Article(id=1241049998842982586, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1720108800000, receivedDateStr=2024-07-05, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773818977318, onlineDateStr=2026-03-18, pubDate=1737302400000, pubDateStr=2025-01-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773818977318, onlineIssueDateStr=2026-03-18, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773818977318, creator=13701087609, updateTime=1773818977318, updator=13701087609, issue=Issue{id=1241049962679694215, tenantId=1146029695717560320, journalId=1234093305789726721, year='2025', volume='45', issue='1', pageStart='1', pageEnd='592', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773818968696, creator=13701087609, updateTime=1773819749443, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241053237428671382, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241053237428671383, tenantId=1146029695717560320, journalId=1234093305789726721, issueId=1241049962679694215, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=557, endPage=570, ext={EN=ArticleExt(id=1241049999371464921, articleId=1241049998842982586, tenantId=1146029695717560320, journalId=1234093305789726721, language=EN, title=Cross-regional energy system optimization and carbon neutrality scenario analysis in China, columnId=1234106390126588740, journalTitle=China Environmental Science, columnName=Carbon Emission Control, runingTitle=null, highlight=null, articleAbstract=

An inter-regional energy system optimization model, NEMO-China-MR, was constructed in this paper. Based on the economic development and energy demand differences across regions, as well as the regional resource endowments and heterogeneity in new energy development, three scenarios were designed: the reference scenario S0for steadily advancing the"carbon peaking and carbon neutrality" targets, the comprehensive scenario S1 for supply-demand coordination, and the balanced regional development scenario S2. Each scenario achieved both the "carbon peaking and carbon neutrality" targets and the transformation of energy demand while building a new power system. The scenario comparison results indicated that supply-demand coordination influenced the optimal development path of the power system. Energy storage facilities and inter-regional transmission overcame the spatial and temporal mismatches of energy resources. Scenario S1 required consideration of future grid uncertainties, while Scenario S2, which promoted healthy economic growth through balanced regional development, reduced grid transmission pressure and was identified as the most ideal development scenario for the future. Moving forward, it is essential to lead the energy system transition through high-quality economic development, construct a new power system through the coordination of electrification and low-carbon power, and build inter-regional transmission channels while promoting balanced regional development.

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通过构建跨区域能源系统优化模型NEMO-China-MR,基于各区域经济发展和能源需求差异性以及区域资源禀赋和新能源发展异质性,设计了稳妥推进“双碳”目标的参考情景S0,供需协同的综合情景S1和区域均衡发展情景S2,不同情景均能在达成“双碳”目标的同时实现能源需求转型与新型电力系统构建.情景对比的结果表明:供需协同影响电力系统最优发展路径,储能设施和跨区域输电克服了能源资源的时空错配,S1情景需要考虑未来电网的不确定性,S2情景在区域均衡发展促进经济健康发展的同时减少了电网传输的压力,是未来最理想的发展情景.未来需要以经济高质量发展引领能源系统转型,以电气化与电力低碳化协同构建新型电力系统,推进区域均衡发展的同时构建跨区域传输通道.

, correspAuthors=许光清, authorNote=null, correspAuthorsNote=
*责任作者,教授,
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赵庆(2000-),男,陕西商洛人,博士研究生,主要从事能源经济学、资源与环境经济学研究.发表论文3篇..

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赵庆(2000-),男,陕西商洛人,博士研究生,主要从事能源经济学、资源与环境经济学研究.发表论文3篇..

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赵庆(2000-),男,陕西商洛人,博士研究生,主要从事能源经济学、资源与环境经济学研究.发表论文3篇..

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Key parameters settings by scenarios

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参数2021年2030年2050年2060年
S0S1S2S0S1S2S0S1S2
人口(亿人)14.1314.1912.8911.69
GDP(万亿元)114.03167.73169.07328.63339.13447.60468.03
工业占比(%)32.5630.6028.8128.6124.1821.7621.6920.6418.5718.65
装备制造业占工业比重(%)26.7439.9743.4744.3851.3356.1957.5954.0058.6059.84
终端电气化率(%)26.4237.2238.6438.5656.1263.5063.2162.9473.8773.41
终端能源强度(tce/万元)0.330.230.20.20.10.080.080.070.050.05
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分情景关键参数设置

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参数2021年2030年2050年2060年
S0S1S2S0S1S2S0S1S2
人口(亿人)14.1314.1912.8911.69
GDP(万亿元)114.03167.73169.07328.63339.13447.60468.03
工业占比(%)32.5630.6028.8128.6124.1821.7621.6920.6418.5718.65
装备制造业占工业比重(%)26.7439.9743.4744.3851.3356.1957.5954.0058.6059.84
终端电气化率(%)26.4237.2238.6438.5656.1263.5063.2162.9473.8773.41
终端能源强度(tce/万元)0.330.230.20.20.10.080.080.070.050.05
), ArticleFig(id=1241050032930091946, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049998842982586, language=EN, label=Table 2, caption=

Source of Supply Side Parameters

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供给端参数数据来源
电力设施最小装机《电力工业资料汇编2021》,Global Energy Monitor,文献[34-36]
电力设施最大装机《电力工业资料汇编2021》,Global Energy Monitor
电力设施成本文献[37-45]
发电效率文献[46-48]
可再生能源出力曲线文献[49-51]
), ArticleFig(id=1241050033106252720, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049998842982586, language=CN, label=表2, caption=

供给侧参数来源

, figureFileSmall=null, figureFileBig=null, tableContent=
供给端参数数据来源
电力设施最小装机《电力工业资料汇编2021》,Global Energy Monitor,文献[34-36]
电力设施最大装机《电力工业资料汇编2021》,Global Energy Monitor
电力设施成本文献[37-45]
发电效率文献[46-48]
可再生能源出力曲线文献[49-51]
), ArticleFig(id=1241050033253053365, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049998842982586, language=EN, label=Table 3, caption=

Constraint parameters by regions

, figureFileSmall=null, figureFileBig=null, tableContent=
区域集中式光伏(万kW)分布式光伏(万kW)海上风电(万kW)陆上风电(万kW)水电(万kW)生物质(PJ)排放(亿tCO2)
华北867100103400 357002793008137554全国36.5
东北124400527001130018050015376732
西北281730035200013720058413760
华中83005660008230055607764
华东380073700967004940017764670
西南34030010700079000239854008
南方26600370008060060800146506728
), ArticleFig(id=1241050033425019834, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241049998842982586, language=CN, label=表3, caption=

分区域约束参数

, figureFileSmall=null, figureFileBig=null, tableContent=
区域集中式光伏(万kW)分布式光伏(万kW)海上风电(万kW)陆上风电(万kW)水电(万kW)生物质(PJ)排放(亿tCO2)
华北867100103400 357002793008137554全国36.5
东北124400527001130018050015376732
西北281730035200013720058413760
华中83005660008230055607764
华东380073700967004940017764670
西南34030010700079000239854008
南方26600370008060060800146506728
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中国跨区域能源系统优化及碳中和情景分析
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赵庆 , 许光清 *
中国环境科学 | 碳排放控制 2025,45(1): 557-570
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中国环境科学 | 碳排放控制 2025, 45(1): 557-570
中国跨区域能源系统优化及碳中和情景分析
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赵庆 , 许光清*
作者信息
  • 中国人民大学生态环境学院,北京 100872
  • 赵庆(2000-),男,陕西商洛人,博士研究生,主要从事能源经济学、资源与环境经济学研究.发表论文3篇..

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*责任作者,教授,
Cross-regional energy system optimization and carbon neutrality scenario analysis in China
Qing ZHAO , Guang-qing XU*
Affiliations
  • School of Ecology & Environment, Renmin University of China, Beijing 100872, China
出版时间: 2025-01-20
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通过构建跨区域能源系统优化模型NEMO-China-MR,基于各区域经济发展和能源需求差异性以及区域资源禀赋和新能源发展异质性,设计了稳妥推进“双碳”目标的参考情景S0,供需协同的综合情景S1和区域均衡发展情景S2,不同情景均能在达成“双碳”目标的同时实现能源需求转型与新型电力系统构建.情景对比的结果表明:供需协同影响电力系统最优发展路径,储能设施和跨区域输电克服了能源资源的时空错配,S1情景需要考虑未来电网的不确定性,S2情景在区域均衡发展促进经济健康发展的同时减少了电网传输的压力,是未来最理想的发展情景.未来需要以经济高质量发展引领能源系统转型,以电气化与电力低碳化协同构建新型电力系统,推进区域均衡发展的同时构建跨区域传输通道.

新型电力系统  /  储能  /  跨区域传输  /  区域均衡发展  /  情景分析

An inter-regional energy system optimization model, NEMO-China-MR, was constructed in this paper. Based on the economic development and energy demand differences across regions, as well as the regional resource endowments and heterogeneity in new energy development, three scenarios were designed: the reference scenario S0for steadily advancing the"carbon peaking and carbon neutrality" targets, the comprehensive scenario S1 for supply-demand coordination, and the balanced regional development scenario S2. Each scenario achieved both the "carbon peaking and carbon neutrality" targets and the transformation of energy demand while building a new power system. The scenario comparison results indicated that supply-demand coordination influenced the optimal development path of the power system. Energy storage facilities and inter-regional transmission overcame the spatial and temporal mismatches of energy resources. Scenario S1 required consideration of future grid uncertainties, while Scenario S2, which promoted healthy economic growth through balanced regional development, reduced grid transmission pressure and was identified as the most ideal development scenario for the future. Moving forward, it is essential to lead the energy system transition through high-quality economic development, construct a new power system through the coordination of electrification and low-carbon power, and build inter-regional transmission channels while promoting balanced regional development.

new power system  /  energy storage  /  cross-regional transmission  /  regional balanced development  /  scenario analysis
赵庆, 许光清. 中国跨区域能源系统优化及碳中和情景分析. 中国环境科学, 2025 , 45 (1) : 557 -570 .
Qing ZHAO, Guang-qing XU. Cross-regional energy system optimization and carbon neutrality scenario analysis in China[J]. China Environmental Science, 2025 , 45 (1) : 557 -570 .
“双碳”目标的实现要求未来实现能源系统的全面转型,而这一转型过程最终能否顺利完成,取决于能源供需双侧的绿色低碳发展是否能够有效协同[1].在能源供给侧,转型的核心在于构建以新能源为主体的新型电力系统.在能源需求侧,如何在保障经济社会发展的同时,转变能源结构与产业结构,实现“双碳”目标是我国面临的重大挑战[2].
在能源供给端,新型电力系统中时空不平衡问题逐渐凸显.新能源出力与电力负荷的不匹配需要储能设施进行消解[3],可再生能源资源禀赋和电力需求在地域上的逆向分布要求跨区域传输[4].且我国存量煤电机组的可调性可以有力地保障电网安全.因此,针对我国新型电力系统长期规划需要充分考虑以上因素并构建多区域模型以反映我国国情.在能源需求端,由于区域发展不平衡问题与能源要素错配的客观存在[5-6].未来各区域的经济社会发展影响了能源与电力系统转型过程,在多区域模型的构建过程中,需要将区域协调发展纳入情景分析.
已有研究建立了大量的电力系统长期规划模型探讨电力系统的未来发展[7-10],这些研究集中于分析能源供给侧变动对能源系统未来发展的影响,而需求侧变动采用相对固定的模式.在电力系统长期规划模型中,LEAP(低排放分析平台)被广泛地应用于多层面与多部门的能源需求和碳排放预测[11-13],其扩展模块NEMO(下一代能源优化模型系统)在能源系统综合核算的基础上,引入了储能设施与跨区域传输,在碳排放等约束条件下,以最低成本分时间片电力负荷曲线和出力曲线的有效匹配.与其它模型相比,该模型对能源需求端变动能够进行有效刻画,且对供给端的模拟过程在准确反应新型电力系统特点的同时实现优化目标.NEMO已在世界多个国家和地区[14-18]的电力系统优化模型中得到应用,以探讨电力系统的发展路径.同时,也有研究使用该模型探讨了我国从全国层面[19-20]到内蒙古[21]、长三角[22]等区域层面的能源转型.相关研究集中于单一区域建模与优化,近期的研究部分引入了储能设施,但研究区域的局限性使得对跨区域传输的模拟不足.
本文重点探讨能源需求变动与不同区域差异性发展对于能源与新型电力系统发展的影响以及未来电力系统的运行形态,因此本文基于LEAP-NEMO平台,构建多区域NEMO-China-MR优化模型.本文的边际贡献在于:基于我国国情进行研究区域划分与情景设计,纳入区域发展异质性这一重要因素,弥补了已有研究需求端设置的不足.在能源系统中考虑电力系统优化问题,同时引入储能与跨区域输电,准确模拟我国各地区新型电力系统特点,反应资源禀赋与新能源发展的区域差异.基于LEAP-NEMO构建的模型具有较好的可扩展性,为未来相关研究的开展提供了有效参考.
在NEMO-China[20]模型的基础上进行空间维度扩展构建NEMO-China-MR(图1),该模型为能源系统中长期规划模型,研究对象为中国的能源系统,一次能源含煤炭、石油、天然气、生物质、太阳能、风能、水能、核能,二次能源含电力和热力,参照电网区域进行区域划分,共华北、东北、西北、华中、华东、西南、南方7个区域.模型的目标为电力系统成本最小化,规划期为2021~2060年.
NEMO-China-MR包含需求、供给、传输和约束条件4个部分.需求模块考虑了技术进步、能源结构和产业结构的变动,根据各行业基准年数据和其后的变动趋势得到各模拟年份分品种的能源需求,其中电力需求分时间片模拟实际负荷.供给模块以能源平衡表为基础表征一次能源的生产和二次能源加工转换过程,在满足需求的同时求解一个成本最小化的优化问题.能源传输过程体现在任意时间片中对区域能源需求的调整.约束条件包含了4项关键约束:发电运行约束、装机容量约束、可再生能源发展目标与碳排放约束以及内置的区域供需耦合约束.
以自下而上的方式构建能源需求,对于任意年份y、区域r,总能源需求TEDr,y为所有部门s对能源品种i需求之和:
部门包括以人口表征活动水平的居民部门和以增加值表征活动水平的产业部门.产业部门包含农林牧渔业、工业、建筑业与第三产业,工业进一步细分为采矿业、轻工业、重工业、装备制造业、电力热力燃气水的生产供应业,第三产业细分为交通运输仓储和邮政业、批发零售和住宿餐饮业、其它第三产业.分品种能源需求以终端用能比例rrr,y,s,i进行分配,包含煤炭、石油、天然气、生物质、电力和热力6种.
供给模块为一个成本优化模型,优化目标即在规划期内电力系统总成本最小化.
发电机组包括燃煤、燃气、生物质、光伏、风电、水电和核电,储能包括抽水蓄能,锂电池和压缩空气.燃煤机组按容量分为≤300×106W,300×106~600×106W,600×106~1000×106W,≥1000×106W,燃气机组分为≤300×106W与>300×106W,光伏分为分布式和集中式,风电分为陆上和海上.
式中:CCr,y,p为初始投入成本;FOMr,y,p为固定运行成本;VOMr,y,p为可变运行成本;ICr,y,p为机组的装机容量;PGr,y,p为发电量.
电力需求以负荷曲线表征波动性,对任意长度为1h的时间片t有:
式中:ELECDr,y,t为电力需求,LCr,y,t,s为负荷曲线,即每年各时间片电力需求占总电力需求的比例.在引入区域间的能源传输后,要求任意区域和时间,区域的发电量加送入电量需要大于等于电力需求加送出电量.
式中:LOSSr,y为线损率;为其它区域向区域r的送入电量;为区域r向其它区域的送出电量;ELECDr,y,t为电力需求.
NEMO-China-MR内置了能源系统约束条件以对供需进行耦合,同时允许设置碳排放约束以及可再生能源发展目标以模拟政策变动,本文的4个关键约束条件如下:
发电运行约束
发电机组的发电量介于发电机组的最大发电能力与最小利用率之间.
式中:len为时间片的长度1h;maxUr,y,p,t和minUr,y,p,t分别为发电机组最大和最小利用率,可再生能源最大利用率即出力曲线.
装机容量约束
发电机组第y年的装机容量不低于计划数,同时不超过社会经济条件与自然条件允许的最大装机容量.
式中:minICr,y,p为发电机组的最低容量即外生容量;maxICr,y,p为发电机组最大装机;enICr,y,p为计算得到的内生容量.
可再生能源发展目标
y年可再生能源的装机容量需要达到给定目标RETy.
碳排放约束
确保全国的碳排放不超过其最大约束量以保障碳中和的实现,包含了加工转换排放和需求端排放,使用排放因子法对碳排放进行核算.
式中:EF为排放因子;GHGTy为碳排放约束.
综合考虑“双碳”目标实现路径,我国能源资源禀赋、地区经济发展与区域间资源传输进行情景设计,本文引入了3个情景:积极稳妥推进“双碳”目标的参考情景S0,多措并举推动能源转型的综合情景S1和推进地区间均衡发展的情景S2.
参考情景S0:以党的二十大报告“积极稳妥推进碳达峰碳中和目标”要求为基础,在实现社会主义现代化强国目标过程中稳步推进高质量发展与经济结构性调整,通过技术进步促进能源效率与电气化率提升,实现能源需求端转型.按照《2030年碳达峰行动方案》与《“十四五”现代能源体系规划》确保如期实现国家自主贡献与碳达峰目标.2030年后以较高的碳汇[23-24]与CCUS能力[25]预期进行能源供给端转型,考虑到我国的基本国情与资源禀赋,2060年保留一定量的煤电作为能源系统兜底[26].跨区域电力传输在当前通道基础上有所发展[27].
综合情景S1:该情景通过供需两侧政策综合发力,以强气候雄心推进“双碳”目标实现.在能源需求端,以产业能源效率提升、用能结构调整和产业结构升级带动未来的能源需求下降时推进电气化率提升支持可再生能源的开发利用.产业结构加快向生产型服务业转换,高耗能部门将进一步“去产能”,装备制造业成为不同区域工业的主导部门.在能源供给端,大力推进可再生能源转型,以COP28的风光装机3倍目标推进2030年前可再生能源建设[28].可再生能源成本进一步下降.常规水电发展有所放缓,且不再进行内陆核电建设,煤电机组在灵活性改造的同时退役加快.跨区域电力传输通道大规模扩容以支持可再生能源开发[29].
区域均衡发展情景S2:该情景充分贯彻落实区域协调发展战略,以高水平区域协调发展推进中国式现代化,基于国务院《关于促进制造业有序转移的指导意见》,因地制宜发展区域特色产业,加速产业升级与产业结构调整,着重推进东北、西北和西南区域先进制造业以及华东与南方区域生产性服务业发展.以新质生产力发展为各区域经济持续健康发展注入新的强劲动力,平衡地区间的经济发展差异.在区域均衡发展的前提下,能源资源得到更有效利用,在能源供给侧推进现代能源体系与新型电力系统建设,跨区域电力传输通道则适当扩容.
所有情景使用相同的人口和负荷曲线,其他需求端参数在S0基础上进行调整设置(表1).
人口基于“七普”数据,在中生育率假设下使用队列要素法外推至2060年[30-31].基准年GDP综合中经网数据库分行业增加值与CEADs城市投入产出表得到[32].未来的GDP增长率结合社会主义现代化强国目标与已有研究进行设定.基准年负荷曲线采用省级电网典型负荷曲线的加权平均,2060年转型至与发达国家相当.
S0以能源强度、三次产业结构和工业内部结构及能源结构调整为途径在需求端实现能源转型.能源强度根据“十四五”规划目标与发达国家能源现状进行外推,2030年达到世界平均水平,2060年达到世界领先水平[33].在产业结构上,参考“十四五”规划和2035年远景目标纲要设置情景的短期产业转型过程,到2035年实现高碳产业贡献不断降低.随着社会主义现代化强国目标实现,到2060年,经济结构转型至与欧盟平均水平相当.在能源结构上,推进各行业和居民用能电气化率不断提升,2050年终端电气化率达到50%,2060年进一步上升至超过60%.
S1在能源需求端实现比S0更强的能源效率提升、全面的电气化转型以及产业结构的快速调整.行业能源效率快速提升实现更高的能源强度下降率,到2060年能源强度为S0的约80%,带动整体能源需求降低.在能源结构上,除生产过程必须的原料用能外,能源需求实现全面的电气化转型,2050年全国终端电气化率超过60%,2060年达到73.9%.在产业结构上,大力推进三次产业向第三产业的全面转型以及工业内部向装备制造业的结构调整,进而带动能源需求的全面转型.
S2以区域协调发展带动全国的能源需求转型,各部门内部实现高能源效率提升与电气化率转型.在参数设置上能源强度下降率与能源结构变化与S1保持一致.东北、西北和西南电网区域以先进制造业与区域性特色产业发展为契机,获取经济增长新动能,从2025年起GDP增长率与装备制造业占比同时提升,采矿业占比有所下降.而华东和南方电网区域实现了更高的第三产业和装备制造业占比.区域均衡发展在全国层面提升了经济总量,进一步改善了经济结构与能源需求的全国分布.
供给端的参数包含电力设施的成本与外生装机(表2).与需求端参数相同,在设置S0参数的基础上,进一步调整得到S1和S2参数.
参考控制指标、国内外研究报告、上市公司数据设置成本参数.燃煤机组与燃气机组分容量选择相应技术进行3项成本设定[37-38].生物质机组采用垃圾发电机组数据、核电以三代核电机组价格为准、水电采用常规水电价格[39].光伏和风电进行应用场景区分,光伏分为分布式和集中式,同时东北最高,西北最低.风电按地形分为平原戈壁、山区以及海上风电[38,40-41].核电和水电的运维成本选择代表性上市公司公开数据进行表征.可再生能源的可变运维成本均为0.抽水蓄能的初始投入成本来源于《抽水蓄能产业发展报告2022》[42],运维成本参考常规水电略有上调.锂电池与铅酸电池的成本参照Viswanathan等(2022)[43].压缩空气储能参照张玮灵等(2023)的总结[44].未来的成本变动结合历史趋势与研究预测进行设定[38,43-45].
除核电和水电外,外生装机根据已有建设计划进行设定到2030年,煤电机组按照当前的建设进度完成建设后,不再额外新增建设计划,综合《电力工业资料汇编2021》与Global Coal Plant Tracker逐年设置外生容量,到2060年保留约6亿kW煤电机组.燃气机组、生物质机组、风电和光伏的外生容量结合Global Energy Monitor与《电力工业资料汇编2021》,保证2030年实现12亿kW装机.新型储能设施根据各地区“十四五”储能发展目标进行设置.水电规模在2030年达到4.0亿kW,2035年完成大部分水电的开发,达到经济可开发极限,2060年,全国水电装机容量达到5.4亿kW.基本达到技术可开发极限水平,与发达国家开发水平相当[34].核电规模参考舒印彪等(2021)[35]的预测,2030年前,当前的沿海站址基本开发完毕,2030年核电装机容量约为1.2亿kW.2030年后适时重启内陆核电建设,2060年装机容量增长至约3.7亿kW.
S1和S2情景的供给端参数相同,两个情景大力推进供给侧的可再生能源转型.在成本上,进一步促进风光成本下降,到2035年,风电和光伏的成本额外下降10%.在最小装机上,在区域可再生能源禀赋允许的前提下,2030年风光外生装机达到2020年的3倍.常规水电在2035年达到4.3亿kW经济可开发极限后后不再大规模新建.内陆核电停止建设,相关区域核电的最大装机容量设置为0.供给端对煤电机组进行灵活性改造的同时加快煤电机组的退役进度,到2060年保留更少的4亿kW煤电装机[26].
跨区域电力传输在当前电力传输通道基础上所有发展,在2030年前,保持年均约4.7%的增长率[27].2030年后,跨区域电力传输增长率下降至1%,到2060年,实现跨区域输电1.17万亿kW·h.S1情景中,2030年后跨区域输电保持约3%的年均增长,2060年实现2.15万亿kW·h输送量.S2情景中区域均衡发展减少了跨区域输电需求,与S0保持一致.线损率参照《电力工业资料汇编2021》分区域数据进行设定,到2060年下降至当前各区域最低水平2%.
S0情景中在最小利用率上,所有发电机组的最小利用率均设置为0,非可再生能源机组的最大利用率为100%.同时考虑长期的技术进步,对燃煤与燃气[46]、生物质[47]与核电机组[48]设置逐渐提升的技术效率.对于可再生能源,风光出力曲线均体现季度与日内变化[49-50].水电的最大利用率在年平均利用率的基础上,根据各地区代表性河流的径流量进行调整设置季度变化[51].水电资源开发上限为技术可开发极限.与装机对应,陆上和海上风电[52]、分布式和集中式光伏[53]分别设定开发上限(表3).考虑粮食安全问题,不引入额外的专门能源作物,生物质能的资源约束以农林废弃物的开采潜力进行设置[54].2030年的可再生能源发展目标即NDC12亿kW风光装机目标.综合全国陆地碳汇[23]、海洋碳汇[24]与2060年CCS能力的最大值[25]设定36.5亿tCO2的排放约束,保证碳中和目标的实现.S1和S2情景中,更强的技术进步使得风电和光伏出力曲线在2030年进一步提升.与COP28目标相符,力争到2030年,实现风光装机达到2020年的3倍[28].
在经济保持高质量发展的同时,能源强度下降与产业结构调整是能源需求总量下降的最主要影响因素.用能结构和跨区域输电对于区域的能源需求同样有所影响.基准年我国能源强度高于世界平均水平,且由于我国“富煤、缺油、少气”的资源禀赋,我国呈现以煤炭为主的终端能源消费结构,与发达国家相比,电力消费水平还有所不足,电气化率仍有较大提升空间.
3个未来情景中(图2图3),2025年前随着经济复苏,终端能源需求将保持增长趋势,2025年终端能源需求达到38亿~39亿tce(电热当量法,后同),随后开始下降.在一次能源需求上,S0在2030年前将保持增长,2030年达到53.8亿tce.S1和S2在2025年达到一次能源消费高峰,分别为51.3和51.6亿tce.
能源强度对未来的能源需求下降过程产生主要影响,而区域均衡发展则使得全国的能源需求略有上升.到2035年,S0、S1、S2下的全国终端能源需求分别为36.4、30.1、31.0亿tce.2060年,3种假设的的全国终端能源需求分别下降到29.5亿、21.7亿和23.5亿tce.
不同情景中工业能源需求下降主导了我国的能源需求变动,居民能源需求呈现先增后降的趋势.随着经济发展与产业转型,第三产业能源需求总量与占比同时上升.2021年,工业部门是我国最大的能源需求部门,以25.0亿tce的能源需求占据了我国终端能源需求的60.2%.全国跨区域传输电力占到了全国终端能源需求的1.8%.分区域来看,华北电网工业需求占比全国最高.西北电网作为跨区域送电的主要供端,输电需求占到了区域总能源需求的7.5%,华东电网作为主要受端,接受外送电力满足了当地4.9%的能源需求.
S0情景从2025年起各区域能源需求匀速下降,其中华北电网区域工业用能下降最快,而其它区域变动相对较少,华东电网将是未来的主要能源需求地.到2060年,全国总终端能源下降至29.5亿tce,其中工业15.8亿tce,比2021年下降49.1%,占到总能源需求的比例超过一半.第三产业增长至11.6亿tce,较基准年增长74.0%,是未来的重要能源需求来源.
S1情景中,能源效率迅速提升带动短期内能源需求的减少,且未来更高的第三产业占比与装备制造业占比同样改变了能源需求结构.从2025年开始,各地工业能源需求下降更为迅速,到2035年,工业能源需求总量18.5亿tce,比S1情景减少5.2亿tce.2060年,总能源需求21.7亿tce,其中工业10.8亿tce,占50.1%.该情景中跨区域输电大幅发展,西北、东北和西南电网的输电需求占到了总终端需求的36.2%、10.8%和25.1%,受端中华东电网36.0%的能源需求由输入电力满足.
S2情景中,东北、西北和西南电网的产业发展产生了较大的能源需求,短期内能源需求增长持续,长期能源需求下降减缓,同时降低了跨区域输电压力.与S1相比,该情景下全国总能源需求略高,但跨区域输电总量下降,西北和西南电网2060年能源需求与基准年持平.华东和南方电网随着装备制造业快速对其它工业行业产生替代,能源需求下降,同时两区域第三产业能源需求略有增长,华东电网调入电力占比下降到18.8%,电网安全性有所提升.
电力系统的最优发展路径包括3个阶段(图4图5):2030年前,按当前规划推进各项基础设施建设,实现国家自主贡献中的风光装机12亿kW目标以及煤电达峰;2030年至2050年间,有序推进火电退役过程以及可再生能源对于火电的替代;2050年后,电力系统加速向可再生能源的过渡,保留合理的火电装机以应对负荷波动.
2030年前的电力系统发展路径取决于当前建设计划,在如期推进老旧煤电退役的基础上,以确保能源安全为前提,新增一批清洁高效的超临界和超超临界机组,煤电整体在2030年达峰,峰值约12.9亿kW.S0情景持续推进可再生能源发展,2030年实现风光总装机13.4亿kW,提前实现国家自主贡献目标.储能设施建设推进,抽水蓄能设施达到1.7亿kW.S1和S2情景在华北与西北电网实现更高的风光装机.在全国范围内,2030年实现光伏装机8.1亿kW,风电装机10.2亿kW.
电力系统中长期发展的主要影响因素是需求端变动.2030~2050年间,电力需求增速放缓,电力总装机增长放缓,在煤电机组有序退役的基础上,全国范围内的风光装机保持增长趋势.S0情景中,华东电网需要大量建设分布式光伏设施应对能源需求.西南电网持续推进水电开发.抽水蓄能设施进一步建设,电化学储能以及压缩空气储能发展相对缓慢.S1中,华东、华中、南方电网电力需求相比S0有所下降,因此装机较少,其中大幅减小了这些区域分布式光伏的开发压力,同时也减少了消纳过程中需要的抽水蓄能装机.S2中,随着区域产业均衡发展,东北、西北、西南电网的风光资源得到更大规模开发.西南电网水电开发在2040年左右达到极限,而后需要开发风光资源.不同情景中,这一时期由于锂电池成本仍较高,且已建成的抽水蓄能与火电均能够发挥有效的调峰作用,锂电池装机在没有政策补贴的情况下难以进一步扩大.
2045年后,随着当前火电机组逐渐到达退役年限,可再生能源对于火电的替代加快.区域根据资源禀赋进行可再生能源替代,西北电网以集中式光伏为主,西南电网在水电装机达到瓶颈后风光均有所增长,其它电网主要加大分布式光伏的发展力度.南方电网则会在分布式光伏发展的同时再发展一批集中式光伏.S0中,华东电网在2050年左右将达到分布式光伏开发上限,后装机容量的提升依赖于陆上风电.S1和S2中,由于跨区域传输和区域产业结构调整的存在使得该区域开发压力下降.
到2060年,我国基本建立起完善的新型电力系统,服务于经济社会发展与“碳中和”目标的实现.总装机容量主要由能源需求决定,S0、S1和S2的3个情景全国装机总量分别达到84.5亿、80.0亿和84.6亿kW.S0情景保留约6亿kW煤电,核电与水电基本达到开发极限,分别装机3.6亿和5.4亿kW.风电和光伏成为电力系统的主力,风光总装机达57.2亿kW,储能设施配套可再生能源发展建设完毕,电化学储能得到进一步发展,锂电池装机达3.1亿kW.S1情景煤电减少至4亿kW,跨区域传输以输出地风电和集中式光伏的增加减少了输入地的集中式光伏开发.S2情景中,东北、西北和西南电网的产业发展对能源需求的改变大于跨区域通道建设,因此与S1相比风光开发程度更高.
选取第一模拟年2022年,2030年,2050年和2060年4个关键年份全国电力系统的运行状况进行分析,同时以2060年为例进行分区域分析(图6).
当前煤电承担了电力系统中的基础负荷,从春季到冬季,煤电输出逐渐增加,同时煤电在日间光伏发电高峰输出有所下降.气电和生物质发电在日内波动较小,基本达到满负荷运行.水电运用主要存在季节变化,夏秋季水电出力较高,春冬相对较低.光伏发电仅在日间出力,正午出力最高,风电与光伏则具有一定的互补性.核电运行相对稳定,日间和季节差异不大.在2022年,储能设施运用相对较少,在夏秋两季,约0.4GW的抽水蓄能电站在2:00~6:00充电,用电高峰时期放电,主要对风电有所消纳,与当前抽蓄电站利用峰谷电价差盈利的模式一致.
2030年,随着可再生能源装机进一步增长,煤电输出总体降低,电力系统呈现以可再生能源为主体,化石能源尤其是煤电调节负荷的特点.随着光伏装机增长,储能设施的运用转变为日间充电夜间放电.不同情景全国电力系统运行略有差异,S0情景约有6GW的抽水蓄能设施充电,同时在夜间放电.S1和S2风电装机更高,对于光伏有所替代,消纳需求减少使得抽水蓄能充/放电量对应下降.
2050年,可再生能源装机进一步攀升,供需两侧同时影响了电力系统运行.电力负荷S0、S2、S1依次下降,由于光伏的大量装机,正午的电力输出已高于用电需求,夜间多种电力设施共同作用应对光伏下降.
2060年,新型电力系统进入成熟期,具有全新形态的电力系统全面建成.不同情景中,日间的发电输出功率将达到电力负荷的2倍,风电和太阳能成为电力的最主要来源,日间储能设施充电,同时在夜间用电高峰放电.在保留煤电的情景中,火电机组的运用以1000MW超超临界机组为主,主要用于满足夜间和冬季负荷.
到2060年,各区域根据自身资源禀赋与经济发展实际,全面建成了以新能源为主体的新型电力系统,不同情景的电力系统运行整体一致(图7),除西南电网外,各区域的日间负荷主要由光伏发电满足,且过量的光伏电力需要储能设施充电进行收集,而其它时段的电力系统运行随装机不同存在显著差异.
S0情景中,华北电网常规水电较少,日间发电主要由太阳能提供,夜间主要为风电,春季由于风光互补较好,几乎没有启用火电设施进行调峰,在秋冬两季存在一定程度火电的启用.东北电网电力负荷小,风光加核电调度能够有效满足需求,火电运用很少.西北电网保留有最高的火电.华中和华东电网的运行模式类似,日间光伏大规模出力,同时抽水蓄能压平“鸭型曲线”,夜间多种电力设施共同作用应对负荷.到2060年,由于华东电网的光伏已到达开发极限,同时水能禀赋不足,需要更高的风电输出.西南电网用电负荷低,水电开发程度较高,且风光资源禀赋相比于其它区域不足,在2060年,在夏秋两季,几乎所有的发电量由水电提供,在冬春两季,河流径流量下降,水电出力下降,光伏在日间替代了部分水电出力,夜间负荷则由气电和储能放电满足.在高水电比例下,储能设施的作用相对较弱[14].南方电网的水电多而风电少,水电提供了日内更稳定的出力,储能除抽水蓄能外,由于南方电网区域多喀斯特地貌,压缩空气储能成本较低,这一储能技术得到了一定的应用.在冬季,水电不足启用了部分煤电.
S1情景的电力负荷与S0对比有所下降,光伏出力受到最大影响,不同区域光伏出力均有所下降,该情景下华北电网春季将主要依赖风电.同时,跨区域输电的增加推进了西北、西南的可再生能源出力,在常规水电下降的情况下,西南电网需要更多的风光出力和抽水蓄能满足电力负荷,华东和南方电网的最高负荷下降,但电力系统运行模式与S0相比基本保持不变.与S1相比,S2中东北、西北和西南电网的电力负荷曲线进一步提高,进而增加了3个区域的可再生能源出力,同时要求一定的灵活性气电.同时华东和南方电网的电力负荷进一步下降,煤电运用减少.
分情景来看(图8图9),需求端变动是影响全国排放的主要因素,供给端变动导致的排放变动主要来源于火电运用的下降.2021年,基于NEMO-China-MR核算,包含需求端直接燃烧过程和电力加工转换过程,全国碳排放共105.1亿tCO2.
基准年,我国的CO2排放有42.0%来源于电力系统的发电过程,共44.2亿t,其他来源于需求端的化石燃料直接燃烧,其中,一次能源需求中,煤炭、石油、天然气和生物质的排放逐渐降低,分别为38.5、17.9、4.4和0.1亿tCO2.
S0情景在2025年实现碳达峰目标,随后进入平台期,2030年后排放总量加速下降,到2060年实现CO2排放33.4亿t.充分考虑到我国的资源禀赋以及产业结构,该情景需求端仍有约23.2亿tCO2排放,与碳汇能力相当,因此碳中和目标实现需要一定CCUS能力的作为补充,同时电力系统需要实现近零排放.
S1和S2情景均在2024年达峰,峰值分别为108.9和109.2亿tCO2,两个情景没有显著的平台期,尽管电力需求有所增长,但技术进步带来的能源效率提升和产业结构调整带来了更快的能源需求和排放量双重下降,所有情景均能够如期完成碳达峰目标.到2060年,两个情景排放分别为17.1和19.3亿tCO2,碳汇能够有效覆盖,进而能够确保实现碳中和目标.
碳排放与区域能源需求直接相关,同时也受到区域能源结构影响,需求端减排与电力排放下降同等重要.基准年中,华北电网能源需求总量与煤炭需求均较高,进而排放最高,达到26.9亿tCO2,其次是华东电网的19.5亿tCO2,西北、华中和南方电网排放相当,分别为12.4、13.6和13.1亿tCO2,东北电网9.9亿tCO2,西南电网能源需求低,且电力结构以水电为主,因此排放最低,为5.6亿tCO2.
S0情景中,不同区域均在2030年前实现碳达峰,华北电网达峰年最早,在2024年达峰,西北电网达峰最晚,在2029年达峰,2030年后不同区域排放均开始下降.从2021年到2060年,需求端减排37.7亿tCO2,电力减排33.9亿tCO2,电力减排的主要途径是煤电的减少.不同区域中,华北电网减排贡献最大,达到18.8亿tCO2.
S1情景的供需两侧同时作用实现了更高的减排量,与S0相比,S1从2021年到2060年实现了621.9亿tCO2的累计减排,需求端贡献了59.0%.到2060年全国电力排放下降至5亿tCO2,在考虑到能源安全的前提下难以继续下降,全国排放以需求端为主.尽管该情景中跨区域输电有所增长,但主要以可再生能源进行满足,因此供电端电力排放仍低于S0情景.
S2情景中,区域均衡发展在促进东北、西北和西南电网可再生能源开发的同时的提高了灵活性火电的使用,因此排放水平略有上升.与S1相比,短期内3个区域能源需求迅速增加,进而煤电有所增长,中长期可再生能源装机提升,灵活性气电增加,进而排放增长.华东和南方电网由于生产性服务业与装备制造业对高耗能产业的替代,能源需求下降导致火电有所下降,进而碳排放略有下降.在全国层面,区域均衡发展以碳排放略有增长为代价,促进了经济发展与地区间结构调整,但仍保证了碳中和目标的实现.
“双碳”目标下,以经济高质量发展引领能源系统转型是必由之路.推动新质生产力发展与产业结构优化升级,通过鼓励发展生产性服务业与先进制造业,引导高耗能产业转型升级,鼓励通过技术手段提高能源效率,减少对化石能源的依赖和消耗,实现碳排放下降.
新型电力系统构建在需求端要以终端电气化率提升作为基础,在供给端要持续推进电力低碳化进程,在可再生资源开发利用的同时配套建设储能设施.同时要盘活存量资源,推进火电机组灵活性改造,合理规划电力系统运行,综合火电调峰、风光互补以及储能设施运用等多项手段确保能源供给的灵活性和适应性.
推进地区间资源优势互补和欠发达地区产业升级,以区域均衡发展缓解要素错配,实现能源需求快速转型.充分利用西北电网沙戈荒风电光伏基地与西南电网水电资源,加强能源系统的互联互通,优化跨区域能源传输通道的规划设计,确保通道建设与区域发展需求相适应,提高能源传输效率.S1情景中,受端电网将接近一半的电力来源于跨区域传输,需要考虑未来电网面临的不确定性.
4.1 供需协同影响电力系统最优发展路径:2030年前实现NDC风光装机12亿kW目标与煤电的达峰;2030年至2050年间,有序推进火电退役过程以及可再生能源对于火电的替代,不同区域根据资源禀赋选择最优的可再生能源进行发展;2050年后,电力系统加速向可再生能源的过渡,保留合理的火电装机.最优路径的建设需要供需协同.
4.2 储能与跨区域设施共同作用克服能源资源时空错配:2050年后储能设施得到大规模应用,消纳光伏发电,夜间放电,应对用电负荷.跨区域输电建设以供电端气电需求与碳排放增加为主要代价,解决了受端电网可再生能源资源禀赋不足的问题.
4.3 区域均衡发展情景在实现碳中和的同时,减少了“双碳”背景下能源供需不平衡与经济发展差异,是未来最理想的发展情景.
  • 国家电网公司总部管理科技项目(1400-202224242A-1-1-ZN)
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  • 接收时间:2024-07-05
  • 首发时间:2026-03-18
  • 出版时间:2025-01-20
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  • 收稿日期:2024-07-05
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国家电网公司总部管理科技项目(1400-202224242A-1-1-ZN)
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    中国人民大学生态环境学院,北京 100872

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2种不同金属材料的力学参数

Family
属数
Number of
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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