TY - JOUR
T1 - Sustainable Operation of CCUS Units Under Low-Carbon Economics
AU - Song, Zhenzi
AU - Wang, Xiuli
AU - Zhao, Tianyang
AU - Qian, Tao
AU - Zhang, Libo
AU - Qi, Buyang
AU - Wang, Yifei
N1 - Publisher Copyright:
© 2004-2012 IEEE.
PY - 2025
Y1 - 2025
N2 - To ensure the sustainable operation of the Carbon Capture, Utilization, and Storage (CCUS) units, a novel cooperative scheme is proposed for CCUS units and wind farm clusters (WFCs) within integrated markets of electricity, carbon emission trading (CET), and green certificate trading (GCT), incorporating tradable green certificate (TGC) offset mechanism. This scheme, utilizing the Asymmetric Nash Bargaining (ANB) theory, is divided into two sub-problems: energy trading and benefit allocation. In the energy trading problem, uncertainties in electricity prices and wind power are addressed using a data-driven distributionally robust (D-DRO) method to maximize the expected utility, and a model-oriented Benders Decomposition (BD) algorithm is proposed to ensure privacy and computational efficiency. For the benefit allocation problem, an analytical method based on Karush-Kuhn-Tucker (KKT) conditions is employed to achieve a rational and fair allocation, considering each participant’s bargaining power. Numerical experiments in small-scale and large-scale systems indicate that CCUS unit revenue has increased by 14.09% and 7.28%, respectively, with the proposed scheme. Additionally, when solving the energy trading problem considering the refined AA model for electrolyzer clusters, the computational time has accelerated by 28.56 times and 66.55 times, while ensuring solution quality.
AB - To ensure the sustainable operation of the Carbon Capture, Utilization, and Storage (CCUS) units, a novel cooperative scheme is proposed for CCUS units and wind farm clusters (WFCs) within integrated markets of electricity, carbon emission trading (CET), and green certificate trading (GCT), incorporating tradable green certificate (TGC) offset mechanism. This scheme, utilizing the Asymmetric Nash Bargaining (ANB) theory, is divided into two sub-problems: energy trading and benefit allocation. In the energy trading problem, uncertainties in electricity prices and wind power are addressed using a data-driven distributionally robust (D-DRO) method to maximize the expected utility, and a model-oriented Benders Decomposition (BD) algorithm is proposed to ensure privacy and computational efficiency. For the benefit allocation problem, an analytical method based on Karush-Kuhn-Tucker (KKT) conditions is employed to achieve a rational and fair allocation, considering each participant’s bargaining power. Numerical experiments in small-scale and large-scale systems indicate that CCUS unit revenue has increased by 14.09% and 7.28%, respectively, with the proposed scheme. Additionally, when solving the energy trading problem considering the refined AA model for electrolyzer clusters, the computational time has accelerated by 28.56 times and 66.55 times, while ensuring solution quality.
KW - ANB
KW - CCUS
KW - Sustainable operation
KW - multi-market environment
KW - privacy preservation
UR - https://www.scopus.com/pages/publications/105003030883
U2 - 10.1109/TASE.2025.3551163
DO - 10.1109/TASE.2025.3551163
M3 - 文章
AN - SCOPUS:105003030883
SN - 1545-5955
VL - 22
SP - 13101
EP - 13116
JO - IEEE Transactions on Automation Science and Engineering
JF - IEEE Transactions on Automation Science and Engineering
ER -