Article(id=1236699939854668205, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236699937195479441, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202404070, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1712851200000, receivedDateStr=2024-04-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772781842435, onlineDateStr=2026-03-06, pubDate=1727193600000, pubDateStr=2024-09-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772781842435, onlineIssueDateStr=2026-03-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772781842435, creator=13701087609, updateTime=1772781842435, updator=13701087609, issue=Issue{id=1236699937195479441, tenantId=1146029695717560320, journalId=1210938733613449225, year='2024', volume='53', issue='9', pageStart='1', pageEnd='154', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772781841801, creator=13701087609, updateTime=1772781841801, updator=13701087609, preIssue=null, nextIssue=null, ext=null, issueFiles=null}, startPage=39, endPage=47, ext={EN=ArticleExt(id=1236699941674996160, articleId=1236699939854668205, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Thermodynamic modeling and performance analysis for micro adiabatic compressed air energy storage system, columnId=1236699940546728377, journalTitle=Thermal Power Generation, columnName=Compressed air energy storage technology, runingTitle=null, highlight=null, articleAbstract=

The arrangement of micro adiabatic compressed air energy storage (A-CAES) system is flexible and suitable for typical distributed energy systems. By accurately modelling a typical device of the miniature A-CAES system based on pneumatic motors, a thermodynamic model that can reflect its system performance is constructed. The experimental bench of the A-CAES system is built, and the average error rate between the simulation model and the experiment is around 5.38%, which verifies the reliability of the model. The round-trip efficiency and comprehensive efficiency of the system are 4.81% and 27.23%, respectively, verifying the necessity of the existence of thermal energy storage devices in the A-CAES system. The effects of compression level and compression ratio on the system performance are analyzed by using this model. The results show that, as the compression level increases, the round-trip efficiency and comprehensive efficiency of the system both increase, and the optimal efficiency of the system can reach 6.10% and 35.81%, respectively. Taking the combination of compression ratios of 2, 3, and 5 as an example, reasonable distribution of compression ratios can improve the round-trip efficiency and overall efficiency of the system by 1.27% and 4.38%, respectively.

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微型绝热压缩空气储能(A-CAES)系统布置灵活,适用于典型分布式能源系统。通过对基于气动马达的微型A-CAES系统典型设备精确建模,构建了能够反映其系统性能的热力学模型。搭建了A-CAES系统的实验台,仿真模型与实验的平均误差在5.38%左右,验证了模型的可靠性。该系统的往返效率与综合效率分别为4.81%、27.23%,验证了热能存储装置在A-CAES系统存在的必要性。利用该模型研究分析了压缩级数、压缩比对系统性能的影响,结果表明:随压缩级数的增加,系统的往返效率和综合效率均随之增加,系统最优效率可分别达到6.10%和35.81%;以2、3和5的压缩比组合为例,其压缩比的合理分布可使得系统往返效率和综合效率分别提高1.27%和4.38%。

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宋永兴(1987),男,博士,副教授,主要研究方向为压缩空气储能,
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张梦洁(2000),女,硕士研究生,主要研究方向为压缩空气储能,

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Parameters of the equipment in laboratory

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设备项目数值
压缩机压缩比4.84/6.20
额定风量/(m3·min–1)0.2
等熵效率/%80
轴功率/kW4
最大供压/MPa3.00
换热器效率/%60
储气罐数量2
最高允许压力/MPa4.00
体积/m31
表面积/m24.82
减压阀1压力范围/MPa0.05~3.50
减压阀2压力范围/MPa0.05~1.00
气动马达最大功率/kW3
最大耗气量/(m3·min–1)3.4
), ArticleFig(id=1236699951263175577, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236699939854668205, language=CN, label=表1, caption=

实验设备参数

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设备项目数值
压缩机压缩比4.84/6.20
额定风量/(m3·min–1)0.2
等熵效率/%80
轴功率/kW4
最大供压/MPa3.00
换热器效率/%60
储气罐数量2
最高允许压力/MPa4.00
体积/m31
表面积/m24.82
减压阀1压力范围/MPa0.05~3.50
减压阀2压力范围/MPa0.05~1.00
气动马达最大功率/kW3
最大耗气量/(m3·min–1)3.4
), ArticleFig(id=1236699951330284445, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236699939854668205, language=EN, label=Tab.2, caption=

Main parameters of the micro A-CAES system

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项目数值
环境温度/K291
环境压力/Pa101 325
压缩机轴功率/kW4
压缩机等熵效率/%80
压缩机多变指数1.3
压缩机压缩比4.84/6.20
压缩机级数2
换热器效率/%60
热载体(水)初始温度/K297.5
储气罐容积/m32
储气罐外表面积/m29.63
储气罐最大允许压力/MPa4
气动马达多变指数1.5
膨胀比2
气动马达机械效率/%51
热力学效率/%60
电效率/%60
气体常数/(J·(kg·K)–1)287
空气定压比热容/(J·(kg·K)–1)1 004
空气定容比热容/(J·(kg·K)–1)717
水的比热容/(J·(kg·K)–1)4 200
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微型A-CAES系统主要参数

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项目数值
环境温度/K291
环境压力/Pa101 325
压缩机轴功率/kW4
压缩机等熵效率/%80
压缩机多变指数1.3
压缩机压缩比4.84/6.20
压缩机级数2
换热器效率/%60
热载体(水)初始温度/K297.5
储气罐容积/m32
储气罐外表面积/m29.63
储气罐最大允许压力/MPa4
气动马达多变指数1.5
膨胀比2
气动马达机械效率/%51
热力学效率/%60
电效率/%60
气体常数/(J·(kg·K)–1)287
空气定压比热容/(J·(kg·K)–1)1 004
空气定容比热容/(J·(kg·K)–1)717
水的比热容/(J·(kg·K)–1)4 200
), ArticleFig(id=1236699951514833830, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236699939854668205, language=EN, label=Tab.3, caption=

Detailed data for experimental model validation

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实验模型误差/%
压缩充电阶段
空气质量流量/(kg·h–1)0.004 00.003 92.56
充电时间/h4.835.003.52
耗电/(kW·h)19.3220.003.52
TES/kJ14 54616 14611.00
膨胀放电阶段
放电时间/h0.330.399.10
放电/(kW·h)0.900.966.67
评价指标
往返效率/%4.664.813.22
综合效率/%25.5727.236.49
), ArticleFig(id=1236699951611302825, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236699939854668205, language=CN, label=表3, caption=

实验模型验证详细数据

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实验模型误差/%
压缩充电阶段
空气质量流量/(kg·h–1)0.004 00.003 92.56
充电时间/h4.835.003.52
耗电/(kW·h)19.3220.003.52
TES/kJ14 54616 14611.00
膨胀放电阶段
放电时间/h0.330.399.10
放电/(kW·h)0.900.966.67
评价指标
往返效率/%4.664.813.22
综合效率/%25.5727.236.49
), ArticleFig(id=1236699951728743342, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236699939854668205, language=EN, label=Tab.4, caption=

Compression ratios corresponding to different compression levels

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压缩级数二级三级四级
压缩比5.48×5.483.11×3.11×3.112.34×2.34×2.34×2.34
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不同压缩级数对应的压缩比

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压缩级数二级三级四级
压缩比5.48×5.483.11×3.11×3.112.34×2.34×2.34×2.34
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微型绝热压缩空气储能系统热力学建模与性能分析
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张梦洁 1 , 刘强 2 , 张彤赫 1 , 宋永兴 1 , 张林华 1
热力发电 | 压缩空气储能技术 2024,53(9): 39-47
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热力发电 | 压缩空气储能技术 2024, 53(9): 39-47
微型绝热压缩空气储能系统热力学建模与性能分析
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张梦洁1 , 刘强2, 张彤赫1, 宋永兴1 , 张林华1
作者信息
  • 1.山东建筑大学热能工程学院,山东 济南 250000
  • 2.济南市特种设备检验研究院,山东 济南 250014
  • 张梦洁(2000),女,硕士研究生,主要研究方向为压缩空气储能,

通讯作者:

宋永兴(1987),男,博士,副教授,主要研究方向为压缩空气储能,
Thermodynamic modeling and performance analysis for micro adiabatic compressed air energy storage system
Mengjie ZHANG1 , Qiang LIU2, Tonghe ZHANG1, Yongxing SONG1 , Linhua ZHANG1
Affiliations
  • 1.School of Thermal Energy Engineering, Shandong Jianzhu University, Jinan 250000, China
  • 2.Jinan Special Equipment Inspection and Research Institute, Jinan 250101, China
出版时间: 2024-09-25 doi: 10.19666/j.rlfd.202404070
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微型绝热压缩空气储能(A-CAES)系统布置灵活,适用于典型分布式能源系统。通过对基于气动马达的微型A-CAES系统典型设备精确建模,构建了能够反映其系统性能的热力学模型。搭建了A-CAES系统的实验台,仿真模型与实验的平均误差在5.38%左右,验证了模型的可靠性。该系统的往返效率与综合效率分别为4.81%、27.23%,验证了热能存储装置在A-CAES系统存在的必要性。利用该模型研究分析了压缩级数、压缩比对系统性能的影响,结果表明:随压缩级数的增加,系统的往返效率和综合效率均随之增加,系统最优效率可分别达到6.10%和35.81%;以2、3和5的压缩比组合为例,其压缩比的合理分布可使得系统往返效率和综合效率分别提高1.27%和4.38%。

微型  /  绝热压缩空气储能  /  热力学建模  /  效率

The arrangement of micro adiabatic compressed air energy storage (A-CAES) system is flexible and suitable for typical distributed energy systems. By accurately modelling a typical device of the miniature A-CAES system based on pneumatic motors, a thermodynamic model that can reflect its system performance is constructed. The experimental bench of the A-CAES system is built, and the average error rate between the simulation model and the experiment is around 5.38%, which verifies the reliability of the model. The round-trip efficiency and comprehensive efficiency of the system are 4.81% and 27.23%, respectively, verifying the necessity of the existence of thermal energy storage devices in the A-CAES system. The effects of compression level and compression ratio on the system performance are analyzed by using this model. The results show that, as the compression level increases, the round-trip efficiency and comprehensive efficiency of the system both increase, and the optimal efficiency of the system can reach 6.10% and 35.81%, respectively. Taking the combination of compression ratios of 2, 3, and 5 as an example, reasonable distribution of compression ratios can improve the round-trip efficiency and overall efficiency of the system by 1.27% and 4.38%, respectively.

micro  /  adiabatic compressed air energy storage  /  thermodynamic modeling  /  efficiency
张梦洁, 刘强, 张彤赫, 宋永兴, 张林华. 微型绝热压缩空气储能系统热力学建模与性能分析. 热力发电, 2024 , 53 (9) : 39 -47 . DOI: 10.19666/j.rlfd.202404070
Mengjie ZHANG, Qiang LIU, Tonghe ZHANG, Yongxing SONG, Linhua ZHANG. Thermodynamic modeling and performance analysis for micro adiabatic compressed air energy storage system[J]. Thermal Power Generation, 2024 , 53 (9) : 39 -47 . DOI: 10.19666/j.rlfd.202404070
随着经济的持续增长,化石燃料日益枯竭[1],加大了对风能、太阳能等可再生能源的需求[2]。可再生能源不稳定性和不可预测性的固有缺陷造成了其利用率低下[3]。因此,亟需一种高效环保的储能技术。压缩空气储能(CAES)系统将电力供应与可再生能源相结合,可以有效缓解上述问题[4]。该系统具有高可靠性、低环境影响性和经济可行性等优点,是一种很有前景的储能技术[5-6]
根据能量转化的理想化过程,CAES可分为非绝热压缩空气储能(D-CAES)和绝热压缩空气储能(A-CAES)[7]。D-CAES在膨胀阶段需要借助燃料辅助燃烧以提高进入膨胀机的空气温度[8],必然会对环境造成影响,违背了CAES的设计初衷[9]。而A-CAES利用压缩阶段的热量对膨胀阶段的空气进行预热,对环境影响小,具有很高的研究价值[10-11]。近年来,也提出了一些新型的CAES系统,Li等人[12]提出了一种将有机郎肯循环、太阳能集热器与CAES系统耦合的新型系统,该系统的电效率、能源效率和㶲效率可达到24.63%、86.96%和67.57%。文献[13]研究分析了CAES系统与多效蒸馏和多级闪蒸的新型系统,该系统提高CAES系统性能的同时,产生了大量饮用水,同时分析了系统回热器的经济性,并得到集成系统的回收期为5.1年的结论。Yao等人[14]提出了将CAES与化学储能耦合的集成系统,该系统中压缩机和燃气轮机的运行参数对热力和经济性能指标有显著影响,系统往返效率可达42.96%。
尽管研究了很多新型系统,但世界上仅有2座CAES工厂投入商用,第一座于1978年在德国Huntorf建成,第二座坐落于美国Mackintosh[15]。上述工厂均需要大容量洞穴进行储存,属于大规模系统,受地理条件的限制,缺乏灵活性[16]。借助钢性容器的微型CAES系统更加适用于分布式能源的使用,具有布置灵活、成本低、对环境污染小的优点,引起了学者的广泛关注[17]。Guo等人[18]研究分析了适用于微型CAES系统的金属压力容器,对储气罐和管道进行了结构优化,得到型号D-400的设备换热更充分,在设计工作压力范围内储气装置的有效容量更大。Jannelli等人[19]提出了一种适用于独立小型可再生能源电站的CAES系统,阐述了该系统的选型方法,并对综合性能进行了计算,得到其存储效率为57%。Roberto等人[20]对微型CAES系统建立数学模型,并对其进行了热力学和能量分析。得出准等温压缩和膨胀对系统效率的影响是积极的。Yao等人[21]对微型CAES的冷热电三联产系统进行分析,研究表明换热器效率、膨胀机进口温度和压力对系统的热力性能影响较大,并且㶲效率可以提高到51%。Mucci等人[22]对微型CAES系统建模的动态策略进行仿真和比较,确定了最合适的控制策略,以提高非设计条件下的性能。
目前,试验台阶段的微型A-CAES系统研究尚少,且仅有的小型实验台未对压缩阶段的热量进行回收。微型系统需借助小型钢性容器对高压空气进行储存,与采用洞穴等大型空间储存的方式对系统的影响必然会存在差异,因此,微型系统的实验研究对理论的验证和优化是必要的。本研究搭建了基于气动马达的实验台,并通过设置热能存储装置的方式,将压缩阶段的热量作为制热量储存起来,实现了热量的回收。为了克服地理条件对A-CAES系统的限制,本文对微型A-CAES系统展开研究,旨在建立可靠的微型A-CAES系统热力学模型。设计并搭建了A-CAES实验台,由于缺乏CAES适配性较高的小型膨胀机,实验台利用气动马达作为膨胀设备。首先构建了微型A-CAES系统的热力学模型,通过实验验证模型的典型部件,并对模型进行精确连接,形成能够反映性能的系统图像;然后研究分析系统压缩机的典型参数对系统性能的影响;最后对结果进行讨论分析并总结。
A-CAES系统主要由压缩机、换热器、水箱、储气罐、调压阀及气动马达组成。其中水箱作为热能存储装置(TES),储存压缩阶段的热量。系统原理如图1所示,主要包括压缩充电、储存和膨胀放电3个阶段。环境中的空气进入压缩机,压缩至高压状态,储存在储气罐中。TES借助换热器将压缩阶段的热量进行储存。在用电高峰或可再生能源匮乏时,储气罐中的空气经过调压阀调节至合适压力后进入气动马达,驱动发电机发电,释放电能。
对A-CAES系统典型设备建立热力学模型,并将典型设备进行整体连接,描述各部分的能量转换和传热过程。为了简化模型,基于热力学第一定律和第二定律,对A-CAES系统的能量平衡做出下述假设:
1)空气是理想气体;
2)所有设备和管道进出口中的压力和热量损失均忽略不计;
3)系统所有组件均在稳定状态下运行;
4)每台换热器中水的质量流量和温度均恒定;
5)势能和动能忽略不计。
活塞式压缩机适用压力范围广,不论流量大小,均能达到所需的压力[23],因此本系统采用活塞式压缩机。压缩机运行过程中的热量损耗,可由多变指数nc表述。i级压缩机的出口温度可定义为:
Tc,i,out=Tc,i,inβc,inc1nc
式中:Tc,i,outTc,i,in压缩机的进出口空气温度,K;βc,ii级压缩机的压缩比。
βc,i=pc,i,outpc,i,in
式中:pc,i,inpc,i,outi级压缩机进出口空气压力,Pa。
假设压缩过程为等熵过程,等熵效率为ηci级压缩机的实际功耗W˙c,i为:
W˙c,i=w˙c,iηc
式中:W˙c,ii级压缩机的实际功耗,W。
w˙c,i=m˙cncnc1RgTc,i,inβc,inc1nc
式中:w˙c,ii级压缩机的理想功耗,W;m˙c为流入i级压缩机的空气质量流量,kg/s;Rg为气体常数,J/(kg·K)。
压缩阶段的总功耗为:
w˙c=i=1NW˙c,i=m˙cncnc1Rgi=1Nβc,inc1ncηc
式中:N为压缩级数。
由于电力是恒定的,因假定该系统电力是输入参数,质量流量为输出参数,有:
m˙c=W˙cηcncnc1Rgi=1NTc,i,inβc,inc1nc
换热器的效率ε可定义为:
ε=cp1m˙1(Tin1Tout1)(cpm˙1)min(Tin1Tin2)=cp2m˙2(Tout1Tin1)(cpm˙2)min(Tin1Tin2)
式中:cp为定压比热容,J/(kg·K);m˙为换热器处载体的质量流量,kg/s;TinTout为换热器进出口载体温度,K;下标1、2分别代表空气和水;(cpm˙)min为冷热流体之间的最小定压比热容。
(cpm˙)min=min[(cpm˙)1,(cpm˙)2]
模型选用逆流式换热器,需采用传热单元数法(ε-NTU法),则效率可定义为[24]
ε=1exp[NTU(1CminCmax)]1CminCmaxexp[NTU(1CminCmax)]
式中:CcpmCminCmax分别为最小、最大的定压比热容;NTU为传热单元数,代表无量纲数。
NTU=kACmin
式中:k为单位总传热系数,W/(m2·K);A为有效换热面积,m2
利用上述换热器效率,可计算i级换热器出口空气和水的温度:
Tc,i+1,in=(1ε)Tc,i+1,out+εTw,i,in
Tw,i,out=εTc,i,out+(1ε)Tw,i,in
式中:Tc,i+1,in为第i级换热器的出口空气温度,同时也是第i+1级压缩机的入口空气温度,K;Tw,i,out为第i级换热器出口水的温度,假设冷水是无限量的,即流入换热器中水的温度都是相同的。
mcw,i为流入i级换热器中水的质量流量,kg/s,可通过下式计算:
m˙cw,i=cpm˙c(Tc,i,outTc,i+1,in)cw(Tcw,i,inTcw,out)
式中:cw为水的比热容,J/(kg·K)。换热器中水的总质量流量为:
M˙cw=i=1Ncm˙cw,i
式中:Nc为压缩级数。
Q˙c,ii级换热器中,空气向水传递的热量,J。可定义为:
Q˙c,i=(cpm˙)air(Tc,i,outTc,i+1,in)     =(cpm˙)water(Tw,i,inTw,i,out)
压缩阶段空气向水传递的总热量为:
Q˙c=i=1NcQ˙c,i
储气罐的最高允许压力为pmax,为了简化计算,假定最后一级压缩机的出口压力(储气罐的入口压力)也为pmax。则压缩充电过程的时间可通过下式计算:
tc=Vptankρpenvm˙c
式中:tc为压缩阶段运行时间,s;V为储气罐的总体积,m3ρ为环境空气密度,kg/m3penv为环境压强,Pa。
假定储气罐为恒定容积,则储气罐的质量守恒方程为:
dmdt=m˙inm˙out
式中:m为储气罐内的空气质量,kg;t为时间,s;minmout分别为流入、流出储气罐的空气质量流量,kg/s。
由热力学第一定律,储气罐内的能量守恒方程为:
d(mu)dt=m˙inhinm˙outhoutUA(TTenv)
式中:u为空气的比内能,J;h为空气的比焓,J;U为储气罐与环境之间的换热系数,J/(kg·K);A为储气罐的表面积,m2T为储气罐内温度,K;Tenv为环境温度,K。
引入理想气体状态方程:
dVV+dppdTTdmm=0
式中:p为储气罐内的压力,Pa。
可得储气罐内温度和压力的变化:
dTdt=1m(m˙incpcvTin+[(1cpcv)m˙outm˙in]T)        UAVVmcv(TTenv)
dpdt=RgVcpcv(m˙inTimtankm˙outT)       UAVRgcv(TTenv)
式中:cv为空气的定容比热容,J/(kg·K)。
系统为微型系统,借助储气罐储存高压空气。罐内的热力性能会影响储气罐与环境之间的换热系数,故该换热系数不是一个常数,可定义为[25]
U=U0+α×(PPenv)+τ|m˙inm˙out|
式中:U0p=penv、空气质量流量为0时的传热系数;α为压力在对流换热过程中的影响系数,可取值0.115 4 W/(m2·K·MPa);τ为质量流量在对流换热过程中的影响系数,压缩阶段可取3 800 W/(m2·K·MPa),膨胀阶段可取5 000 W/(m2·K·MPa)。
气动马达是将空气的压缩势能转化为旋转机械能的装置,具有体积小、操作灵活等特点,可以很好的适配微型系统。在膨胀发电过程中,高压空气经过调压阀降低压力后进入气动马达。其理想状态下的输出功可由下式计算:
W˙e,i=m˙enene1RgTe,in(1βe1nene)
式中:W˙e,i为气动马达理想输出功,W;m˙e为进入气动马达的空气质量流量,kg/s;ne为气动马达的多变指数;Te,in为进入气动马达的空气温度,K;βe为气动马达的膨胀比。
βe=pe,inpe,out
式中:pe,inpe,out分别为气动马达的进出口空气压力,Pa。
受机械效率ηm和热效率ηth的影响,气动马达的实际输出功与理想输出功存在误差,实际输出功率W˙e可由下式计算:
W˙e=W˙e,iηmηth
当电效率为ηel时,气动马达输出电功率为:
W˙el=W˙eηel
气动马达的出口空气温度Te,out可以定义为:
Te,out=Te,inβe1nene
膨胀放电时间可由下式计算得到:
te=m˙ctcm˙e
1)往返效率为:
η=E˙outE˙in
式中:E˙out为输出电能,可定义为式(31);E˙in为输入电能,可定义为式(32)。
E˙out=W˙ctc
E˙in=W˙elte
2)综合效率为:
COP=Q˙c+E˙outE˙in
实验台选用二级活塞式压缩机,具体如图2所示,级间通过逆流板式换热器进行热量交换。为确保气动马达入口气压的相对稳定,设置高压和低压2台减压阀。碘钨灯作为末端设备,通过功率表测量其耗电量。设置温度传感器、压力传感器及电子流量计监测空气的热力性质。设备详细参数如表1
实验台的仪表设置如图3所示。经过4.83 h将2 m3的储气罐完全充满至3 MPa。
在压缩充电过程中,每20 min记录一次仪表的温度和压力,监测点的压力变化如图4a)所示。图4a)中P1与P2、P3与P4的压力变化基本重合,故空气在换热器处的压力损失可忽略不计。P1、P2最终稳定在0.477附近,与压缩机一级压缩比0.484基本吻合。P3、P4以及储气罐内的压力变化Pt1、Pt2呈线性一直增加到储气罐的储存压力3 MPa。温度变化如图4b)所示,一级压缩和二级压缩出口空气经过换热器后,温度均有不同程度的降低,此时压缩阶段的部分热量被水箱储存。
气动马达的入口空气性质如图5所示,通过实时调节减压阀,将气动马达进气压力控制在0.5 MPa附近,此时气动马达进气空气温度从290 K快速降低后稳定在280 K附近。
热力学模型的详细参数见表2
实验与模型结果以及误差率见表3。其中实验与模型的误差率R为:
R=|EM|min[E,M]
式中:E为实验结果数据;M为模型结果数据。
储气罐中实验及模拟状态下的压力变化如图6所示,其压力增长趋势及增长量基本一致,误差率为2.33%。模型与实验的平均误差为5.38%,可以验证模型的准确性。
压缩机作为系统的主要设备,运行工况对系统热力学性能起着关键作用,其典型参数压缩级数和压缩比的影响至关重要。
假定膨胀过程中空气性质保持不变,因此气动马达的输出功和效率恒定。设定总压缩比不变,不同压缩级数对应的压缩比见表4。压缩级数对系统性能的影响如图7所示。
图7a)可知,当压缩级数增加时,制热量也趋于提高。压缩级数由2级依次提高至4级时,制热量分别增加630、362 kJ。每级压缩后均设置换热器,换热次数随压缩级数的增加而增加,导致换热更加彻底,且制热量随压缩级数的增加其增加量趋于平缓。由于压缩级数增加,固定电力的输入时间减少,致使充电时间与放电时间的比值降低。压缩级数对系统往返效率和综合效率的影响如图7b)所示,压缩比从2级增加到4级时,往返效率和综合效率分别增加了1.91%、13.22%,并且各效率的增加幅度逐渐降低。
以三级压缩为例,分析相同压缩比、不同压缩比分布对系统性能的影响。压缩比分别设置为2、3和5,对其进行6种不同的排列。压缩比分布对系统性能的影响如图8所示。
图8a)可知,较小压缩比设置的位置越靠后,充电时间与放电时间的比值越大。当压缩比分布从2×3×5改为5×3×2时,制热量从15 655 kJ增加到17 708 kJ,即将较小压缩比设置在越靠后的位置,制热量越高。压缩比分布对往返效率和综合效率的影响如图8b)所示,当压缩比分布设置从2×3×5改为5×3×2时,往返效率和综合效率分别下降了1.27%和4.38%。在此过程中获得较高的往返效率和综合效率是通过在靠后的位置设置大压缩比来实现的。
1)本研究构建了A-CAES系统的热力学模型,利用基于气动马达的实验平台对模型进行验证,模型仿真与实验的平均误差率为5.38%,模型的精度具有可靠性。A-CAES系统往返电效率非常低,仅为4.81%,而综合效率达到27.23%,表明了TES设置的必要性及多联产系统的高效性。
2)随着压缩级数的增加,系统往返效率和综合效率均会提高,制热量也会随之增加。当压缩级数从2级增加到4级时,往返效率和综合效率分别提高1.91%、13.22%,制热量增加992 kJ。
3)合理的压缩比分布可以获得较高的往返效率和综合效率。当压缩比分布从5×3×2设置为2×3×5时,往返效率和综合效率分别提高1.27%和4.38%。即将较高的压缩比设置在靠后的位置对系统的效率是积极的。
  • 压缩机技术国家重点实验室(压缩机技术安徽省实验室)开放基金项目(SKL-YSJ202108)
  • 山东省自然科学基金(ZR2021QE157)
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2024年第53卷第9期
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doi: 10.19666/j.rlfd.202404070
  • 接收时间:2024-04-12
  • 首发时间:2026-03-06
  • 出版时间:2024-09-25
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  • 收稿日期:2024-04-12
基金
Open Fund Project of State Key Laboratory of Compressor Technology (Anhui Provincial Laboratory of Compressor Technology)(SKL-YSJ202108)
压缩机技术国家重点实验室(压缩机技术安徽省实验室)开放基金项目(SKL-YSJ202108)
Shandong Provincial Natural Science Foundation(ZR2021QE157)
山东省自然科学基金(ZR2021QE157)
作者信息
    1.山东建筑大学热能工程学院,山东 济南 250000
    2.济南市特种设备检验研究院,山东 济南 250014

通讯作者:

宋永兴(1987),男,博士,副教授,主要研究方向为压缩空气储能,
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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
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红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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