Article(id=1236323805073830710, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236323797054312545, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202411235, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1731340800000, receivedDateStr=2024-11-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772692164915, onlineDateStr=2026-03-05, pubDate=1758729600000, pubDateStr=2025-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772692164915, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772692164915, creator=13701087609, updateTime=1772692164915, updator=13701087609, issue=Issue{id=1236323797054312545, tenantId=1146029695717560320, journalId=1210938733613449225, year='2025', volume='54', issue='9', pageStart='1', pageEnd='178', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772692163003, creator=13701087609, updateTime=1772692223569, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236324051153646111, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236323797054312545, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236324051153646112, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236323797054312545, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=35, endPage=45, ext={EN=ArticleExt(id=1236323807250674545, articleId=1236323805073830710, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Performance analysis of peak load balancing for compressed air energy storage assisted solar power station, columnId=1236321537943458718, journalTitle=Thermal Power Generation, columnName=Special topic on energy storage and power generation coupling technology, runingTitle=null, highlight=null, articleAbstract=

In order to effectively improve the energy efficiency and operational flexibility of solar power generation, an integrated system coupling solar photovoltaic, solar thermal and compressed air energy storage is proposed. During the day, the compressed air energy storage system will store the photovoltaic abandoned power, and transfer the compression heat to the photothermal power station. At night, the compressed air energy storage system releases air and uses water supply of the photothermal power station to heat up, thereby increasing the power generation load of the unit. Based on the system simulation, the coupling scheme is analyzed thermodynamically and economically. The overall generation efficiency of the coupled system is 41.24%, while the overall exergy efficiency is 66.79%. The round-trip efficiency of the compressed air energy storage system is 72.14%, while the exergy efficiency of the compressed air system is 84.30%, both of which have increased significantly. The peaking depth of the coupled system is 7.02% in the daytime and 19.69% in the evening. In addition, the dynamic recovery cycle of the coupling scheme is 3.10 years, and the net present value is 41.350 6 million yuan.

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为有效提高太阳能发电的能源利用率和运行灵活性,提出了一种耦合太阳能光伏、太阳能光热、压缩空气储能的集成系统。在白天,压缩空气储能系统将光伏弃电存储,并将压缩热传递至光热电站;在晚上,压缩空气储能系统释放空气,并采用光热电站的给水加热,从而提高机组的发电负荷。基于系统模拟,对耦合方案进行了热力学分析和经济性分析。分析结果表明:耦合系统光热整体发电效率为41.24%,整体㶲效率为66.79%;压缩空气储能系统往返效率为72.14%,压缩空气系统㶲效率为84.30%,均有显著提高;耦合系统白天的调峰深度为7.02%,晚上调峰深度为19.69%;此外,耦合方案的动态回收周期为3.10年,净现值为4 135.06万元,与非耦合系统相比,均表现良好。

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薛小军(1992),男,副研究员,博士,主要研究方向为火储调峰、压缩空气储能以及熔盐储热技术,
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葛士宇(2001),男,汉,硕士研究生,主要研究方向为压缩空气储能系统集成技术,

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Journal of Energy Storage, 2021, 34: 102000., articleTitle=Compressed air energy storage systems: components and operating parameters: a review, refAbstract=null), Reference(id=1236323826305397265, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[25], rfOrder=40, authorNames=甘肃省发展和改革委员会, journalName=null, refType=null, unstructuredReference=甘肃省发展和改革委员会. 甘肃省发展和改革委员会关于调整销售电价及优化峰谷分时电价政策有关事项的通知: 甘发改价格〔2024〕424号[EB/OL]. (2020-11-30) [2025-05-27]. https://fzgg.gansu.gov.cn/fzgg/c106108/202106/18022c0ea91e4a9ab215bc3d28b79c06.shtml., articleTitle=甘肃省发展和改革委员会关于调整销售电价及优化峰谷分时电价政策有关事项的通知: 甘发改价格〔2024〕424号, refAbstract=null), Reference(id=1236323826372506132, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[25], rfOrder=41, authorNames=Gansu Provincial Development and Reform Commission, journalName=null, refType=null, unstructuredReference=Gansu Provincial Development and Reform Commission. Notice of Gansu Provincial Development and Reform Commission on matters related to adjustment of sales electricity price and optimization of peak-valley time-sharing electricity price policy: GFGJG〔2024〕No. 424[EB/OL]. (2020-11-30) [2025-05-27]. https://fzgg.gansu.gov.cn/fzgg/c106108/202106/18022c0ea91e4a9ab215bc3d28b79c06.shtml., articleTitle=Notice of Gansu Provincial Development and Reform Commission on matters related to adjustment of sales electricity price and optimization of peak-valley time-sharing electricity price policy: GFGJG〔2024〕No. 424, refAbstract=null)], funds=[Fund(id=1236323820907327892, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, awardId=52406019, language=EN, fundingSource=National Natural Science Foundation of China: Youth Science Foundation Project(52406019), fundOrder=null, country=null), Fund(id=1236323820987019672, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, awardId=52406019, language=CN, fundingSource=国家自然科学基金青年科学基金项目(52406019), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1236323810656449526, tenantId=1146029695717560320, journalId=1210938733613449225, 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figureFileBig=g1fH7dSaRh65EfvE4NWk+A==, tableContent=null), ArticleFig(id=1236323816687857954, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=图6, caption=耦合系统改造投资成本, figureFileSmall=TU89BZ4WmG4gxAfjedeZgg==, figureFileBig=g1fH7dSaRh65EfvE4NWk+A==, tableContent=null), ArticleFig(id=1236323816813687082, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.1, caption=

Basic parameters of the case photothermal unit

, figureFileSmall=null, figureFileBig=null, tableContent=
项目白天夜晚
熔盐循环量/(kg·s–1)286.09143.04
主蒸汽压力/MPa14.0014.00
温度/℃540.60540.60
质量流量/(kg·s–1)40.8520.43
再热蒸汽压力/MPa2.752.75
温度/℃540.60540.60
质量流量/(kg·s–1)36.4118.21
排汽压力/MPa0.010.01
温度/℃45.8045.80
质量流量/(kg·s–1)28.0014.00
光热机组总功率/MW50.0025.00
净功率/MW45.3022.65
净发电效率/%38.6138.61
), ArticleFig(id=1236323816922738990, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表1, caption=

案例光热机组基本参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目白天夜晚
熔盐循环量/(kg·s–1)286.09143.04
主蒸汽压力/MPa14.0014.00
温度/℃540.60540.60
质量流量/(kg·s–1)40.8520.43
再热蒸汽压力/MPa2.752.75
温度/℃540.60540.60
质量流量/(kg·s–1)36.4118.21
排汽压力/MPa0.010.01
温度/℃45.8045.80
质量流量/(kg·s–1)28.0014.00
光热机组总功率/MW50.0025.00
净功率/MW45.3022.65
净发电效率/%38.6138.61
), ArticleFig(id=1236323817090511155, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.2, caption=

Parameters of reheating system of the case photothermal unit

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名称抽汽给水/凝结水
入口温度出口温度入口温度出口温度
1号高温加热器385.9261.4230.8237.8
2号高温加热器320.6232.0187.8230.3
除氧器399.2179.9146.2179.9
3号低温加热器302.1147.9111.7146.2
4号低温加热器189.0111.477.9109.7
5号低温加热器80.375.945.974.2
), ArticleFig(id=1236323817207951670, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表2, caption=

案例光热机组回热系统参数

, figureFileSmall=null, figureFileBig=null, tableContent=
名称抽汽给水/凝结水
入口温度出口温度入口温度出口温度
1号高温加热器385.9261.4230.8237.8
2号高温加热器320.6232.0187.8230.3
除氧器399.2179.9146.2179.9
3号低温加热器302.1147.9111.7146.2
4号低温加热器189.0111.477.9109.7
5号低温加热器80.375.945.974.2
), ArticleFig(id=1236323817346363707, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.3, caption=

Basic design parameters of the compressed air energy storage system

, figureFileSmall=null, figureFileBig=null, tableContent=
项目1级数2级数3级数4级数项目1级数2级数3级数4级数
压缩比4.103.463.332.30膨胀比2.922.892.852.82
压缩机出口压力/MPa0.411.394.5010.02透平出口压力/MPa2.340.820.290.10
空气质量流量/(t·h–1)52.6052.6052.6052.60空气质量流量/(t·h–1)105.20105.20105.20105.20
压缩机等熵效率/%90909090透平等熵效率/%88888888
压缩机出口温度/℃187.0195.0190.0141.0透平进口温度/℃120.0120.0120.0120.0
储能时间/h8释能时间/h4
系统额定输入功率/MW7.78系统额定输出功率/MW10.00
), ArticleFig(id=1236323817488970052, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表3, caption=

压缩空气储能基本设计参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目1级数2级数3级数4级数项目1级数2级数3级数4级数
压缩比4.103.463.332.30膨胀比2.922.892.852.82
压缩机出口压力/MPa0.411.394.5010.02透平出口压力/MPa2.340.820.290.10
空气质量流量/(t·h–1)52.6052.6052.6052.60空气质量流量/(t·h–1)105.20105.20105.20105.20
压缩机等熵效率/%90909090透平等熵效率/%88888888
压缩机出口温度/℃187.0195.0190.0141.0透平进口温度/℃120.0120.0120.0120.0
储能时间/h8释能时间/h4
系统额定输入功率/MW7.78系统额定输出功率/MW10.00
), ArticleFig(id=1236323817610604872, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.4, caption=

The model validation results

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子系统项目设计值[13,15]模拟值误差
光热机组熔盐循环量/(kg·s–1)281.51286.09–1.63%
净输出功率/MW45.3045.300
系统效率/%39.2338.611.58%
CAES系统系统输入功率/MW7.787.822–0.54%
系统输出功率/MW10.0010.07–0.70%
循环效率/%64.2763.920.54%
), ArticleFig(id=1236323817723851085, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表4, caption=

模型验证结果

, figureFileSmall=null, figureFileBig=null, tableContent=
子系统项目设计值[13,15]模拟值误差
光热机组熔盐循环量/(kg·s–1)281.51286.09–1.63%
净输出功率/MW45.3045.300
系统效率/%39.2338.611.58%
CAES系统系统输入功率/MW7.787.822–0.54%
系统输出功率/MW10.0010.07–0.70%
循环效率/%64.2763.920.54%
), ArticleFig(id=1236323817824514386, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.5, caption=

Investment cost estimation for main equipment of the CAES system

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设备名称投资成本估算(方程)公式名词解释
压缩机Ccom=N51mairN52ηis,Comrpln(rp)N51=504.1美元/(kg/s);N52=0.9;mair为空气的质量流量,kg/s;rp为压气机的压比
透平CEXP=(266.3×mair0.92ηEXP)×ln(EXP)×(1+e0.036Tin54.4)Tin为膨胀机入口温度,℃;ηEXP为膨胀机的绝热效率;EXP为膨胀机的膨胀比
换热器ZIntc=ZR(AAR)0.6AR为换热器的面积,大小为100 m2ZR=85 080美元
发电机PEC=60(1 000W)0.95 W为发电机的发电功率,MW
节流阀CTV=114.5mm为质量流量,kg/s
储气室ZCAS=1.218exp[2.631+1.367 3(lnVCAS)0.063 09(lnVCAS)2]VCAS为储气罐的体积,m3
), ArticleFig(id=1236323818055201110, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表5, caption=

CAES系统主要设备投资成本估算

, figureFileSmall=null, figureFileBig=null, tableContent=
设备名称投资成本估算(方程)公式名词解释
压缩机Ccom=N51mairN52ηis,Comrpln(rp)N51=504.1美元/(kg/s);N52=0.9;mair为空气的质量流量,kg/s;rp为压气机的压比
透平CEXP=(266.3×mair0.92ηEXP)×ln(EXP)×(1+e0.036Tin54.4)Tin为膨胀机入口温度,℃;ηEXP为膨胀机的绝热效率;EXP为膨胀机的膨胀比
换热器ZIntc=ZR(AAR)0.6AR为换热器的面积,大小为100 m2ZR=85 080美元
发电机PEC=60(1 000W)0.95 W为发电机的发电功率,MW
节流阀CTV=114.5mm为质量流量,kg/s
储气室ZCAS=1.218exp[2.631+1.367 3(lnVCAS)0.063 09(lnVCAS)2]VCAS为储气罐的体积,m3
), ArticleFig(id=1236323819535790425, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.6, caption=

Energy analysis results of the primary system and the coupling system

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项目原系统耦合系统
白天光伏发电功率/MW50.0042.00
光热机组净发电功率/MW45.3046.61
CAES输入功率/MW7.788.00
储能时间/h8.008.00
CAES输入能量/(MW·h)64.2464.00
夜晚光热机组净发电功率/MW22.6521.04
CAES输出功率/MW10.006.07
释能时间/h4.008.00
CAES输出能量/(MW·h)40.0048.56
性能指标光热整体净发电效率/%38.6041.24
CAES系统往返效率/%64.3572.14
), ArticleFig(id=1236323819649036638, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表6, caption=

原系统与耦合系统能量分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
项目原系统耦合系统
白天光伏发电功率/MW50.0042.00
光热机组净发电功率/MW45.3046.61
CAES输入功率/MW7.788.00
储能时间/h8.008.00
CAES输入能量/(MW·h)64.2464.00
夜晚光热机组净发电功率/MW22.6521.04
CAES输出功率/MW10.006.07
释能时间/h4.008.00
CAES输出能量/(MW·h)40.0048.56
性能指标光热整体净发电效率/%38.6041.24
CAES系统往返效率/%64.3572.14
), ArticleFig(id=1236323819745505635, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.7, caption=

Analysis results of peak regulation performance of the coupling system

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
原系统光伏发电输出功率/MW50.00
原系统光热净发电输出功率/MW45.30
压缩空气储能系统输入功率(弃电)/MW8.00
耦合系统储能过程光热净输出功率/MW46.61
压缩空气储能系统输出功率/MW6.07
耦合系统释能过程光热净输出功率/MW21.04
白天调峰容量/MW6.69
晚上调峰容量/MW4.46
白天调峰深度/%7.02
晚上调峰深度/%19.69
), ArticleFig(id=1236323819854557546, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表7, caption=

耦合系统调峰性能分析结果

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项目数值
原系统光伏发电输出功率/MW50.00
原系统光热净发电输出功率/MW45.30
压缩空气储能系统输入功率(弃电)/MW8.00
耦合系统储能过程光热净输出功率/MW46.61
压缩空气储能系统输出功率/MW6.07
耦合系统释能过程光热净输出功率/MW21.04
白天调峰容量/MW6.69
晚上调峰容量/MW4.46
白天调峰深度/%7.02
晚上调峰深度/%19.69
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Exergy efficiency analysis for the coupled system

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项目数值
原系统光热机组熔盐㶲输入/(MW·h)35.56
总㶲输出/(MW·h)22.65
总㶲输入/(MW·h)35.56
㶲效率/%63.69
耦合系统熔盐㶲输入/(MW·h)35.56
空气㶲输入/(MW·h)5.02
压缩空气储能㶲输出/(MW·h)6.07
光热机组㶲输出/(MW·h)21.04
总㶲输出/(MW·h)27.11
总㶲输入/(MW·h)40.59
㶲效率/%66.79
整体㶲效率提升值3.10百分点
), ArticleFig(id=1236323820106215795, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表8, caption=

耦合系统整体㶲效率分析

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项目数值
原系统光热机组熔盐㶲输入/(MW·h)35.56
总㶲输出/(MW·h)22.65
总㶲输入/(MW·h)35.56
㶲效率/%63.69
耦合系统熔盐㶲输入/(MW·h)35.56
空气㶲输入/(MW·h)5.02
压缩空气储能㶲输出/(MW·h)6.07
光热机组㶲输出/(MW·h)21.04
总㶲输出/(MW·h)27.11
总㶲输入/(MW·h)40.59
㶲效率/%66.79
整体㶲效率提升值3.10百分点
), ArticleFig(id=1236323820211073399, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.9, caption=

Exergy analysis for the compressed air energy storage system

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项目㶲或㶲损失/(MW·h)占比/%
㶲输入(电力)64.0063.76
㶲输入(空气)0.120.12
㶲输入(给水)36.2636.12
总㶲输入100.38100.00
㶲输出(空气)0.11
㶲输出(电力)48.5648.38
㶲输出(给水)35.9535.82
总㶲输出84.6284.30
压缩空气储能子系统㶲效率/%67.67
㶲效率/%84.30
㶲损率/%15.70
), ArticleFig(id=1236323820336902524, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表9, caption=

压缩空气储能系统㶲分析

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项目㶲或㶲损失/(MW·h)占比/%
㶲输入(电力)64.0063.76
㶲输入(空气)0.120.12
㶲输入(给水)36.2636.12
总㶲输入100.38100.00
㶲输出(空气)0.11
㶲输出(电力)48.5648.38
㶲输出(给水)35.9535.82
总㶲输出84.6284.30
压缩空气储能子系统㶲效率/%67.67
㶲效率/%84.30
㶲损率/%15.70
), ArticleFig(id=1236323820450148734, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.10, caption=

Basic data of economic analysis

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项目数值
压缩空气储能系统生命周期建设周期/a2
运行年限/a25
年运行时间/d300
贴现率/%12
年度设备运行维护费用占比/%6
储能过程电价(09:00—17:00)/(元·(kW·h)–1)0.314
释能过程电价(07:00—09:00,18:00—24:00)/(元·(kW·h)–1)0.895
), ArticleFig(id=1236323820567589253, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表10, caption=

经济性分析基本数据

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项目数值
压缩空气储能系统生命周期建设周期/a2
运行年限/a25
年运行时间/d300
贴现率/%12
年度设备运行维护费用占比/%6
储能过程电价(09:00—17:00)/(元·(kW·h)–1)0.314
释能过程电价(07:00—09:00,18:00—24:00)/(元·(kW·h)–1)0.895
), ArticleFig(id=1236323820680835464, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=EN, label=Tab.11, caption=

Economical analysis results of the compressed air energy storage system

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项目数值
耦合系统非耦合系统
总投资成本/万元1 751.161 718.95
年运行维护成本/万元105.07103.13
年度燃料成本/万元0589.47
年总成本/万元105.07692.60
年总收入/万元1 029.911 081.30
年总利润/万元924.84388.70
动态回收周期/a3.108.69
净现值/万元4 134.061 450.63
), ArticleFig(id=1236323820785693069, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236323805073830710, language=CN, label=表11, caption=

压缩空气储能系统经济性分析结果

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
耦合系统非耦合系统
总投资成本/万元1 751.161 718.95
年运行维护成本/万元105.07103.13
年度燃料成本/万元0589.47
年总成本/万元105.07692.60
年总收入/万元1 029.911 081.30
年总利润/万元924.84388.70
动态回收周期/a3.108.69
净现值/万元4 134.061 450.63
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压缩空气储能辅助太阳能电站调峰性能分析
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葛士宇 1 , 史汪洋 2 , 徐钢 1 , 薛小军 2
热力发电 | 储能与发电耦合技术研究专题 2025,54(9): 35-45
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热力发电 | 储能与发电耦合技术研究专题 2025, 54(9): 35-45
压缩空气储能辅助太阳能电站调峰性能分析
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葛士宇1 , 史汪洋2, 徐钢1, 薛小军2
作者信息
  • 1.华北电力大学热电生产过程污染物监测与控制北京市重点实验室,北京 102206
  • 2.山西大学电力与建筑学院,山西 太原 030006
  • 葛士宇(2001),男,汉,硕士研究生,主要研究方向为压缩空气储能系统集成技术,

通讯作者:

薛小军(1992),男,副研究员,博士,主要研究方向为火储调峰、压缩空气储能以及熔盐储热技术,
Performance analysis of peak load balancing for compressed air energy storage assisted solar power station
Shiyu GE1 , Wangyang SHI2, Gang XU1, Xiaojun XUE2
Affiliations
  • 1.Beijing Key Laboratory of Pollutant Monitoring and Control in Thermoelectric Power Production, North China Electric Power University, Beijing 102206, China
  • 2.School of Electric Power and Architecture, Shanxi University, Taiyuan 030006, China
出版时间: 2025-09-25 doi: 10.19666/j.rlfd.202411235
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为有效提高太阳能发电的能源利用率和运行灵活性,提出了一种耦合太阳能光伏、太阳能光热、压缩空气储能的集成系统。在白天,压缩空气储能系统将光伏弃电存储,并将压缩热传递至光热电站;在晚上,压缩空气储能系统释放空气,并采用光热电站的给水加热,从而提高机组的发电负荷。基于系统模拟,对耦合方案进行了热力学分析和经济性分析。分析结果表明:耦合系统光热整体发电效率为41.24%,整体㶲效率为66.79%;压缩空气储能系统往返效率为72.14%,压缩空气系统㶲效率为84.30%,均有显著提高;耦合系统白天的调峰深度为7.02%,晚上调峰深度为19.69%;此外,耦合方案的动态回收周期为3.10年,净现值为4 135.06万元,与非耦合系统相比,均表现良好。

太阳能发电  /  压缩空气储能  /  系统耦合  /  性能分析

In order to effectively improve the energy efficiency and operational flexibility of solar power generation, an integrated system coupling solar photovoltaic, solar thermal and compressed air energy storage is proposed. During the day, the compressed air energy storage system will store the photovoltaic abandoned power, and transfer the compression heat to the photothermal power station. At night, the compressed air energy storage system releases air and uses water supply of the photothermal power station to heat up, thereby increasing the power generation load of the unit. Based on the system simulation, the coupling scheme is analyzed thermodynamically and economically. The overall generation efficiency of the coupled system is 41.24%, while the overall exergy efficiency is 66.79%. The round-trip efficiency of the compressed air energy storage system is 72.14%, while the exergy efficiency of the compressed air system is 84.30%, both of which have increased significantly. The peaking depth of the coupled system is 7.02% in the daytime and 19.69% in the evening. In addition, the dynamic recovery cycle of the coupling scheme is 3.10 years, and the net present value is 41.350 6 million yuan.

solar power generation  /  compressed air energy storage  /  system coupling  /  performance analysis
葛士宇, 史汪洋, 徐钢, 薛小军. 压缩空气储能辅助太阳能电站调峰性能分析. 热力发电, 2025 , 54 (9) : 35 -45 . DOI: 10.19666/j.rlfd.202411235
Shiyu GE, Wangyang SHI, Gang XU, Xiaojun XUE. Performance analysis of peak load balancing for compressed air energy storage assisted solar power station[J]. Thermal Power Generation, 2025 , 54 (9) : 35 -45 . DOI: 10.19666/j.rlfd.202411235
随着全球电能需求的不断增长以及化石能源消耗造成的负面效应,近年来新能源发电在世界各地取得了快速发展。其中,以光伏发电(photovoltaic,PV)和光热发电(concentrated solar power,CSP)为代表的太阳能发电技术成为了推动能源结构转型的关键技术之一[1]。光伏发电以其成本低、适用范围广得到广泛应用;而光热电站则利用太阳能聚光发电的高效性,在提供稳定能源方面展现出巨大潜力。目前,太阳能光伏发电多采用太阳能光伏板直接将太阳能转化为电能;而太阳能光热发电则通过太阳能集热系统、熔盐系统、汽水发电系统将太阳能转化为热能、机械能、电能[2]。然而,光伏电站和光热电站的电能输出会随外部环境及昼夜变化而波动,接入电网会给电网安全稳定运行带来巨大挑战,从而导致一定量的光伏弃电[3]。此外,随着电网用电负荷的不断波动,光伏发电也存在运行灵活性较差的问题。
储能技术可以在用电需求低时段存储电能,在用电高峰时段释放电能。因此,储能系统可以很好地平抑太阳能发电的波动性和不连续性,同时有效提高发电系统的运行灵活性。在各种储能技术中,压缩空气储能(compressed air energy storage,CAES)因其储能容量大、建设成本低且寿命长等优点,近年来受到越来越多的关注[4]。CAES系统通过压气机在储能时段压缩空气,在释能时段则利用压缩空气在透平中膨胀,带动发电机发电[5]。由于空气压缩和膨胀都会伴随着热能的转移,导致CAES系统运行效率低。
为有效提高太阳能发电系统的运行灵活性,同时提高压缩空气储能系统的效率,许多学者针对两者之间的耦合做了大量研究。崔杨等[6]提出一种先进绝热压缩系统,合理配置容量后,在有效解决弃光的前提下,最大程度对燃煤机组进行清洁代替。Yan等人[7]提出了火电机组与CAES耦合的11种方案,从中确定了最优耦合方案,并通过算法对关键参数进行了优化,结果表明,耦合CAES储能阶段可以协助火电机组吸收可再生能源,能量释放阶段可以缓解其高负荷需求的压力,有效提高其灵活性。张文成[8]针对传统压缩空气储能系统能量效率较低的问题,提出新型太阳能辅热式变压比CAES系统,有效提高系统储能效率和太阳能利用效率。为了同时产生电力、冷却能力和热水,以减少峰值能源需求,Kandezi M S等[9]引入了一种基于CAES与聚光太阳能和吸收式冷水机组相结合的新型绿色有效概念,作为一种新的混合动力。张斌[10]构建了集成恒压压缩空气储能的新型燃气轮机系统,提高了负荷的运行效率。以上研究表明,国内外学者已经对压缩空气储能与燃煤电厂的热集成进行了广泛研究。然而,将压缩空气储能与光热熔盐电厂相结合的研究却不多。
有鉴于此,本文提出了一种基于太阳能光热电站及太阳能光伏弃电驱动的CAES系统,通过将光热电站的热能、光伏发电的弃电以及CAES系统的热能耦合集成,提升了耦合系统的效率和经济性;并基于EBSILON Professional软件平台搭建系统仿真模型,通过优选最佳方案,对耦合系统进行了热力学分析与经济性分析。本研究可为提高太阳能发电系统运行灵活性提供理论支持和参考依据。
本文以某太阳能发电站为研究对象,包含50 MW光伏发电系统和50 MW光热发电系统。光伏发电系统由大量光伏板组成,直接将太阳能转化为电能。光热发电系统主要由定日镜场、塔式集热器、熔盐系统、蒸汽循环发电系统组成。假设运行期内每天的光照强度均可维持稳定发电[11]。熔盐系统中,采用太阳盐作为换热介质。此外,采用4个不同功能的换热器,将高温熔盐的热能传递至蒸汽循环发电系统中实现发电。蒸汽循环发电系统主要包含高压缸、中压缸、低压缸以及由6个加热器组成的回热系统。白天光照充分时,储热罐边充边放,机组满负荷发电;晚上无光照时,机组利用熔盐在白天存储多余的热能,可以达到白天50%的发电功率[12]
图1给出了案例光热机组结构示意,表1[13]表2[14]给出了案例光热机组及其回热系统在白天和晚上的运行参数。太阳能发电站中接收器存在1 MPa的压降,因为熔盐经过接收塔时,有一段上升抬举的过程,故而存在压降[13]。从表2看出,回热系统采用汽轮机抽汽将凝结水和给水从45.9 ℃加热到237.8 ℃,因此可以考虑采用不同温度的凝结水或给水来对压缩空气储能系统的空气进行冷却或加热,从而实现能量梯级利用。
图2为压缩空气储能系统示意。利用导热油来冷却压缩后的空气以及加热膨胀后的空气以完成循环。压缩过程由1台电动机、4台换热器、4台压缩机和1个储冷罐组成。压缩过程中常温、常压(25.0 ℃、1.01×105 Pa)状态下进入的空气经由一系列多级绝热压缩、级间冷却后最终以47.0 ℃、10.00 MPa状态进入温度为25.0 ℃的储罐。膨胀过程由1个储热罐、4台换热器、4台膨胀机、1台发电机以及1个节流阀等设备组成。膨胀过程中,压缩空气排出储气罐,进行膨胀做功,膨胀阶段常温、加压空气经由一系列多级绝热膨胀、级间加热完成做功,最终回归常温、常压状态排入大气。表3[15]为压缩空气储能的主要运行参数。其中,储气罐存储压力为10 MPa,储气罐出口压力为7 MPa,储气罐存储温度为25.0 ℃,储气罐容积为15 000 m3,储能时间为8 h,释能时间为4 h。压缩空气系统释能阶段的节流阀存在3 MPa的压降,这是为了便于模拟,需取释放过程中的1个中间压力进行恒压释放[15]
为了有效提高耦合系统的能量利用率和运行灵活性,基于能量梯级利用原理,本文设计集成了光热、光伏发电与压缩空气储能系统,其光热机组耦合压缩空气储能系统示意如图3所示。储能过程中,以光热机组的凝结水作为冷却介质,通过1—4号换热器分别来冷却经压缩的空气,被加热的凝结水混合进入除氧器,从而为机组送去压缩热,而后,压缩空气以高压状态进入储气罐中。该阶段电能转化为压缩空气能量,节省了加热给水的抽汽,有助于增加光热电站的发电功率。
释能过程中,高压空气从储气罐释放,由光热机组出口给水及除氧器出口给水作为加热介质梯级加热。首先,除氧器出口给水通过5、7、9号换热器进行一次加热,冷却后的给水通入4号高温加热器出口给水;之后,整个机组出口给水通过6、8、10号换热器进行二次加热,将冷却水通入除氧器出口给水汇入机组;最后,将压缩空气储能系统发电机产生的电能输送到电网,完成整个储能和释能过程。
为保证压缩空气储能稳定运行,假设所用的光伏弃电在整个运行周期内保持稳定供应。与压缩空气储能子系统不同,根据耦合系统的质量平衡,分别选用储能时间和释能时间为8 h。通过与光热电站耦合,有效提高压缩空气储能系统的往返效率以及耦合系统的光热整体发电效率,并通过调峰提高太阳能发电系统的运行灵活性,同时省去了传统压缩空气储能系统蓄热设备,大大降低了设备投资,提高了系统的经济性和运行可靠性。
EBSILON Professional软件在模拟热力学系统时具有较高的精准度以及普适性,因而广泛用于发电行业的各类热力系统建模。本文涉及热力机组的搭建,故选择此软件对所研究的系统进行模拟仿真。为了更方便地评估耦合系统的各项性能指标,提出以下假设[16]
1)储、释能熔盐循环量保持不变;
2)锅炉效率保持不变;
3)环境温度和压力分别为25.0 ℃和0.10 MPa;
4)忽略辅助系统及周围环境对系统的影响;
5)忽略流体在热交换器中流动时的传热损失和流体动能、势能的变化;
6)管道压降忽略不计。
表4模型验证结果显示:光热机组熔盐循环量和系统效率等参数的设计值与模拟值误差在2%之内;压缩空气储能系统的输入、输出功率及循环效率设计值与模拟值误差在1%以内。
基于热力学第一定律对耦合系统进行了能量分析。在分析过程中通过控制太阳能输入量保持不变,研究耦合系统输出功率的变化。采用耦合系统光热整体发电效率和压缩空气储能系统往返效率作为评价指标。
耦合系统的光热整体发电效率可用于描述系统的能量利用水平,表示为[17]
η=WoutWin
式中:Wout为耦合系统在1个循环过程中的发电量;Win为耦合系统在1个循环过程中的能量输入量。
压缩空气储能的系统往返效率(RTE)指该系统总输出能量与总输入能量的比值,表示为[17]
RTE=ECAES-out/ECAES-in×100%
ECAES-out=(Pout-chPout-ori)×tch+(Pout-dischPout-ori)×tdisch
ECAES-in=Pin-ch×tch
式中:ECAES-out为1个循环过程中系统输出的电能,MW·h;ECAES-in为1个循环过程中系统输入的光伏弃电量,MW·h;Pout-ch为耦合系统在充电过程中的输出功率,MW;Pout-disch为耦合系统在放电过程中的输出功率,MW;Pout-ori为原有光热机组的输出功率,MW;Pin-ch为系统在充电过程中的输入功率,MW;tch为充电时间,h;tdis,ch为放电时间,h。
采用调峰容量和调峰深度对耦合系统的调峰能力进行评估。系统调峰容量指耦合系统的电能输出功率与案例太阳能发电厂的电能输出功率的差值。具体而言,储能过程和释能过程中耦合系统的调峰容量计算公式分别如下[18]
Ptf,cha=P0Pch
Ptf,disch=PdischP0
式中:Ptf,chaPtf,disch分别为储、释能过程中耦合系统调峰容量,MW;P0为案例光热发电机组系统电负荷,MW;PchPdisch分别为储能、释能过程中耦合系统总电负荷,MW。
此外,耦合系统的调峰深度指各阶段调峰容量与案例光热发电机组电能输出功率的比值。储能过程和释能过程计算公式为[18]
ξch=Ptf,chP0×100%
ξdisch=Ptf,dischP0×100%
式中:ξchξdisch分别为储、释能过程中耦合系统调峰深度。
基于热力学第二定律,对压缩空气储能子系统进行㶲分析。在压缩空气储能系统的储、释能过程中,系统输出㶲与输入㶲之比定义为其㶲效率,表示为[19]
ηex,CAES=eout,CAESein,CAES
压缩空气储能系统中各工质(水、空气、蒸汽)的㶲em可表示为[19]
em=qm[(hh0)T0(ss0)]
式中:qm为工质的质量流量,kg/s;h为工质当前的焓,kJ/kg;h0为工质在环境状态下的焓,kJ/kg;T0为工质在环境状态下的温度,K;s为工质在当前状态下的熵,kJ/(kg·K);s0为工质在环境状态下的熵,kJ/(kg·K)。
对于系统整体㶲效率而言,我们需要考虑熔盐的㶲计算[20]
Exms=mmscp,ms(TmsT0T0LnTmsT0)
式中:mms为熔盐的质量流量,kg/s;cp,ms为熔盐的定压比热容,kJ/(kg K);Tms为熔盐的温度,K。
对此,本文提出一种整体㶲效率的计算公式[20]
储能阶段为:
ηex=ed+eout,airein,airein,mseout,ms
释能阶段为:
ηex=edein,mseout,ms+ein,aireout,air
式中:ein,mseout,ms分别为熔盐的入口及出口㶲,MW;ein,aireout,air分别为空气的入口㶲和出口㶲,MW;ed为系统的发电量,MW。
本文在已有光热和光伏发电机组的基础上新增了压缩空气储能系统。白天利用一部分光伏弃电将常温、常压空气压缩以存储电能;夜晚释放压缩空气,并与光热机组集成,共同发电。通过储能阶段汽轮机组多余发电,以及释能过程用电高峰时的售电,达到项目的盈利。本文采用动态投资回收期H和净现值N2个指标,分析耦合系统的经济性能。
动态投资回收期指初始资本投资与财务回报相平衡的时间点,动态投资回收期缩短,反映了项目盈利能力增强,表示为[21]
y=1HIO(1+idis)y=0
H=t+1+|(IO)t+1|(IO)t+
式中:IO分别为第y年的现金流入和现金流出,元;t+为假设系统各年度累计净现金流量第一次为正或为零的年份;idis为贴现率,%。
净现值指耦合系统在整个寿命期间(n年)累积的净现金流量。净现值增多,反映了项目盈利能力增强。N可表示为[22]
N=y=1nIO(1+idis)y
表5给出了压缩空气储能系统主要设备的投资成本估算方法[23-24]
表6给出了耦合系统和案例太阳能发电站以及案例压缩空气储能系统(统称为原系统)的能量分析结果。保证系统耦合前后,熔盐储热系统的熔盐循环量不变,因此随着CAES系统的加入,耦合系统的电能输出会发生变化。对于原系统,白天净发电功率为45.30 MW,晚上机组净发电功率为22.65 MW;对于耦合系统,白天净发电功率为46.61 MW,晚上净发电功率为光热机组净发电功率与CAES输出功率之和。
为了进一步分析各系统间能量转移的情况,图4给出了耦合系统的能流图。由图4可以看出,在耦合系统储能和释能过程中,保持机组在白天和晚上时段熔盐的输入能量恒定。光热整体净发电效率为41.24%,相比单一光热发电机组提高2.64百分点;压缩空气储能系统的往返效率为72.14%,比案例CAES机组效率提高7.79百分点。储能过程中,压缩空气为光热机组的给水系统提供了7.532 MW的热量,从而使机组净发电量提高了1.31 MW。释能过程中,给水系统向压缩空气储能系统输送6.12 MW的热量,使得压缩空气储能系统输出6.07 MW的电量,系统总输出电量提高了4.46 MW。在1个循环过程内,压缩空气储能系统在储能过程中可存储8 MW的光伏弃电,耦合系统在储能及释能过程中共释放5.77 MW(1.31 MW+4.46 MW)的电能。
表7给出了耦合系统的调峰性能分析结果。耦合系统在储能过程中的调峰容量主要包括消纳的光伏弃电以及输入到光热系统的压缩热所引起光热发电机组的发电功率变化值,即白天调峰容量为6.69 MW。耦合系统释能过程的调峰容量主要包括光热机组给水系统输入压缩空气储能的能量所转化的3个透平的发电量以及因机组给外部能量所引起的机组发电功率变化量,即晚上调峰容量4.46 MW。储能过程和释能过程的调峰深度分别为7.02%和19.69%。由此可见,通过耦合储能系统光热机组可以有效提高其调峰性能。
表8为耦合系统整体㶲效率分析。原系统整体㶲效率为63.69%,耦合系统整体㶲效率为66.79%,耦合系统整体㶲效率比原光热机组提升了3.10百分点。
为了揭示压缩空气储能系统的性能提升方向,系统㶲分析结果如表9所示。图5给出了系统内各部件㶲损失结果。从表9可以看出,系统的㶲效率为84.30%,同时系统内存在15.70%的㶲损失。由图5可知,在压缩空气储能系统各部件中,节流阀的㶲损失最大,达到了3.12 MW·h,约占系统总㶲损失的19.80%,这是由于节流阀的压力损失较大。此外,1号压缩机和3号透平的㶲损失也较大。这是由于1号压缩机进口是常温空气,因此在设备效率以及系统设计不变的情况下,相较于其他压缩机而言,换热温差较大,因此㶲损失也较大;而3号透平㶲损失大则是因为其膨胀比相较于其他透平来说大了1倍左右,相当于压力损失增大。
图6为耦合系统改造的设备投资成本。表10[23-25]给出了耦合系统经济性分析的基本数据。储能和释能过程的电价选择甘肃省的分时电价[25]
耦合系统经济性分析结果如表11所示。可以看出,耦合系统的设备总投资为1 751.16万元,新增系统年度设备运行维护费用为105.07万元,年度消耗电能成本作为年度燃料成本忽略不计,因此年总成本为105.07万元。通过发电带来的年收益为1 029.91万元,所以,该系统每年可获得924.84万元的总利润。非耦合系统的动态投资回收期和净现值分别为8.69年和1 450.63万元。计算得出,耦合系统动态投资回收期和净现值分别为3.10年和4 134.06万元。这表明,耦合系统具有较好的经济效益。
本文提出了压缩空气储能系统与光热、光伏发电机组耦合的方案,以消纳光伏弃电,并提高系统的调峰能力和发电效率。采用能量分析、调峰性能分析、㶲分析和经济性分析方法对所提出的系统性能进行了评估,具体研究结论如下。
1)能量分析结果表明,耦合系统的光热整体发电效率为41.24%,相比单一光热发电机组提高2.64百分点;压缩空气储能系统的往返效率为72.14%,比案例CAES机组效率提高7.79%。
2)㶲分析结果表明,压缩空气储能系统的㶲效率为84.30%,其中节流阀为㶲损失最大的部件,是提高该系统㶲效率的关键。此外,分析了耦合系统整体㶲效率,以晚上释能过程为例,从63.69%提升到66.79%,提升了3.10百分点。
3)调峰性能分析表明,在白天光照时段,系统的调峰容量为6.69 MW,调峰深度为7.02%;夜晚无光照时段,系统的调峰容量为4.46 MW,调峰深度为19.69%。
4)对耦合方案进行了经济性分析,得出该系统的设备总投资为1 751.16万元,动态投资回收期和净现值分别为3.10年和4 134.06万元,表明该系统具有较好的经济性能。本文模拟光热电站时未充分考虑到1年中各时间段的太阳辐照强度、季节性变化和天气变化对系统产生的影响,只是假设光热电站出力50 MW稳定发电,并未做实时出力预测及调度。并且,本文的容量配置着重点在于各效率的提升,并未以经济性为目标优化配置。后期这2点均可通过MATLAB软件编程模拟进行实时的热集成分析,以及在此基础上获得更好的容量配置。
  • 国家自然科学基金青年科学基金项目(52406019)
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doi: 10.19666/j.rlfd.202411235
  • 接收时间:2024-11-12
  • 首发时间:2026-03-05
  • 出版时间:2025-09-25
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  • 收稿日期:2024-11-12
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National Natural Science Foundation of China: Youth Science Foundation Project(52406019)
国家自然科学基金青年科学基金项目(52406019)
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    1.华北电力大学热电生产过程污染物监测与控制北京市重点实验室,北京 102206
    2.山西大学电力与建筑学院,山西 太原 030006

通讯作者:

薛小军(1992),男,副研究员,博士,主要研究方向为火储调峰、压缩空气储能以及熔盐储热技术,
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
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