10.1016/j.egypro.2017.12.581

Acceptance Date	May 30, 2017
Online Publication Date	Jan 31, 2018
Publication Date	Dec 1, 2017
Deposit Date	Aug 1, 2017
Publicly Available Date	Aug 1, 2017
Volume	142
Pages	2089-2094
Series ISSN	1876-6102
DOI	https://doi.org/10.1016/j.egypro.2017.12.581