Article(id=1149744613193330763, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1149744590489568247, articleNumber=null, orderNo=null, doi=10.3969/j.issn.1672-6073.2024.02.005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1697731200000, receivedDateStr=2023-10-20, revisedDate=1703088000000, revisedDateStr=2023-12-21, acceptedDate=null, acceptedDateStr=null, onlineDate=1752050077006, onlineDateStr=2025-07-09, pubDate=null, pubDateStr=null, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752050077006, onlineIssueDateStr=2025-07-09, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752050077006, creator=13701087609, updateTime=1752050077006, updator=13701087609, issue=Issue{id=1149744590489568247, tenantId=1146029695717560320, journalId=1146123302524792850, year='2024', volume='37', issue='2', pageStart='1', pageEnd='145', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1752050071594, creator=13701087609, updateTime=1753780157843, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1157001098214068362, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1149744590489568247, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1157001098214068363, tenantId=1146029695717560320, journalId=1146123302524792850, issueId=1149744590489568247, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=28, endPage=38, ext={EN=ArticleExt(id=1149744613566623835, articleId=1149744613193330763, tenantId=1146029695717560320, journalId=1146123302524792850, language=EN, title=Carbon Emission Predictions and Carbon Reduction Targets of Xi'an Urban Rail Transit Operations, columnId=1154040217725031287, journalTitle=Urban Rapid Rail Transit, columnName=Green·Smart Album, runingTitle=null, highlight=null, articleAbstract=

To address uncertainties in future operational carbon emissions and carbon reduction targets for urban rail transit networks, this study analyzes operational energy consumption data for the Xi'an urban rail transit system from 2018 to 2022. The carbon emission factor method is employed to calculate operational carbon emissions for the past five years. The index method is then used to predict energy consumption and carbon emissions for the next five years. Based on the binding energy intensity requirements outlined in China's Action Plan for Green Urban Rail Development, we quantitatively analyze the total energy saving target for Xi'an's urban rail transit operations over the next five years. Subsequently, the total carbon emission constraint and carbon reduction target are determined. The research results indicate that approximately 175.9×10⁴ tCO2e of total carbon emissions were generated during the operation of the Xi'an urban rail transit system from 2018 to 2022. Under these conditions, it is expected that approximately 321.6×10⁴ tCO2e of total carbon emissions will be generated over the next five years of operations, with traction and station power consumption being the main sources of carbon emissions. If the comprehensive energy intensity reduction target proposed by the association is achieved, compared with the predicted carbon emissions, the carbon emissions of the entire network are expected to reduce by approximately 13% by 2025; the carbon emissions of the entire network are expected to reduce by approximately 15% by 2027; and in total, carbon emissions are expected to reduce by approximately 10% over the next five years.

, correspAuthors=Yiran WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Xiaoqiang YANG, Shufeng LIU, Haoyu QU, Li DENG, Huijun LI, Rui YANG, Yiran WANG), CN=ArticleExt(id=1149744652611400330, articleId=1149744613193330763, tenantId=1146029695717560320, journalId=1146123302524792850, language=CN, title=西安城市轨道交通运营碳排放预测与减碳目标研究, columnId=1154040217863443320, journalTitle=都市快轨交通, columnName=绿色·智慧专论, runingTitle=null, highlight=null, articleAbstract=

为了解决城市轨道交通线网未来运营碳排放量与减碳目标不明确的问题,基于20182022年西安城轨交通运营能耗的统计分析,采用碳排放因子法对过去5年运营碳排放量进行核算;采用指标法对未来5年西安城轨交通运营能耗进行预测,并得到碳排放量的预测值;依据《中国城市轨道交通绿色城轨发展行动方案》提出的综合能耗强度约束性要求,对未来5年西安城轨交通运营节能总量目标进行量化分析,进而确定碳排放总量需达到的约束值与减碳量目标值。研究表明:2018—2022年,西安城轨交通运营产生的碳排放约175.9×10⁴tCO2e,在此情景下估算未来5年城轨交通运营预计将产生的碳排放约321.6×104 tCO₂e,其中以牵引与车站电耗产生的间接碳排放为主要来源。若达到城轨行业协会提出的综合能耗强度下降目标,经估算与预测碳排放量相比,2025年全线网运营碳排放量预计可减少约13%,2027年全线网运营碳排放量预计可减少约15%;未来5年预计共减少碳排放量约10%。

, correspAuthors=王奕然, authorNote=null, correspAuthorsNote=
王奕然,男,本科,教授级高级工程师,主要从事轨道交通设计研究工作,
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杨晓强,男,本科,教授级高级工程师,主要从事轨道交通建设研究工作,

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杨晓强,男,本科,教授级高级工程师,主要从事轨道交通建设研究工作,

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(2011-05) [2023-09-20]. http://www.gxdtrc.cn/h-nd-217.html., articleTitle=省级温室气体清单编制指南(发改办气候〔2011〕1041号), refAbstract=null), Reference(id=1154040357428908671, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149744613193330763, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=https://www.ndrc.gov.cn/xxgk/zcfb/tz/201511/t20151111_963496.html?code=&state=123, language=null, rfNumber=[18], rfOrder=27, authorNames=中华人民共和国国家发展和改革委员会, journalName=null, refType=null, unstructuredReference=中华人民共和国国家发展和改革委员会. 陆上交通运输企业温室气体排放核算方法与报告指南(发改办气候〔2015〕1722号)[Z]. (2015-07-06)[2023-09-20]. https://www.ndrc.gov.cn/xxgk/zcfb/tz/201511/t20151111_963496.html?code=&state=123., articleTitle=陆上交通运输企业温室气体排放核算方法与报告指南(发改办气候〔2015〕1722号), refAbstract=null), Reference(id=1154040357479240320, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149744613193330763, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=null, pageStart=30, pageEnd=31, url=null, language=null, rfNumber=[19], rfOrder=28, authorNames=住房和城乡建设部科技与产业化发展中心, journalName=null, refType=null, unstructuredReference=住房和城乡建设部科技与产业化发展中心. 建筑领域碳达峰碳中和实施路径研究[M]. 北京: 中国建筑工业出版社, 2021: 30-31., articleTitle=建筑领域碳达峰碳中和实施路径研究, refAbstract=null)], funds=[Fund(id=1154040355847656034, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149744613193330763, awardId=D8-YJ-082022007, language=CN, fundingSource=西安市轨道交通集团有限公司研究课题(D8-YJ-082022007), fundOrder=null, country=null), Fund(id=1154040355902181987, tenantId=1146029695717560320, 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年份 2019—2020 2021 2022
电力碳排放因子/(tCO2/MWh) 0.610 1 0.581 0 0.570 3
天然气燃烧碳排放因子/( ${\mathrm{{tCO}}}_{2}/{\mathrm{m}}^{3}$ ) ${1.978} \times {10}^{-3}$
热力碳排放因子/( ${\mathrm{{tCO}}}_{2}$ / $\mathrm{{GJ}}$ ) 0.11
), ArticleFig(id=1154040355692466785, tenantId=1146029695717560320, journalId=1146123302524792850, articleId=1149744613193330763, language=CN, label=表 1, caption=2018-2022 年碳排放因子, figureFileSmall=null, figureFileBig=null, tableContent=
年份 2019—2020 2021 2022
电力碳排放因子/(tCO2/MWh) 0.610 1 0.581 0 0.570 3
天然气燃烧碳排放因子/( ${\mathrm{{tCO}}}_{2}/{\mathrm{m}}^{3}$ ) ${1.978} \times {10}^{-3}$
热力碳排放因子/( ${\mathrm{{tCO}}}_{2}$ / $\mathrm{{GJ}}$ ) 0.11
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西安城市轨道交通运营碳排放预测与减碳目标研究
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杨晓强 1 , 刘树峰 2 , 屈昊聿 2 , 邓莉 1 , 栗慧珺 1 , 杨锐 2 , 王奕然 2
都市快轨交通 | 绿色·智慧专论 2024,37(2): 28-38
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都市快轨交通 | 绿色·智慧专论 2024, 37(2): 28-38
西安城市轨道交通运营碳排放预测与减碳目标研究
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杨晓强1 , 刘树峰2, 屈昊聿2, 邓莉1, 栗慧珺1, 杨锐2, 王奕然2
作者信息
  • 1 西安城市轨道交通集团有限公司 西安 710016
  • 2 北京城建设计发展集团股份有限公司 北京 100037
  • 杨晓强,男,本科,教授级高级工程师,主要从事轨道交通建设研究工作,

通讯作者:

王奕然,男,本科,教授级高级工程师,主要从事轨道交通设计研究工作,
Carbon Emission Predictions and Carbon Reduction Targets of Xi'an Urban Rail Transit Operations
Xiaoqiang YANG1 , Shufeng LIU2, Haoyu QU2, Li DENG1, Huijun LI1, Rui YANG2, Yiran WANG2
Affiliations
  • 1 Xi'an Rail Transit Group Company Limited Xi'an 710016
  • 2 Beijing Urban Construction Design & Development Group Co., Limited Beijing 100037
doi: 10.3969/j.issn.1672-6073.2024.02.005
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为了解决城市轨道交通线网未来运营碳排放量与减碳目标不明确的问题,基于20182022年西安城轨交通运营能耗的统计分析,采用碳排放因子法对过去5年运营碳排放量进行核算;采用指标法对未来5年西安城轨交通运营能耗进行预测,并得到碳排放量的预测值;依据《中国城市轨道交通绿色城轨发展行动方案》提出的综合能耗强度约束性要求,对未来5年西安城轨交通运营节能总量目标进行量化分析,进而确定碳排放总量需达到的约束值与减碳量目标值。研究表明:2018—2022年,西安城轨交通运营产生的碳排放约175.9×10⁴tCO2e,在此情景下估算未来5年城轨交通运营预计将产生的碳排放约321.6×104 tCO₂e,其中以牵引与车站电耗产生的间接碳排放为主要来源。若达到城轨行业协会提出的综合能耗强度下降目标,经估算与预测碳排放量相比,2025年全线网运营碳排放量预计可减少约13%,2027年全线网运营碳排放量预计可减少约15%;未来5年预计共减少碳排放量约10%。

城市轨道交通  /  运营  /  碳排放  /  碳排放因子  /  减碳目标

To address uncertainties in future operational carbon emissions and carbon reduction targets for urban rail transit networks, this study analyzes operational energy consumption data for the Xi'an urban rail transit system from 2018 to 2022. The carbon emission factor method is employed to calculate operational carbon emissions for the past five years. The index method is then used to predict energy consumption and carbon emissions for the next five years. Based on the binding energy intensity requirements outlined in China's Action Plan for Green Urban Rail Development, we quantitatively analyze the total energy saving target for Xi'an's urban rail transit operations over the next five years. Subsequently, the total carbon emission constraint and carbon reduction target are determined. The research results indicate that approximately 175.9×10⁴ tCO2e of total carbon emissions were generated during the operation of the Xi'an urban rail transit system from 2018 to 2022. Under these conditions, it is expected that approximately 321.6×10⁴ tCO2e of total carbon emissions will be generated over the next five years of operations, with traction and station power consumption being the main sources of carbon emissions. If the comprehensive energy intensity reduction target proposed by the association is achieved, compared with the predicted carbon emissions, the carbon emissions of the entire network are expected to reduce by approximately 13% by 2025; the carbon emissions of the entire network are expected to reduce by approximately 15% by 2027; and in total, carbon emissions are expected to reduce by approximately 10% over the next five years.

urban rail transit  /  operational  /  carbon emissions  /  caron emission factor  /  carbon reduction target
杨晓强, 刘树峰, 屈昊聿, 邓莉, 栗慧珺, 杨锐, 王奕然. 西安城市轨道交通运营碳排放预测与减碳目标研究. 都市快轨交通, 2024 , 37 (2) : 28 -38 . DOI: 10.3969/j.issn.1672-6073.2024.02.005
Xiaoqiang YANG, Shufeng LIU, Haoyu QU, Li DENG, Huijun LI, Rui YANG, Yiran WANG. Carbon Emission Predictions and Carbon Reduction Targets of Xi'an Urban Rail Transit Operations[J]. Urban Rapid Rail Transit, 2024 , 37 (2) : 28 -38 . DOI: 10.3969/j.issn.1672-6073.2024.02.005
城市轨道交通作为各大城市的重要交通方式, 其大运量、高频次的特征使其成为能源消耗与碳排放的大户 [ 1 ] 。中国城市轨道交通协会在 2022 年 8 月制定了 《中国城市轨道交通绿色城轨发展行动方案》(以下简称《行动方案》),提出实现城轨 “双碳” 目标,建成绿色城轨的 “三步走” 战略[ 2 ]。为实现行业协会提出的目标, 结合具体城市轨道交通的运营现状, 在对未来运营能耗预测的基础上, 确定明确的节能量与减碳量目标值对于节能减排管理工作具有重要意义。
关于城轨交通碳排放的研究, 秦骜、钱生泽以单一的车站作为研究对象, 分析了其全生命周期的碳排放规律 [ 1 , 3 ] ; DONG 等以某一城市的城轨交通全网为对象, 对其运营阶段的能耗与碳排放进行了统计计算与分析 [ 4 - 5 ] ; 马占奎等对某一城市或某一条具体线路的未来能耗进行了预测 [ 6 - 10 ] ; 周琪等对不同类型城市的碳排放效率进行了研究 [ 11 ] 。而涉及某一城市的城轨交通全线网未来碳排放预测的研究以及结合《行动方案》 确定减碳目标值的研究尚属空白。
西安城轨交通经过 10 余年的发展建设, 截至 2023 年 6 月底,已有运营线路 9 条,运营里程 ${312}\mathrm{\;{km}}$ ; 在建线路 3 条,在建里程 ${104}\mathrm{\;{km}}$ ,将在 2027 年前全部投入使用。本文基于西安城轨交通运营阶段的实测数据, 对未来 5 年全网的碳排放量进行预测, 并结合 (行动方案》要求对未来的减碳目标值进行研究, 为西安城轨交通运营节能减碳管理工作提供参考。
首先对西安城轨交通 2018-2022 年的运营阶段能耗数据进行统计, 并采用碳排放因子法得出 2018- 2022 年的运营碳排放量。能耗包括牵引电耗、车站电耗、场段及控制中心的电耗、天然气消耗及热力消耗。相关的碳排放量可通过下式得出 [ 1 ]
$ E =\mathrm{{AD}}\cdot \mathrm{{EF}}$
式中, $E$ 为温室气体排放量; $\mathrm{{AD}}$ 为活动数据(某排放源的具体使用和投入数量); EF 为排放因子(单位某排放源使用量所产生的温室气体排放量)。
然后在已有统计数据的基础上, 结合未来 5 年的建设规划, 使用指标法对 2023-2027 年的运营能耗进行预测,并得出相应的碳排放量。采用的指标主要包括车公里牵引能耗与单位面积能耗, 其计算式分别为 [ 12 ]
${E}_{v}\left( i\right)= {x}_{i}{n}_{i}\cdot {L}_{i}\cdot {\eta }_{i}$
式中, ${E}_{v}\left( i\right)$ 为第 $i$ 条线路的牵引能耗, $\mathrm{{kW}}\cdot \mathrm{h};{n}_{i}$ 为年发车列数; ${x}_{i}$ 为列车编组数; ${L}_{i}$ 为线路里程, $\mathrm{{km}};{\eta }_{i}$ 为线路的车公里牵引能耗, $\mathrm{{kW}}\cdot \mathrm{h}/$$\cdot \mathrm{{km}}$
${E}_{s}\left( i\right)= {E}_{i}\cdot S $
式中, ${E}_{s}\left( i\right)$ 为第 $i$ 条线路的车站 (场段及控制中心) 能耗, $\mathrm{{kW}}\cdot \mathrm{h};{E}_{i}$ 为车站 (场段及控制中心) 单位面积能耗, $\mathrm{{kW}}\cdot \mathrm{h}/{\mathrm{m}}^{2};S$ 为车站 (场段及控制中心) 面积, ${\mathrm{m}}^{2}$
最后以《行动方案》 [ 2 ] 提出的 “三步走” 战略为目标, 在现有能耗统计的基础上, 对减排目标在各年份的牵引能耗与车站能耗中进行分解, 并结合预测能耗, 对 2023-2027 年节能要求进行量化, 并最终得出该目标下碳排放量的减少值。研究路线如 图 1 所示。
2018-2022 年, 西安城轨交通的线网规模不断扩大,线路数量由 3 条增长至8条,线路里程由 ${91}\mathrm{\;{km}}$ 增加至 ${277}\mathrm{\;{km}}$ 。全线网的年客运周转量与年运营里程如 图 2 所示。
依据运营过程中监测所得的数据, 对 2018- 2022 年的既有线各项能耗进行统计, 作为碳排放计算与后续能耗预测的基础。能耗统计范围包括牵引电耗、 车站电耗、场段与控制中心的电耗及供暖能耗。
图 3 为既有线路电耗统计结果, 随着新线路的不断开通, 2018-2022 年, 西安既有线路的年牵引电耗由 ${1.73}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ 增长到 ${3.79}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ ,年车站电耗由 ${1.26}\times {10}^{8}\mathrm{\;{kW}}\cdot \mathrm{h}$ 增长到 ${3.22}\times {10}^{8}\mathrm{\;{kW}}\cdot \mathrm{h}$ ,场段及控制中心年电耗由 ${0.16}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ 增长到 ${0.46}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ , 总电耗由 ${3.14}\times {10}^{8}\mathrm{\;{kW}}\cdot \mathrm{h}$ 增长至 ${7.47}\times {10}^{8}\mathrm{\;{kW}}\cdot \mathrm{h}$
根据场段及控制中心供暖方式的不同, 将供暖能耗分为天然气与热力两部分, 图 4 为既有线路场段及控制中心供暖能耗的年消耗量统计结果。随着投入使用的场段数量增多, 2018-2022 年天然气消耗由 259.52 × ${10}^{4}{\mathrm{\;m}}^{3}$ 增长至 ${477.04}\times {10}^{4}{\mathrm{\;m}}^{3}$ ,热力消耗由 ${69.86}\times {10}^{3}\mathrm{{GJ}}$ 增长至 ${197.63}\times {10}^{3}\mathrm{{GJ}}$
首先确定各类能源的碳排放因子。根据陕西省生态环境厅的相关文件 [ 13 ] 要求,本研究采用国家发展改革委与生态环境部的相关核算指南或文件 [ 14 - 18 ] 中的碳排放因子, 见 表 1
然后根据式(1)计算各年度碳排放量, 计算结果见 图 5 。受新线开通影响,年碳排放量由 2018 年的 21.9 × ${10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$ 上升至 2021 年的 ${46.6}\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$ ;2022 年没有新线开通, 且电力碳排放因子下降, 年碳排放量略有下降,为 ${45.7}\times {10}^{4}\mathrm{t}{\mathrm{{CO}}}_{2}\mathrm{e}$ 。 5 年内共产生碳排放 ${175.9}\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$
图 6 为不同能耗部门与能源种类引起的碳排放占比图, 可以看出车站与牵引能耗所引起的碳排放占主要部分,分别为 38.56%和 47.90%。而在不同能源种类中电力碳排放总量占主要部分,达到了 91.68%。因此在运营阶段,车站与牵引电耗具有较大的减碳潜力。
根据既有线的运营能耗统计结果, 对 2018-2022 年西安各线各项运营能耗指标进行计算, 分析处理后作为未来运营能耗预测的能耗指标。指标包括: 车公里牵引电耗、单位面积年车站电耗、单位面积年场段 (控制中心)电耗、单位面积年场段(控制中心)热力消耗及单位面积年场段(控制中心)天然气消耗。
图 7 为各条线各年度的车公里牵引电耗计算结果。 结果表明, 各条线路各年的牵引能耗指标较稳定, 且各条线路之间的指标差异较小,在 ${1.45}\sim {1.85}\mathrm{\;{kW}}\cdot \mathrm{h}/$$\cdot \mathrm{{km}}$ 之间。
图 8 为单位面积车站电耗指标。由结果可见, 各线路车站电耗指标各年份较为稳定, 开通较早的线路 1 与线路 2 车站电耗指标较高。随着新技术、新设备的
应用, 后续开通线路的车站电耗指标有降低趋势。
场段及控制中心单位面积电耗指标如 图 9 所示, 各年份的电耗指标较为稳定。
对于不同供暖方式的场段及控制中心, 其单位面积热负荷在 ${25}\sim {50}\mathrm{\;W}/{\mathrm{m}}^{2}$ 之间,相关天然气及热力指标计算不再赘述。
在未来 5 年, 西安城轨交通网络规模仍处于不断扩大阶段。到 2027 年,将有 4 条新线路陆续投入运营, 新增线路里程 ${137}\mathrm{\;{km}}$ 。根据既有线现状及各新建线路工程设计资料, 未来 5 年西安全线网的年客运周转流量与年运营里程如 图 10 所示。
本节采用指标法, 分别对既有线与在建线 2023- 2027 年的各项能耗进行预测,方法如下。
1) 牵引电耗的预测。既有线牵引能耗指标采用各条线路往年指标的平均值, 运营里程由往年运营里程得出。在建线牵引能耗指标采用既有线的全网牵引能耗指标的平均值, 运营里程根据工程设计资料确定。
2) 车站电耗的预测。既有线指标为各站的车站电耗指标, 在建线指标采用与在建车站形式与面积相似车站的能耗指标, 面积为各车站面积。
3) 场段能耗的预测。既有场段为各场段电耗指标, 在建场段采用与其面积相近的既有场段的指标。 由于在建场段均采用市政供热, 因此在建场段只进行热力消耗预测, 热力指标为即有场段热力指标的平均值 ${40}\mathrm{\;W}/{\mathrm{m}}^{2}$ 。由于新建场段供暖无天然气消耗,未来 5 年天然气消耗为 2021、2022 年既有场段天然气消耗的平均值。
电耗及热力消耗预测结果如 图 11 所示。2023-2027 年,年牵引电耗由 ${4.65}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ 增长至 ${6.27}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ , 年车站电耗由 ${3.74}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ 增长至 ${5.33}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ ,年场段及控制中心电耗由 ${0.54}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ 增长至 ${0.73}\times$ ${10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ 。5 年内将消耗电能总计 ${55.37}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ ,消耗热力 ${161.32}\times {10}^{4}\mathrm{{GJ}}$ ,天然气 ${2.39}\times {10}^{7}{\mathrm{\;m}}^{3}$
首先确定各类能源的碳排放因子。随着火力发电技术与设备的进步, 发电机组的煤耗逐渐降低, 同时可再生能源发电量在总发电量中占比逐渐增大, 使得电力碳排放因子有逐年下降的趋势。根据住房和城乡建设部科技与产业化发展中心的相关研究 [ 19 ] ,本文采用的 2023-2027 年电力碳排放因子分别为 0.561 、 0.551、0.542、0.532、0.523 ${\mathrm{{tCO}}}_{2}/\mathrm{{MWh}}$ ,天然气燃烧与热力的碳排放因子则保持不变。
2023-2027 年西安全网运营碳排放预测结果如 图 12 所示。随着新线逐步投入运营, 年碳排放量预计由 2023 年的 ${53.4}\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$ 增长至 2025 年的 ${72.1}\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$ ; 2025-2027 年没有新线投入运营, 受碳排放因子下降的影响,年碳排放量逐步降低至 2027 年的 ${69.7}\times {10}^{4}\mathrm{t}{\mathrm{{CO}}}_{2}\mathrm{e}$ 。 5 年内将产生碳排放 321.6 $\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$
图 13 为 2023-2027 年为各部门及能耗种类所产生的碳排放占比, 结果与过去 5 年的碳排放分布规律基本一致。
《行动方案》结合国家 “双碳” 目标, 以 “绿色转型为主线, 清洁能源为方向, 节能降碳为重点, 智慧赋能, 创新驱动, 开展六大绿色城轨行动, 实现碳达峰碳中和, 建设绿色城轨” 为总体思路, 提出了 “三步走” 发展战略的主要目标: 到 2025 年, 预计城市轨道交通综合能耗强度较 2020 年下降 10%以上,牵引能耗强度较 2020 年下降 10%以上; 到 2030 年, 预计城市轨道交通综合能耗强度较 2020 年下降 15%以上, 牵引能耗强度较 2020 年下降 15%以上(由于疫情影响, 实际均以 2019 年为基数)[2]
本文基于 “三步走” 战略, 将 2025 年及 2030 年的目标分配至 2023-2027 年。由于既有线路在不进行改造的情况下节能潜力较低, 主要按照当年新开通线路里程占 5 年总新开通线路里程的比例进行分配。2023- 2027 年的能耗强度下降比例(以 2019 年为基数)分别为: 0.97%、3.02%、10%、11%、12%。各年份的综合能耗强度及牵引能耗强度如图 14所示。
基于 4.1 节确定的能耗强度目标以及前文预测的在目前运营状况下未来 5 年的能耗, 可得出未来各年份为达成节能目标所需达到的综合能耗节能量、牵引能耗节能量以及车站能耗节能量。
图 15 为节能目标量化计算结果。结果表明, 若达到协会目标,2023 年综合能耗需降低 ${1.18}\times {10}^{7}\mathrm{{kg}}$ cee, 其中牵引电耗需降低 ${0.09}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ (电力与综合能耗换算系数为 ${0.1229}\mathrm{{kg}}\mathrm{{cee}}/\mathrm{{kW}}\cdot \mathrm{h}$ ),车站电耗需降低 ${0.02}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ ;2025 年综合能耗需降低 ${2.15}\times {10}^{7}\mathrm{{kg}}$ cee, 其中牵引电耗需降低 ${0.69}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ ,车站电耗需降低 ${1.06}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ ;5 年内综合能耗共需降低 ${7.50}\times {10}^{7}\mathrm{{kg}}$ cee, 其中牵引电耗需降低 ${2.54}\times {10}^{8}\mathrm{{kW}}\cdot \mathrm{h}$ ,车站电耗需降低 ${3.56}\times {10}^{8}\mathrm{\;{kW}}\cdot \mathrm{h}$
在达成节能目标的情景下,对年碳排放量进行计算, 结果如 图 16 所示。与预测结果相比, 2023 年, 碳排放量下降了约 ${0.5}\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$ ,降低了约 $1\%;{2025}$ 年, 年碳排放量下降了约 ${9.5}\times {10}^{4}\mathrm{t}{\mathrm{{CO}}}_{2}\mathrm{e}$ ,降低了约 ${13}\%$ ; 2027 年,年碳排放量下降了约 ${10.6}\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$ ,降低了约 ${15}\%\circ 5$ 年总碳排放量共降低了 ${32.6}\times {10}^{4}\mathrm{t}{\mathrm{{CO}}}_{2}\mathrm{e}$ , 降低了约 10%。
以车公里碳排放量和人公里碳排放量作为碳排放指标, 与预测结果相比, 2025 年两种碳排放指标分别下降 ${2.56}\times {10}^{-4}{\mathrm{{tCO}}}_{2}$ /车 $\cdot \mathrm{{km}}$${0.06}{\mathrm{{tCO}}}_{2}$ /万人 $\cdot \mathrm{{km}}$ ; 2027 年两种碳排放指标分别下降 ${2.84}\times {10}^{-4}{\mathrm{{tCO}}}_{2}$ /车 $\cdot \mathrm{{km}}$${0.07}\mathrm{{tC}}{\mathrm{O}}_{2}$ /万人· $\mathrm{{km}}$
为达成上述运营减碳目标, 建议针对目前减碳面临的关键性问题, 从技术与管理两个方面采取一系列必要措施。
在降低牵引能耗引起的碳排放方面, 针对车辆再生能源利用控制的双向变流机组与整流机组的协同控制、对牵引供电系统的适应性以及双向变流机组柔性控制问题, 采用合理的站级控制策略; 针对车体自重大、能耗高的问题,采取车体、内装、车内设备、转向架、车下设备、线缆等车体轻量化措施;针对部分线路客流需求与运力不匹配造成运力浪费的问题, 采取 4/6 编组混跑、3+3 灵活编组节能运行方案。
在降低建筑能耗引起的碳排放方面, 针对通风空调系统能耗高、运营控制繁琐等问题, 采用通风空调一体化系统、多联氟泵机房空调技术方案;针对变压器空载损耗占比大的问题, 采用非晶合金变压器和立体卷铁芯变压器; 针对照明系统耗能占比高、公共区照明 BAS 控制方式固定单一、节能空间大的问题, 采用车站、区间及场段智能照明控制方案。
在可再生能源利用方面, 针对光伏发电应用不足的问题, 充分利用车辆基地和地上线路车站建筑表面、 区间声屏障表面的空间资源发展光伏发电; 针对光伏发电并网接入位置的选取和多源系统下的潮流分布难点问题, 根据不同光伏装机容量确定接入位置; 针对市政热力和燃气锅炉房等传统热源,由于化石燃料的使用受限、难以满足降碳要求的问题, 采用热泵技术、 中深层地热技术解决。
构建企业绿色低碳管理体系。绿色低碳的发展要求贯穿了城市轨道交通工程全生命期, 相关企业的各个层级、各个职能部门都有新的要求。为此, 按照科学合理的原则将绿色低碳与企业固有的管理体系进行融合, 构建基于原有企业组织架构的绿色低碳管理体系是非常必要的体制保障。
形成地方性绿色低碳标准体系, 并尽快完成亟须标准的编制。基于《行动方案》提出的绿色城轨标准体系框架, 根据分类原则和方法, 围绕绿色低碳主题, 以满足西安市轨道交通规划设计、建造施工、运营管理全过程的实际需求为驱动, 建立适用于西安市轨道交通发展的绿色城轨标准体系, 并首先发布实施可直接指导绿色低碳实践工作的规划设计、施工与运维指南。
强化运营能源管理手段。建立涵盖全线网能源智能管理平台,实现能源管理功能:主要用能设备、能源计量器具等信息汇总; 全线网的分类、分项能耗统计;能耗数据信息的查询、显示、分析诊断,对重点用能主体及重点能耗设备进行系统性管理。
通过研究, 本文得到如下主要结论:
1) 随着线网规模扩大, 西安城轨交通运营碳排放不断增长,在过去 5 年中共产生碳排放量约 ${175.9}\times$ ${10}^{4}\mathrm{{tC}}{\mathrm{O}}_{2}\mathrm{e}$
2) 基于实际运营数据, 结合线网规模、碳排因子变化等因素,对西安城轨交通未来 5 年的碳排放量预测的结果表明, 2023-2027 年的碳排放量呈现先升后降趋势,未来 5 年共产生碳排放量约 ${321.6}\times {10}^{4}{\mathrm{{tCO}}}_{2}\mathrm{e}$
3) 达到《行动方案》提出的能耗强度下降目标的情境下,与预测碳排放量相比,2025 年全线网碳排放量可减少约 13%;2027 年全线网碳排放量可减少约 15%;未来 5 年预计可总计减少约 10%。
4) 为达到减碳目标, 建议针对目前减碳面临的关键性问题, 在技术方面积极采取牵引节能、建筑节能、可再生能源利用等措施; 在管理方面尽快建立绿色低碳管理体系,形成绿色低碳标准体系、强化能源管理等措施。
  • 西安市轨道交通集团有限公司研究课题(D8-YJ-082022007)
  • 国家重点研发计划项目(2017YFB1201104)
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2024年第37卷第2期
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doi: 10.3969/j.issn.1672-6073.2024.02.005
  • 接收时间:2023-10-20
  • 首发时间:2025-07-09
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  • 收稿日期:2023-10-20
  • 修回日期:2023-12-21
基金
西安市轨道交通集团有限公司研究课题(D8-YJ-082022007)
国家重点研发计划项目(2017YFB1201104)
作者信息
    1 西安城市轨道交通集团有限公司 西安 710016
    2 北京城建设计发展集团股份有限公司 北京 100037

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王奕然,男,本科,教授级高级工程师,主要从事轨道交通设计研究工作,
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genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
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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