环境科学研究  2018, Vol. 31 Issue (1): 187-193  DOI: 10.13198/j.issn.1001-6929.2017.03.35

引用本文

ZHANG Ning, HE Shutong, WANG Junfeng, et al. Carbon Intensity and Benchmarking Analysis of Power Industry in Tianjin under the Context of Cap-and-Trade[J]. Research of Environmental Sciences, 2018, 31(1): 187-193.

基金项目

Supported by Grant Projects from China CDM Fund (No.2014130); National Natural Science Foundation of China (No.71373134)

文章历史

1. 天津市环境保护科学研究院, 天津 300191;
2. 南开大学循环经济与低碳发展研究中心, 天津 300071;
3. 河北工业大学, 天津 300401

Carbon Intensity and Benchmarking Analysis of Power Industry in Tianjin under the Context of Cap-and-Trade
ZHANG Ning1,3 , HE Shutong2 , WANG Junfeng2 , CHEN Ying1 , KANG Lei1
1. Tianjin Academy of Environmental Sciences, Tianjin 300191, China;
2. Circular Economy and Low Carbon Development Research Center, Nankai University, Tianjin 300071, China;
3. Hebei University of Technology, Tianjin 300401, China
Abstract: Power industry is one of the key participants in the cap-and-trade system of China. The research of regional carbon intensity and benchmarking of power industry conduces to proposing measures for regional carbon emissions reduction and meanwhile has great reference value for constructing the cap-and-trade system of China, especially for allowance allocation. By adopting data from 15 major power plants with 32 generator units in Tianjin City, we firstly calculated and analyzed the regional carbon intensity of power industry and then designed three scenarios for carbon emissions benchmarking along with an analysis of applicability for Tianjin City, which are actual emissions scenario, advanced value of current standards scenario and comprehensive emissions reduction scenario. The study shows: (1) In the industries with solid database and single product, adopting benchmarking method in allowance allocation is propitious to the rational distribution of carbon markets resources, hence promoting the low-carbon development of regional power industry. (2) The carbon intensity of Tianjin's power industry is 822.9 g/(kW·h) in 2014 and the intensity for coal-fired power and gas-fired power are 824.4 and 502.0 g/(kW·h) respectively. (3) Carbon intensity of electricity generation reflects the level of energy consumption and management of specific units. Coal-fired power units with high pressure and capacity conduce to the reduction of regional carbon emission from power industry. (4) Comprehensive emission reduction scenario is suitable for regions lacking local units, which is set under the consideration of both local carbon emission level and the benchmarking of other pilot provinces. As the toughest sets of benchmarking, the scenario might bring more pressure on the power companies, but the incentive effect for regional reduction is non-negligible, especially for companies running low capacity units with relatively high intensity.
Keywords: power industry    carbon intensity    benchmarking    cap-and-trade    allowance allocation

1 研究方法 1.1 电力行业碳排放量与排放强度计算方法

a) 化石燃料燃烧产生的CO2排放

 ${E_{{\rm{C}}{{\rm{O}}_2}-{\rm{ff}}}} = \sum\limits_{i = 1}^n {\left( {{\rm{A}}{{\rm{D}}_i} \times {H_i} \times {F_{{\rm{c}}{{\rm{h}}_i}}} \times {F_{{\rm{o}}{{\rm{x}}_i}}} \times \frac{{44}}{{12}}} \right)}$ (1)

b) 石灰石-石膏湿法脱硫过程产生的CO2排放

 ${E_{{\rm{C}}{{\rm{O}}_2}-{\rm{ff}}}} = {\rm{A}}{{\rm{D}}_i} \times S \times \eta \times \frac{{44}}{{12}}$ (2)

c) 机组及天津市发电碳排放强度

 ${E_{{\rm{C}}{{\rm{O}}_2}-j}} = \frac{{{E_{{\rm{C}}{{\rm{O}}_2}-{\rm{f}}{{\rm{f}}_j}}} + {E_{{\rm{C}}{{\rm{O}}_2}-{\rm{p}}{{\rm{p}}_j}}}}}{{{\rm{P}}{{\rm{G}}_j}}}$ (3)

 ${E_{{\rm{C}}{{\rm{O}}_2}-{\rm{TJ}}}} = \frac{{\sum\limits_{j = 1}^n {\left( {{E_{{\rm{C}}{{\rm{O}}_2}-{\rm{f}}{{\rm{f}}_j}}} + {E_{{\rm{C}}{{\rm{O}}_2}-{\rm{p}}{{\rm{p}}_j}}}} \right)} }}{{\sum\limits_{j = 1}^n {{\rm{P}}{{\rm{G}}_j}} }}$ (4)

1.2 行业基准线情景设置方法

1.3 数据来源

a) 企业发电锅炉所用烟煤、天然气低位发热值为符合《指南》监测标准的监测值；柴油低位发热值采用《指南》中的缺省值.

b) 烟煤、天然气、柴油单位热值含碳量、氧化率等相关参数均采用《指南》中的缺省值.

c) 煤的收到基全硫采用监测分析仪读数或按照GB/T 214—2007《煤中全硫的测定方法》进行测定.

d) 石灰石-石膏湿法脱硫效率为企业在线监测系统年平均数据.

2 结果与讨论 2.1 电力行业碳排放强度

 图 1 天津市火力发电煤耗水平比较 Fig.1 The Comparison on coal consumption of thermal power generation in Tianjin

 图 2 天津市各类机组发电量与碳排放强度 Fig.2 Carbon emissions and carbon intensity of each group in Tianjin

2.2 电力行业基准线情景设置与分析

3 结论

a) 在数据和统计基础较好、产品单一的行业采用基准线法进行配额分配，有利于碳市场资源的公平、合理配置；该研究结果丰富了对我国北方地区电力行业基准线的研究，对天津市案例进行了3种基准线情景设置，有利于深化天津碳排放权交易试点配额分配工作，可有效促进区域电力行业低碳发展.

b) 天津市2014年电力行业碳排放强度为822.9 g/(kW·h)，煤电碳排放平均强度为824.4 g/(kW·h)，天然气发电碳排放强度为502.0 g/(kW·h)；天然气发电的碳排放强度显著低于燃煤发电，增加天然气发电比例有助于区域电力行业CO2减排.

c) 天津市300 MW以上机组占天津市总发电量近80%，其中亚临界300 MW为天津市主力发电机组；发电碳排放强度可反映出单台机组的能耗和管理水平，对于燃煤机组，压力越高、机组容量越大，其发电碳排放强度越小.

d) 综合减排情景设置方法既考虑了本地区电力行业碳排放水平，同时参考了其他省市基准线设定，对部分类型机组数量较少、代表性不足的地区适用性更强；该情景对地区电力行业低碳水平要求最为严格，虽然为企业减排带来一定压力，但更有利于区域行业减排，且对于排放强度较高的较小容量机组能够起到更强的激励作用.