Article(id=1221497394043212457, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202208163, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1659542400000, receivedDateStr=2022-08-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769157273063, onlineDateStr=2026-01-23, pubDate=1682352000000, pubDateStr=2023-04-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769157273063, onlineIssueDateStr=2026-01-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769157273063, creator=13701087609, updateTime=1769157273063, updator=13701087609, issue=Issue{id=1221497393514730153, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='4', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769157272938, creator=13701087609, updateTime=1769157397933, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1221497917878223060, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1221497917878223061, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1221497393514730153, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=14, endPage=23, ext={EN=ArticleExt(id=1221497394290676396, articleId=1221497394043212457, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Economic analysis of cogeneration units based on carbon emission characteristics and carbon trading rules, columnId=1221497394227761835, journalTitle=Thermal Power Generation, columnName=Special topics on low carbon transformation of power system under the "double-carbon" goal, runingTitle=null, highlight=null, articleAbstract=

Under the policy of peak shaving and carbon trading, cogeneration units face a more complex background. In order to obtain the carbon emission characteristics, income distribution and carbon trading economy of the traditional extraction condensing cogeneration unit under all operating conditions, and provide a reference for the unit to deal with carbon market fluctuations when participating in carbon trading. Using EBSILON simulation software combined with python program, the carbon emission distribution and income composition of the unit under all operating conditions are obtained. The research results show that the carbon emission kilowatt hour is inversely proportional to the load. Taking 325 MW and 150 MW as examples, the carbon emission intensity of power supply increases from 903.54 g/(kW·h) to 1 015.28 g/(kW·h), and the total carbon emission is proportional to the load; The highest proportion of peak shaving income can reach 55%; The highest proportion of carbon trading income can reach 8%, and the peak shaving income accounts for a large proportion in the low load, while the carbon trading income accounts for a large proportion in the high load. Comparing the change of carbon price from 40 yuan/t to 90 yuan/t and the change of power supply carbon emission standard from 0.9 times to 1.3 times, it is found that the change of power supply carbon emission standard has a greater impact on the proportion of carbon trading income. The formulation of power supply carbon emission standard should be combined with the unit emission level. Too high or too low will affect the enthusiasm of cogeneration power plants to participate in carbon trading.

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在调峰和碳交易的政策下,热电联产机组面临更加复杂的背景。为了得到抽凝热电联产机组全工况碳排放特性、收益分布、以及碳交易经济性,为机组参与碳交易应对碳市场波动提供参考,利用EBSILON仿真软件结合Python程序得到机组全工况碳排放分布以及收益组成。结果表明:机组度电碳排放量与负荷呈反比;以电负荷325 MW和150 MW为例,供电碳排放强度从903.54 g/(kW·h)上升到1 015.28 g/(kW·h),碳排放总量与负荷呈正比;调峰收益占比最高可达55%,碳交易收益占比最高可达8%;调峰收益在低负荷占比大,而碳交易收益在高、中负荷占比较大;对比碳价从40元/t变化到90元/t和供电碳排放标准从原来的0.9倍变化到1.3倍,得到供电碳排放标准改变对碳交易收益有更大影响;供电碳排放标准的制定应当结合机组排放水平,过高或者过低都会影响热电联产电厂参与碳交易的积极性。

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刘嘉康(1997),男,硕士研究生,主要研究方向为热电联产机组经济性、负荷优化调度,
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郭喜燕(1975),女,副教授,主要研究方向为能源高效利用与节能,

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郭喜燕(1975),女,副教授,主要研究方向为能源高效利用与节能,

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郭喜燕(1975),女,副教授,主要研究方向为能源高效利用与节能,

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基于碳排放特性及碳交易规则的热电联产机组经济性分析
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郭喜燕 , 刘嘉康 , 白雪 , 杨志平 , 王宁玲
热力发电 | “双碳”目标下电力低碳转型研究专题 2023,52(4): 14-23
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热力发电 | “双碳”目标下电力低碳转型研究专题 2023, 52(4): 14-23
基于碳排放特性及碳交易规则的热电联产机组经济性分析
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郭喜燕 , 刘嘉康 , 白雪, 杨志平, 王宁玲
作者信息
  • 华北电力大学电站能量传递转化与系统重点实验室,北京 102206
  • 郭喜燕(1975),女,副教授,主要研究方向为能源高效利用与节能,

通讯作者:

刘嘉康(1997),男,硕士研究生,主要研究方向为热电联产机组经济性、负荷优化调度,
Economic analysis of cogeneration units based on carbon emission characteristics and carbon trading rules
Xiyan GUO , Jiakang LIU , Xue BAI, Zhiping YANG, Ningling WANG
Affiliations
  • North China Electric Power University Key Laboratory of power station energy transfer and transformation system, Beijing 102206, China
出版时间: 2023-04-25 doi: 10.19666/j.rlfd.202208163
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在调峰和碳交易的政策下,热电联产机组面临更加复杂的背景。为了得到抽凝热电联产机组全工况碳排放特性、收益分布、以及碳交易经济性,为机组参与碳交易应对碳市场波动提供参考,利用EBSILON仿真软件结合Python程序得到机组全工况碳排放分布以及收益组成。结果表明:机组度电碳排放量与负荷呈反比;以电负荷325 MW和150 MW为例,供电碳排放强度从903.54 g/(kW·h)上升到1 015.28 g/(kW·h),碳排放总量与负荷呈正比;调峰收益占比最高可达55%,碳交易收益占比最高可达8%;调峰收益在低负荷占比大,而碳交易收益在高、中负荷占比较大;对比碳价从40元/t变化到90元/t和供电碳排放标准从原来的0.9倍变化到1.3倍,得到供电碳排放标准改变对碳交易收益有更大影响;供电碳排放标准的制定应当结合机组排放水平,过高或者过低都会影响热电联产电厂参与碳交易的积极性。

碳交易  /  热电联产  /  全工况分析  /  碳市场  /  排放特性

Under the policy of peak shaving and carbon trading, cogeneration units face a more complex background. In order to obtain the carbon emission characteristics, income distribution and carbon trading economy of the traditional extraction condensing cogeneration unit under all operating conditions, and provide a reference for the unit to deal with carbon market fluctuations when participating in carbon trading. Using EBSILON simulation software combined with python program, the carbon emission distribution and income composition of the unit under all operating conditions are obtained. The research results show that the carbon emission kilowatt hour is inversely proportional to the load. Taking 325 MW and 150 MW as examples, the carbon emission intensity of power supply increases from 903.54 g/(kW·h) to 1 015.28 g/(kW·h), and the total carbon emission is proportional to the load; The highest proportion of peak shaving income can reach 55%; The highest proportion of carbon trading income can reach 8%, and the peak shaving income accounts for a large proportion in the low load, while the carbon trading income accounts for a large proportion in the high load. Comparing the change of carbon price from 40 yuan/t to 90 yuan/t and the change of power supply carbon emission standard from 0.9 times to 1.3 times, it is found that the change of power supply carbon emission standard has a greater impact on the proportion of carbon trading income. The formulation of power supply carbon emission standard should be combined with the unit emission level. Too high or too low will affect the enthusiasm of cogeneration power plants to participate in carbon trading.

carbon trading  /  cogeneration  /  full condition analysis  /  carbon market  /  emission characteristics
郭喜燕, 刘嘉康, 白雪, 杨志平, 王宁玲. 基于碳排放特性及碳交易规则的热电联产机组经济性分析. 热力发电, 2023 , 52 (4) : 14 -23 . DOI: 10.19666/j.rlfd.202208163
Xiyan GUO, Jiakang LIU, Xue BAI, Zhiping YANG, Ningling WANG. Economic analysis of cogeneration units based on carbon emission characteristics and carbon trading rules[J]. Thermal Power Generation, 2023 , 52 (4) : 14 -23 . DOI: 10.19666/j.rlfd.202208163
  • 国家自然科学基金创新研究群体科学基金(51821004)
2023年第52卷第4期
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doi: 10.19666/j.rlfd.202208163
  • 接收时间:2022-08-04
  • 首发时间:2026-01-23
  • 出版时间:2023-04-25
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  • 收稿日期:2022-08-04
基金
National Natural Science Foundation of China(51821004)
国家自然科学基金创新研究群体科学基金(51821004)
作者信息
    华北电力大学电站能量传递转化与系统重点实验室,北京 102206

通讯作者:

刘嘉康(1997),男,硕士研究生,主要研究方向为热电联产机组经济性、负荷优化调度,
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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