Carbon reduction cost estimating of Chinese coal-fired power generation units: A perspective from national energy consumption standard
Carbon reduction cost estimating of Chinese coal-fired power generation units: A perspective from national energy consumption standard
复制标题
中国燃煤发电机组减碳成本测算：基于国家能耗标准的视角
DOI：
10.1016/j.jclepro.2016.08.066
复制
发表时间：
2016-12
影响因子：
11.1
通讯作者：
Jinhua Cheng
中科院分区：
文献类型：
--
作者：
Shiwei Yu;Junjie Zhang;Jinhua Cheng
燃煤发电行业是中国二氧化碳排放的主要来源。本文提出了一种基于计算中国国家单位能耗标准（NECS）与单位单位供电煤耗（CCES）之间排放差距的碳减排成本估算模型。通过应用所提出的模型，在六种组合情景下估算了有或没有碳捕集与封存 (CCS) 的四个燃煤发电机组（600 MW USC、1000 MW USC、265 MW IGCC、600 MW SC CFB）的减排成本。结果表明：（1）实施CCS后，单位总成本增加一倍以上，单位碳减排成本降低64%以上； （2）在所有情景下，采用CCS的1000MW USC机组的碳减排潜力最大，单位减排成本最低； （3）265MW IGCC机组使用25年后无碳减排潜力，可缩短使用年限； (4) 256 MW IGCC 和 600 MW SC CFB 对 NECS 年均递减率最敏感，而已安装的 4 台 CCS 则不然。
The coal-fired power sector is the main source of CO2emissions in China. This paper proposes a carbon abatement cost estimate model based on computing the emissions gap between the Chinese national energy consumption standards of units (NECS) and the units' coal consumption for per unit electricity supply (CCES). By applying the proposed model, the abatement costs of four coal-fired power generation units (600 MW USC, 1000 MW USC, 265 MW IGCC, 600 MW SC CFB) with or without Carbon capture and storage (CCS) are estimated in six combined scenarios. The results show that (1) the units' total cost would increase more than one time after conducting CCS while the unit cost of carbon emissions abatement would decrease more than 64%; (2) the 1000 MW USC unit with CCS has the largest carbon reduction potential and the lowest unit abatement cost among all units in all scenarios; (3) 265 MW IGCC unit would have no carbon reduction potential after 25 service years, and its service years may be shortened; and (4) the 256 MW IGCC and 600 MW SC CFB is the most sensitive to the average annual decline rate of NECS but the four units of installed CCS are not.
登录
查看更多内容
中国省际碳强度减排潜力估算：PSO-GA优化的多因素环境学习曲线方法
影响因子：
9
作者：
Shiwei Yu;Junjie Zhang;Shuhong Zheng;Han Sun
通讯作者：
Han Sun
影响因子：
9
作者：
Zhaohua Wang;Zhongmin Yang;Yixiang Zhang;Jianhua Yin
通讯作者：
Zhaohua Wang;Zhongmin Yang;Yixiang Zhang;Jianhua Yin
影响因子：
10.4
作者：
G. Ordorica-garcia;P. Douglas;E. Croiset;Ligang Zheng
通讯作者：
G. Ordorica-garcia;P. Douglas;E. Croiset;Ligang Zheng
影响因子：
10.4
作者：
Dongjie Zhang;Wang Zhe;Ji-ning Sun;Z. Lili;Li Zheng
通讯作者：
Dongjie Zhang;Wang Zhe;Ji-ning Sun;Z. Lili;Li Zheng
影响因子：
11.2
作者：
Jorge Islas;G. Grande
通讯作者：
Jorge Islas;G. Grande