Article(id=1236345819956302477, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202403045, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1709913600000, receivedDateStr=2024-03-09, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772697413672, onlineDateStr=2026-03-05, pubDate=1729785600000, pubDateStr=2024-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772697413672, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772697413672, creator=13701087609, updateTime=1772697413672, updator=13701087609, issue=Issue{id=1236345813933289655, tenantId=1146029695717560320, journalId=1210938733613449225, year='2024', volume='53', issue='10', pageStart='1', pageEnd='162', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772697412236, creator=13701087609, updateTime=1772697498476, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236346175725556508, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236346175725556509, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=114, endPage=121, ext={EN=ArticleExt(id=1236345820283458201, articleId=1236345819956302477, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Carbon emission performance calculation and impact analysis of gas-steam combined cycle, columnId=1211002405299294959, journalTitle=Thermal Power Generation, columnName=Thermal energy science research, runingTitle=null, highlight=null, articleAbstract=

To improve the accuracy of carbon emission accounting and make the effect of carbon emission reduction more intuitive, it is proposed to associate power plant generation with carbon emission intensity. Firstly, the carbon emission performance of the gas-steam combined cycle unit is calculated based on Aspen Plus. Then, the carbon emission performance is analyzed from the aspects of four influencing factors: unit load, environmental temperature, heat network input and natural gas composition. The results show that, the established Aspen Plus model can simulate the operation of the power plant accurately. Taking the S106FA multi-axis gas-steam combined cycle unit of a power plant as an example, the calculated carbon emission performance is 342.66 g/(kW·h). The carbon emission is calculated by comparing the measured method and the emission factor method. The carbon emission performance accounting is closer to the measured method, and the deviation between the carbon emission performance method and the measured method is 0.20%. The deviation between the measured method and the emission factor method applying the measured low calorific value and the saved and deficient low calorific value are 5.24% and 19.66%, respectively. Unit load has the most obvious effect on carbon emission performance of the combined cycle unit, followed by heat network input, ambient temperature and natural gas composition. To reduce the carbon emission performance of the combined cycle unit, the power plant needs to arrange the peak regulation time and heat network heating reasonably, and using renewable energy as an alternative or supplementary fuel can be considered.

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为提高碳排放核算的准确性,使企业评估碳减排效果更加直观,提出将电厂发电量与碳排放强度相关联。首先,基于Aspen Plus对燃气-蒸汽联合循环机组进行建模分析,计算其碳排放绩效;然后,从机组负荷、环境温度、热网投入和天然气成分4个影响因素分别对碳排放绩效进行分析。结果表明,建立的Aspen Plus模型能准确模拟电厂运行,以某电厂S106FA多轴燃气-蒸汽联合循环机组为例,经计算碳排放绩效为342.66 g/(kW·h)。采用碳排放绩效对比实测法与排放因子法对碳排放进行计算,碳排放绩效核算碳排放量更贴近实测法计算结果,碳排放绩效法与实测法核算偏差在0.20%。排放因子法采用实测法低位发热量和省缺值低位发热量与实测法偏差分别为5.24%和19.66%;机组负荷对联合循环机组碳排放绩效影响最为显著,其次为热网投入、环境温度和天然气成分。为降低联合循环机组碳排放绩效,电厂需要合理安排调峰时间和热网供热,可以考虑使用可再生能源作为替代或补充燃料。

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刘松涛(1980),男,博士,副教授,主要研究方向为煤的清洁燃烧及污染物排放控制技术,
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孙天祥(1998),男,硕士研究生,主要研究方向为火力发电碳排放绩效评估模型,

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孙天祥(1998),男,硕士研究生,主要研究方向为火力发电碳排放绩效评估模型,

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Guidelines for accounting methods and reporting of power generation facilities[R]. Beijing: Ministry of Ecology and Environment, 2022: 1., articleTitle=Guidelines for accounting methods and reporting of power generation facilities, refAbstract=null)], funds=[Fund(id=1236345828332327049, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, awardId=1400-202214404A-2-0-ZN, language=EN, fundingSource=Science and Technology Project of Funding from State Grid Corporation of China(1400-202214404A-2-0-ZN), fundOrder=null, country=null), Fund(id=1236345828600762509, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, awardId=1400-202214404A-2-0-ZN, language=CN, fundingSource=国家电网有限公司科技项目(1400-202214404A-2-0-ZN), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1236345821952791269, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, xref=1., ext=[AuthorCompanyExt(id=1236345821961179878, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, companyId=1236345821952791269, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.Department of environmental Science and Engineering, North China Electric Power University (Baoding), Baoding 071003, China), AuthorCompanyExt(id=1236345821969568487, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, companyId=1236345821952791269, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.华北电力大学(保定)环境科学与工程系,河北 保定 071003)]), AuthorCompany(id=1236345822061843179, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, xref=2., ext=[AuthorCompanyExt(id=1236345822070231789, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, companyId=1236345822061843179, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.Jibei Electric Power Research Institute, State Grid Jibei Electric Power Co., Ltd., Beijing 100045, China), AuthorCompanyExt(id=1236345822078620398, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, companyId=1236345822061843179, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.国网冀北电力有限公司电力科学研究院,北京 100045)])], figs=[ArticleFig(id=1236345825463423991, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Fig.1, caption=Aspen flow diagram for the gas-steam combined cycle, figureFileSmall=B6LUikmqv5GIP5/go7cO5A==, figureFileBig=xekuDMzcAqi7V0fR2zQwWw==, tableContent=null), ArticleFig(id=1236345825526338557, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=图1, caption=燃气-蒸汽联合循环Aspen流程, figureFileSmall=B6LUikmqv5GIP5/go7cO5A==, figureFileBig=xekuDMzcAqi7V0fR2zQwWw==, tableContent=null), ArticleFig(id=1236345825727664139, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Fig.2, caption=The effect of different loads on carbon emission performance, figureFileSmall=jeRLf7XQe5rOKgIaOn4YYg==, figureFileBig=gXPBzfz6wmN+tqPwR+8CFA==, tableContent=null), ArticleFig(id=1236345825824133133, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=图2, caption=不同负荷对碳排放绩效影响, figureFileSmall=jeRLf7XQe5rOKgIaOn4YYg==, figureFileBig=gXPBzfz6wmN+tqPwR+8CFA==, tableContent=null), ArticleFig(id=1236345825937379352, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Fig.3, caption=Effect of heat network input on carbon emission performance, figureFileSmall=wwtJA/S+11reIf/a2iRy+w==, figureFileBig=cvFVKe3/dFsHExSNp0Cuaw==, tableContent=null), ArticleFig(id=1236345826059014175, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=图3, caption=热网投入对碳排放绩效影响, figureFileSmall=wwtJA/S+11reIf/a2iRy+w==, figureFileBig=cvFVKe3/dFsHExSNp0Cuaw==, tableContent=null), ArticleFig(id=1236345826142900259, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Fig.4, caption=Effect of ambient temperature on carbon emission performance, figureFileSmall=0uX6XX0JuXdxZDsI3EekYg==, figureFileBig=ubpdml/bfOxOaMQLcGY44w==, tableContent=null), ArticleFig(id=1236345826264535084, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=图4, caption=环境温度对碳排放绩效影响, figureFileSmall=0uX6XX0JuXdxZDsI3EekYg==, figureFileBig=ubpdml/bfOxOaMQLcGY44w==, tableContent=null), ArticleFig(id=1236345826373586994, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Fig.5, caption=Effect of natural gas composition on carbon emission performance, figureFileSmall=co8/f+cVAT/gSxRQToNxkg==, figureFileBig=cUgye8RIRMzIHWbqrCVZrA==, tableContent=null), ArticleFig(id=1236345826457473079, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=图5, caption=天然气成分对碳排放绩效影响, figureFileSmall=co8/f+cVAT/gSxRQToNxkg==, figureFileBig=cUgye8RIRMzIHWbqrCVZrA==, tableContent=null), ArticleFig(id=1236345826545553469, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Tab.1, caption=

Parameters related to double-pressure HRSG

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
空气流量/(t·h–1)731.20
天然气流量/(t·h–1)16.10
排气温度/℃602.60
排烟温度/℃89.06
高压蒸汽温度/℃536.83
高压蒸汽压力/kPa5 305.20
低压蒸汽温度/℃252.69
低压蒸汽压力/kPa550.70
给水温度/℃23.06
给水压力/kPa230.20
), ArticleFig(id=1236345826637828162, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=表1, caption=

双压余热锅炉相关参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
空气流量/(t·h–1)731.20
天然气流量/(t·h–1)16.10
排气温度/℃602.60
排烟温度/℃89.06
高压蒸汽温度/℃536.83
高压蒸汽压力/kPa5 305.20
低压蒸汽温度/℃252.69
低压蒸汽压力/kPa550.70
给水温度/℃23.06
给水压力/kPa230.20
), ArticleFig(id=1236345826763657289, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Tab.2, caption=

Error analysis of the simulation results

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项目实际系统模拟系统
空气流量/(t·h–1)731.20731.20
天然气流量/(t·h–1)16.1016.10
排气温度/℃602.60602.60
排烟温度/℃89.6089.06
高压蒸汽流量/(t·h–1)114.11114.12
高压蒸汽温度/℃538.83583.83
高压蒸汽压力/kPa5 5055 505
低压蒸汽流量/(t·h–1)15.6115.61
低压蒸汽温度/℃252.69254.11
低压蒸汽压力/kPa550.70550.70
汽轮机输出功率/MW37.9737.97
), ArticleFig(id=1236345826914652237, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=表2, caption=

模拟结果误差分析

, figureFileSmall=null, figureFileBig=null, tableContent=
项目实际系统模拟系统
空气流量/(t·h–1)731.20731.20
天然气流量/(t·h–1)16.1016.10
排气温度/℃602.60602.60
排烟温度/℃89.6089.06
高压蒸汽流量/(t·h–1)114.11114.12
高压蒸汽温度/℃538.83583.83
高压蒸汽压力/kPa5 5055 505
低压蒸汽流量/(t·h–1)15.6115.61
低压蒸汽温度/℃252.69254.11
低压蒸汽压力/kPa550.70550.70
汽轮机输出功率/MW37.9737.97
), ArticleFig(id=1236345827053064276, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Tab.3, caption=

Carbon performance results

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
联合功率/MW125.40
天然气消耗率/(g·(kW·h)–1)130.45
碳排放绩效/(g·(kW·h)–1)342.66
碳排放量/t507.49
), ArticleFig(id=1236345827166310490, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=表3, caption=

碳排放绩效结果

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
联合功率/MW125.40
天然气消耗率/(g·(kW·h)–1)130.45
碳排放绩效/(g·(kW·h)–1)342.66
碳排放量/t507.49
), ArticleFig(id=1236345827401191514, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Tab.4, caption=

Composition of the natural gas

, figureFileSmall=null, figureFileBig=null, tableContent=
天然气成分数值
甲烷(体积分数,下同)/%95.949 4
乙烷/%0.907 5
丙烷/%0.136 7
二氧化碳/%3.000 0
水/%0.006 2
硫化氢/%0.000 2
低位发热量/(MJ·m–3)34.24
), ArticleFig(id=1236345827510243426, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=表4, caption=

天然气成分

, figureFileSmall=null, figureFileBig=null, tableContent=
天然气成分数值
甲烷(体积分数,下同)/%95.949 4
乙烷/%0.907 5
丙烷/%0.136 7
二氧化碳/%3.000 0
水/%0.006 2
硫化氢/%0.000 2
低位发热量/(MJ·m–3)34.24
), ArticleFig(id=1236345827631878246, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Tab.5, caption=

List of equipment in the monitoring system

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设备型号
气体分析仪ABB EL3020
预处理机柜FT-91
采样器HGSP-1000
VPTVPT511NF
), ArticleFig(id=1236345827770290282, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=表5, caption=

监测系统设备清单

, figureFileSmall=null, figureFileBig=null, tableContent=
设备型号
气体分析仪ABB EL3020
预处理机柜FT-91
采样器HGSP-1000
VPTVPT511NF
), ArticleFig(id=1236345827862564976, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Tab.6, caption=

Carbon emissions calculated using the emission factor method

, figureFileSmall=null, figureFileBig=null, tableContent=
项目实测值省缺值
燃气消耗量/万m328.0528.05
低位发热量/(MJ·m–3)34.24038.931
单位热值含碳量/(t·GJ–1)0.015 30.015 3
碳氧化率/%9999
碳排放因子/(t·GJ–1)0.055 50.055 5
碳排放量/t533.03606.06
), ArticleFig(id=1236345827988394101, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=表6, caption=

基于排放因子法计算的碳排放量

, figureFileSmall=null, figureFileBig=null, tableContent=
项目实测值省缺值
燃气消耗量/万m328.0528.05
低位发热量/(MJ·m–3)34.24038.931
单位热值含碳量/(t·GJ–1)0.015 30.015 3
碳氧化率/%9999
碳排放因子/(t·GJ–1)0.055 50.055 5
碳排放量/t533.03606.06
), ArticleFig(id=1236345828072280188, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=EN, label=Tab.7, caption=

Steam parameters at different loads

, figureFileSmall=null, figureFileBig=null, tableContent=
THA0.8THA0.7THA0.5THA0.3THA
高压温度/℃536.83538.84538.84538.90538.89
高压压力/kPa5 305.204 712.304 542.204 063.403 346.00
低压温度/℃254.69246.06243.80239.56231.03
低压压力/kPa600.66478.10455.20417.06334.18
), ArticleFig(id=1236345828198109315, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345819956302477, language=CN, label=表7, caption=

不同负荷下蒸汽参数

, figureFileSmall=null, figureFileBig=null, tableContent=
THA0.8THA0.7THA0.5THA0.3THA
高压温度/℃536.83538.84538.84538.90538.89
高压压力/kPa5 305.204 712.304 542.204 063.403 346.00
低压温度/℃254.69246.06243.80239.56231.03
低压压力/kPa600.66478.10455.20417.06334.18
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燃气-蒸汽联合循环的碳排放绩效计算及影响分析
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孙天祥 1 , 刘松涛 1 , 曹悦 1 , 陈传敏 1 , 贾文波 1 , 李朋 2 , 周卫青 2
热力发电 | 热能科学研究 2024,53(10): 114-121
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热力发电 | 热能科学研究 2024, 53(10): 114-121
燃气-蒸汽联合循环的碳排放绩效计算及影响分析
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孙天祥1 , 刘松涛1 , 曹悦1, 陈传敏1, 贾文波1, 李朋2, 周卫青2
作者信息
  • 1.华北电力大学(保定)环境科学与工程系,河北 保定 071003
  • 2.国网冀北电力有限公司电力科学研究院,北京 100045
  • 孙天祥(1998),男,硕士研究生,主要研究方向为火力发电碳排放绩效评估模型,

通讯作者:

刘松涛(1980),男,博士,副教授,主要研究方向为煤的清洁燃烧及污染物排放控制技术,
Carbon emission performance calculation and impact analysis of gas-steam combined cycle
Tianxiang SUN1 , Songtao LIU1 , Yue CAO1, Chuanmin CHEN1, Wenbo JIA1, Peng LI2, Weiqing ZHOU2
Affiliations
  • 1.Department of environmental Science and Engineering, North China Electric Power University (Baoding), Baoding 071003, China
  • 2.Jibei Electric Power Research Institute, State Grid Jibei Electric Power Co., Ltd., Beijing 100045, China
出版时间: 2024-10-25 doi: 10.19666/j.rlfd.202403045
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为提高碳排放核算的准确性,使企业评估碳减排效果更加直观,提出将电厂发电量与碳排放强度相关联。首先,基于Aspen Plus对燃气-蒸汽联合循环机组进行建模分析,计算其碳排放绩效;然后,从机组负荷、环境温度、热网投入和天然气成分4个影响因素分别对碳排放绩效进行分析。结果表明,建立的Aspen Plus模型能准确模拟电厂运行,以某电厂S106FA多轴燃气-蒸汽联合循环机组为例,经计算碳排放绩效为342.66 g/(kW·h)。采用碳排放绩效对比实测法与排放因子法对碳排放进行计算,碳排放绩效核算碳排放量更贴近实测法计算结果,碳排放绩效法与实测法核算偏差在0.20%。排放因子法采用实测法低位发热量和省缺值低位发热量与实测法偏差分别为5.24%和19.66%;机组负荷对联合循环机组碳排放绩效影响最为显著,其次为热网投入、环境温度和天然气成分。为降低联合循环机组碳排放绩效,电厂需要合理安排调峰时间和热网供热,可以考虑使用可再生能源作为替代或补充燃料。

燃气-蒸汽联合循环  /  Aspen Plus  /  模拟计算  /  碳排放绩效  /  影响因素

To improve the accuracy of carbon emission accounting and make the effect of carbon emission reduction more intuitive, it is proposed to associate power plant generation with carbon emission intensity. Firstly, the carbon emission performance of the gas-steam combined cycle unit is calculated based on Aspen Plus. Then, the carbon emission performance is analyzed from the aspects of four influencing factors: unit load, environmental temperature, heat network input and natural gas composition. The results show that, the established Aspen Plus model can simulate the operation of the power plant accurately. Taking the S106FA multi-axis gas-steam combined cycle unit of a power plant as an example, the calculated carbon emission performance is 342.66 g/(kW·h). The carbon emission is calculated by comparing the measured method and the emission factor method. The carbon emission performance accounting is closer to the measured method, and the deviation between the carbon emission performance method and the measured method is 0.20%. The deviation between the measured method and the emission factor method applying the measured low calorific value and the saved and deficient low calorific value are 5.24% and 19.66%, respectively. Unit load has the most obvious effect on carbon emission performance of the combined cycle unit, followed by heat network input, ambient temperature and natural gas composition. To reduce the carbon emission performance of the combined cycle unit, the power plant needs to arrange the peak regulation time and heat network heating reasonably, and using renewable energy as an alternative or supplementary fuel can be considered.

gas-steam combined cycle  /  Aspen Plus  /  simulation calculation  /  carbon emission performance  /  influencing factors
孙天祥, 刘松涛, 曹悦, 陈传敏, 贾文波, 李朋, 周卫青. 燃气-蒸汽联合循环的碳排放绩效计算及影响分析. 热力发电, 2024 , 53 (10) : 114 -121 . DOI: 10.19666/j.rlfd.202403045
Tianxiang SUN, Songtao LIU, Yue CAO, Chuanmin CHEN, Wenbo JIA, Peng LI, Weiqing ZHOU. Carbon emission performance calculation and impact analysis of gas-steam combined cycle[J]. Thermal Power Generation, 2024 , 53 (10) : 114 -121 . DOI: 10.19666/j.rlfd.202403045
2020年9月22日,习近平总书记在第75届联合国大会一般性辩论中指出,CO2排放力争于2030年前达到峰值,努力争取2060年前实现碳中和[1-3]。实现碳达峰、碳中和目标,电力行业是主战场[4-7]。国家电网公司发布的《国家电网公司“碳达峰、碳中和”行动方案》[8]中明确提到,围绕“电能替代”等加强碳减排方法研究,为产业链上下游提供碳减排服务。
碳排放核算是准确掌握我国碳排放变化趋势、有效开展各项碳减排工作、促进经济绿色转型的基本前提[9-10]。电能替代是使用电能替代传统的能源,可以降低整个电力系统的碳排放水平。因此,在进行电能替代时,需要考虑不同能源的碳排放量,并对其进行合理的核算,以评估电能替代对减少温室气体排放的实际影响。准确的碳排放核算能有效支撑“电能替代”碳减排效果评估。一些学者[11-17]已经对具体燃煤机组碳排放进行相关研究。燃气电厂作为火力发电的主要方式之一,其碳排放核算准确性非常重要。目前,碳排放核算主要采用排放因子法,根据活动数据等参数计算得出排放量。但是,其存在时间尺度大,无法结合企业实际运行情况进行测算等问题。唐小亮等[18]分别采用排放因子法和实测法计算机组的碳排放量,结果表明采用排放因子法计算的碳排放量大于试算配额。宋涛涛等[19]采用排放因子法和实测法核算燃气电厂碳排放量,因燃料实测发热量与省缺值相差,且含碳量和碳氧化率等参数受实际工作影响变化,致使2种核算方法结果产生偏差。监勇[20]对燃气电厂温室气体排放的多种计算方法进行对比,结果得出在线监测数据最为准确,移动监测次之,排放因子法最不准确。排放因子法时效性滞后,且不能结合电厂实际情况进行计算,实测工作开展较少,如今需要能够结合电厂实际运行情况且准确性高的碳核算方法。
Aspen Plus是一款标准化大型化工流程模拟软件,现已逐渐扩展到电力行业。有学者[21-24]基于Aspen Plus对联合循环机组进行模拟,且试验证实结果符合锅炉运行基本规律、过程机理。总体来看,利用Aspen Plus对燃气-蒸汽联合循环机组进行模拟是可行可靠的方法。本文通过电厂实际运行数据,采用Aspen Plus模拟燃气电厂运行,对其碳排放绩效进行计算,并对碳排放绩效的影响因素进行分析。本文碳排放核算方法与传统核算方法不同,将发电量与碳排放关联,结合电厂实际运行情况,减少核算数据负担,更好的帮助燃气电厂进行精确的碳排放核算以及碳减排的相关工作。
本文以某电厂S106FA多轴燃气-蒸汽联合循环发电机组为研究对象,包括一套燃气轮机、一套双压余热锅炉、一套汽轮机,余热锅炉为双压、再热、卧式、无补燃、自然循环燃机余热锅炉。模拟主要有4大模块:压气机B1、燃气透平B4和汽轮机B14选择Compr模型模拟,通过排放压力和所需功率进行定义;燃烧室B2选用RStoic模型,规定相关化学反应以及转化率进行定义;余热锅炉中的高压HSH1、2/低压LSH1过热器、高压HEV1/低压B13蒸发器、高压HEC1、2/低压LEC1省煤器均用MHeatX模块进行模拟,MHeatX可用来模拟多股物流的换热过程;高压HP/低压B9汽包采用Flash模块模拟,进行汽水分离。在主要单元操作模块确定后,用流股按照实际运行工艺进行连接,具体如图1所示。
Aspen Plus提供的物性模型分为理想模型、状态方程模型、活度系数模型和特殊模型。Aspen Plus软件提供了多种物性方法,本文选择PENG-POB方法[25-26]
在模拟过程中,需要输入进料流股和单元操作模块的相关参数,具体参数来自现场运行数据,具体见表1
污染物的排放绩效就是生产单位产品所排放污染物的量[27]。电力行业碳排放绩效是以电力产出为依据制定的电力行业污染物排放标准,即每生产1 kW·h电的污染物排放量。污染物排放绩效是一项能综合反映电力企业生产过程中能源利用效率和污染物排放情况的重要指标。
联合循环机组的碳排放绩效根据Aspen Plus模拟结果结合数学模型进行计算。
1)天然气联合循环机组总功率
Plh=Prj+Pqj
式中:Plh为联合循环功率,kW;Pqj为汽轮机功率,kW;Prj为燃气透平功率,kW。
2)联合循环机组天然气消耗率
glh==GfPlh
式中:glh为天然气消耗率,kg/(kW·h);Gf为天然气流量,kg/h。
3)碳排放绩效
E=i=1nθi×glh×M(CO2)×OFiMi
式中:E为碳排放绩效,g/(kW·h);n为天然气含烷烃种类数量;θi为烷烃i在天然气占比,%;M(CO2)为CO2分子质量,取44 g/mol;OFi为碳氧化率,%;Mi为烷烃i分子质量,g/mol。
本文模型采用燃气-蒸汽联合循环机组纯凝工况下进行模拟,其中,环境温度为17 ℃,大气压力为100.51 kPa,相对湿度为80%。模拟结果与实际系统的相关参数进行对比,其中,实际系统来自于电厂数据,对比结果见表2
表2可以看出,模拟系统的参数计算值与实际运行的设计值的误差在规定范围之内。由此可见,该模型具有较高的准确性,模拟结果准确。
本文根据模拟结果结合数学模型对碳排放绩效以及碳排放量进行计算,具体结果见表3,天然气成分见表4
本文将基于不同核算方法的碳排放量进行比较。对于实测法来说,按照固定污染源烟气排放连续监测系统技术及检测方法的要求,对排放源进行连续监测,主要设备见表5
CO2测量仪安装于垂直烟囱出口40 m平台处。CO2测量仪监测到的数据通过数据采集模块采集后传输至主站,由主站进行存储。燃气电厂机组碳排量计算见式(4)。
Esc=M(CO2)22.4×Q×C×273273+T×p101 325×105
式中:Esc为实测碳排放量,t;Q为烟气流量,m3/h;C为CO2体积分数,%;T、p分别为测量温度与压力,℃、Pa。
其中,CO2现场取样带回室内,采用《气体中CO、CO2和碳氢化合物的测定气相色谱法》[28]进行分析检测,经过计算,CO2体积分数为3.821%。基于实测法计算的机组碳排放量为506.47 t。
对于排放因子法来说,碳排放量根据《企业温室气体排放核算方法与报告指南 发电设施》[29]进行计算,具体结果见表6
由上可知,3种方法中,实测法核算结果最低,本文核算结果与实测法偏差在0.20%,排放因子法采用实测法低位发热量和省缺值低位发热量与实测法偏差分别为5.24%和19.66%。实测法与本文核算结果产生偏差的主要原因是在运行过程中,机组负荷发生变化,需要分段核算并累积,并未及时累计核算。实测法与排放因子法产生偏差的主要原因是实测发热量与缺省值发热量相差4.69 MJ/m³,天然气的含碳量、碳氧化率采用的缺省值,但其受天然气品质和实际燃烧影响,处于变化状态。
碳排放绩效对于企业开展碳排放核算及后续的碳交易工作至关重要,也反映了企业碳排放水平。因此,本文对机组负荷、热网投入和天然气成分3个影响因素进行不同工况模拟,对环境温度采用Aspen Plus中灵敏度分析方法,探究并分析其对碳排放绩效的影响。
本文选择了THA(满负荷)、0.8THA、0.7THA、0.5THA和0.3THA 5种不同负荷下的工况进行分析。不同负荷下,蒸汽的品质也有所不同。不同负荷下蒸汽参数见表7,不同负荷对碳排放绩效影响如图2所示。表7显示,随着负荷的降低,高压蒸汽温度略微波动,压力则呈下降趋势,低压蒸汽的温度和压力也在下降。蒸汽品质的降低导致了联合循环机组的功率下降。由图2可见,汽轮机功率下降最为明显,下降了40.03%。这一变化导致了联合功率的下降,由125.40 MW降至103.63 MW,因此燃料消耗和碳排放绩效也必然会增加。具体来看,天然气消耗率从130.45g/(kW·h)升至157.86 g/(kW·h),而碳排放绩效也从342.66 g/(kW·h)增加至414.66 g/ (kW·h),增加了21%。值得注意的是,汽轮机蒸汽参数虽受到机组负荷的影响,但其可调节程度相对较小。因此,对于机组负荷的变化,合理安排调峰时间至关重要。尤其在机组启动阶段,由于初始负荷较低,导致了天然气消耗率和相应的碳排放绩效较高。频繁的调峰可能会导致电厂的天然气消耗率和碳排放绩效上升,从而不利于碳减排工作的开展。
本文模拟燃气-蒸汽循环联合电厂在不同抽汽量情况下的运行情况。在热电联产系统中,供热过程通常不直接产生电力,故供热碳排放通常不计入电厂的直接碳排放。因此,计算供电碳排放绩效时,应考虑热电联产总碳排放绩效与(1–供热比)的乘积。用于供热的抽汽会间接影响电厂的碳排放,部分蒸汽被送入热网,汽轮机做功能力逐渐下降。热网投入对碳排放绩效影响如图3所示。图3显示,抽汽量越大,汽轮机的功率就越小,导致联合功率降低。联合功率由119.9 MW降低至111.84 MW,降低了7.21%。联合功率的下降会导致天然气消耗率增加,从而使热电联产总碳排放绩效呈现上升趋势,由358.39 g/(kW·h)增至384.22 g/(kW·h)。但是,抽汽量的增加通常会导致供热比上升。这是因为更多的抽汽意味着更多的蒸汽被用于供热,从而提高了供热比例。据《火力发电厂技术经济指标计算方法》对供热比进行计算,最小抽汽、额定抽汽和最大抽汽3种工况的供热比分别为41.87%、46.16%和48.84%。供热比的提升,使得供电分摊的碳排放绩效变小。因此可以看出,随着抽汽量的增加,供电碳排放绩效呈下降趋势,由208.34 g/(kW·h)降至196.57 g/(kW·h)。因此,电厂可以通过增加抽汽量,提高热电联产系统的供热比例,使系统的热效率提升。建议优化热电联产系统的设计和运行,确保最大限度地利用热能进行供热,降低能源浪费,减少碳排放。
本文选取环境温度为–10、-5、0、5、10、15、20 ℃,空气质量流量随环境温度变化而改变,采用灵敏度分析工具,插入FORTRAN,探究环境温度对碳排放绩效的影响。当温度的变化会影响空气的比热容,温度升高,空气密度下降,相同体积流量的空气,随之对应的质量流量会下降。环境温度对碳排放绩效影响如图4所示。图4显示,随着环境温度的逐渐升高,空气的质量流量由最初的806.31 t/h下降至723.99 t/h,环境温度每上升1 ℃,空气质量流量下降约2.74 t/h。空气质量流量的下降会使燃气透平的功率降低。因为在运行过程中,燃气透平所做的功约有2/3是压气机消耗的,空气质量流量的下降使得压气机的功耗减小,燃气透平的功率随之下降。燃气透平功率由87.61 MW下降至84.04 MW,下降了4.11%,联合功率由125.61 MW下降至122.01 MW。每kW·h消耗的天然气也随之增加,最后影响机组碳排放绩效,由335.71 g/(kW·h)增至345.62 g/(kW·h)。
本文以CH4的含量为变量,设计了4种工况:1)天然气1,成分为85%(体积分数,下同)CH4、9%C2H6、6%N2,低位热值为44 986.16 kJ/kg;2)天然气2,成分为75%CH4、9%C2H6、16%N2,低位热值为38 008.52 kJ/kg;3)天然气3,成分为65%CH4、9%C2H6、26%N2,低位热值为31 849.07 kJ/kg;4)天然气4,成分为55%CH4、 9%C2H6、36%N2,低位热值为26 371.88 kJ/kg。天然气有效成分(CH4)比例依次递减10%,其低位热值依次递减。天然气成分对碳排放绩效影响如图5所示。由图5看出,为了保证总热量,天然气有效成分的降低会导致天然气质量流量的增加,从16.10 t/h上升至27.64 t/h。质量流量的增加会提高燃气透平的功率,从87.43 MW升至103.79 MW,提高了18.71%,进而影响联合功率。但是,天然气的有效成分比例的下降,其低位热值也下降,下降41.38%。低位热值的下降趋势更明显,使得天然气消耗率呈上升趋势,由130.45 g/(kW·h)上升至196.85 g/(kW·h)。碳排放绩效也呈上升趋势,由318.93 g/(kW·h)上升至324.49 g/(kW·h),上升1.74%。由此看来,对于天然气品质的选择,有效成分比例高的更为适宜。热值越高,单位体积天然气燃烧后产生热能越高,所能转化的电能越大。以此作为燃料,达到降低天然气消耗率的目的,更有利于降低机组碳排放绩效。但是,基于电厂经济性考虑,此燃料可能在碳排放较低的同时价格较高。电厂可以考虑使用可再生能源作为替代或补充燃料。选择这些可再生能源作为燃料不仅可以降低碳排放,还可以提高电厂的可持续性和环境友好性。
1)为了有效评估、准确计算燃气电厂碳排放,帮助开展各项碳减排工作。本文提出一种基于Aspen Plus结合数学模型针对燃气电厂碳排放计算模型,燃气-蒸汽联合循环机组进行建模和稳态模拟,模拟结果与实际相符。
2)经过计算,机组纯凝状态下碳排放绩效为342.66 g/(kW·h)。本文采用碳排放绩效对比实测法与排放因子法对碳排放进行计算,碳排放绩效法与实测法核算偏差在0.20%;排放因子法采用实测法低位发热量和省缺值低位发热量与实测法偏差分别为5.24%和19.66%。碳排放绩效核算碳排放量提高了燃气电厂碳排放核算的准确性与时间分辨率。
3)通过影响因素分析,可以看出机组负荷对碳排放绩效影响最大,其次为热网投入、环境温度和天然气成分。为降低碳排放绩效,电厂需要合理安排调峰时间。优化热电联产系统的设计和运行,确保最大限度利用热能进行供热,降低能源浪费。电厂可以考虑使用可再生能源为替代或补充燃料。降低碳排放,提高电厂的可持续性和环境友好性。
  • 国家电网有限公司科技项目(1400-202214404A-2-0-ZN)
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2024年第53卷第10期
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doi: 10.19666/j.rlfd.202403045
  • 接收时间:2024-03-09
  • 首发时间:2026-03-05
  • 出版时间:2024-10-25
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  • 收稿日期:2024-03-09
基金
Science and Technology Project of Funding from State Grid Corporation of China(1400-202214404A-2-0-ZN)
国家电网有限公司科技项目(1400-202214404A-2-0-ZN)
作者信息
    1.华北电力大学(保定)环境科学与工程系,河北 保定 071003
    2.国网冀北电力有限公司电力科学研究院,北京 100045

通讯作者:

刘松涛(1980),男,博士,副教授,主要研究方向为煤的清洁燃烧及污染物排放控制技术,
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Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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