Article(id=1222513216404575024, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222513210519970621, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202305090, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1684944000000, receivedDateStr=2023-05-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1769399463987, onlineDateStr=2026-01-26, pubDate=1700841600000, pubDateStr=2023-11-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1769399463987, onlineIssueDateStr=2026-01-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1769399463987, creator=13701087609, updateTime=1769399463987, updator=13701087609, issue=Issue{id=1222513210519970621, tenantId=1146029695717560320, journalId=1210938733613449225, year='2023', volume='52', issue='11', pageStart='1', pageEnd='198', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1769399462585, creator=13701087609, updateTime=1769405983425, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1222540560984957089, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222513210519970621, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1222540560984957090, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1222513210519970621, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=105, endPage=114, ext={EN=ArticleExt(id=1222513217633506178, articleId=1222513216404575024, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Research on power distribution strategy of DC microgrid system based on hydrogen energy storage, columnId=1213164439017276071, journalTitle=Thermal Power Generation, columnName=Special topic on new energy power generation technology, runingTitle=null, highlight=null, articleAbstract=

Based on a DC microgrid system coupled with photovoltaic power generation, lithium battery-supercapacitor hybrid energy storage, electrolysistank and hydrogen-burning micro gas turbine, a power allocation strategy that integrates the lithium battery state of charge (SOC) and hydrogen storage tank hydrogen state (LOH) is proposed. A PV-electrolysistank-micro gas turbine DC microgrid system model is constructed. The allocation logic of the power judgment module of the coordination control layer is designed, and three operation modes are given when the residual power exists in the DC network. The power allocation strategy is simulated and verified using MATLAB/Simulink software. The simulation results show that the power allocation strategy of DC microgrid system based on hydrogen energy storage can make the lithium battery charge state gradually converge to a reasonable storage interval and can improve the service life of lithium battery.

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基于光伏发电、锂电池-超级电容混合储能、电解水制氢和燃氢微型燃气轮机耦合的直流微电网系统,提出了一种综合考虑锂电池荷电状态(SOC)和储氢罐氢状态(LOH)的功率分配策略。构建了光伏-制氢-微型燃气轮机直流微电网系统模型;设计了协调控制层功率判断模块的分配逻辑,给出了直流网内存在剩余功率时的3种运行模式。使用MATLAB/ SIMULINK软件对该功率分配策略进行仿真验证,结果表明,基于氢储能的直流微电网系统功率分配策略能够使得锂电池荷电状态逐渐趋于合理存储区间,可以提升锂电池的使用寿命。

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王宇轩(1997),男,硕士研究生,主要研究方向为分布式发电技术,

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figureFileSmall=ypFET/NlXUVLUvvSG/lS2w==, figureFileBig=RLSav/6OHCkk3jMhRvnNdQ==, tableContent=null), ArticleFig(id=1241137061885956117, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=EN, label=Tab.1, caption=

Power judgment module logic

, figureFileSmall=null, figureFileBig=null, tableContent=
SOCLOH运行模式
LSLLMP
LSMLLP
LSHLLP
MSLLMP
MSMLMP
MSHLMP
HSLLHP
HSMLHP
HSHLMP
), ArticleFig(id=1241137062066311194, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=CN, label=表1, caption=

功率判断模块逻辑

, figureFileSmall=null, figureFileBig=null, tableContent=
SOCLOH运行模式
LSLLMP
LSMLLP
LSHLLP
MSLLMP
MSMLMP
MSHLMP
HSLLHP
HSMLHP
HSHLMP
), ArticleFig(id=1241137062125031454, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=EN, label=Tab.2, caption=

SOC and LOH critical design values

, figureFileSmall=null, figureFileBig=null, tableContent=
项目设计数值
锂电池最低放电值SOCBat_min0.2
锂电池合理存储区间下限设计值SOCBat_L0.3
锂电池合理存储区间上限设计值SOCBat_H0.7
锂电池最高充电值SOCBat_max0.8
储氢罐最低氢水平值LOHmin0.2
储氢罐合理存储区间下限设计值LOHL0.4
储氢罐合理存储区间上限设计值LOHH0.6
储氢罐最高氢水平值LOHmax0.8
), ArticleFig(id=1241137062213111842, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=CN, label=表2, caption=

锂电池SOC和储氢罐LOH临界设计值

, figureFileSmall=null, figureFileBig=null, tableContent=
项目设计数值
锂电池最低放电值SOCBat_min0.2
锂电池合理存储区间下限设计值SOCBat_L0.3
锂电池合理存储区间上限设计值SOCBat_H0.7
锂电池最高充电值SOCBat_max0.8
储氢罐最低氢水平值LOHmin0.2
储氢罐合理存储区间下限设计值LOHL0.4
储氢罐合理存储区间上限设计值LOHH0.6
储氢罐最高氢水平值LOHmax0.8
), ArticleFig(id=1241137063681118249, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=EN, label=Tab.3, caption=

PV unit parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
最大功率点电压/V29.00
最大功率点电流/A7.35
开路电压/V36.30
短路电流/A7.84
组件串联个数10
组件并联个数47
恒压控制比例系数4.00
恒压控制积分系数0.01
), ArticleFig(id=1241137063781781550, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=CN, label=表3, caption=

PV单元参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
最大功率点电压/V29.00
最大功率点电流/A7.35
开路电压/V36.30
短路电流/A7.84
组件串联个数10
组件并联个数47
恒压控制比例系数4.00
恒压控制积分系数0.01
), ArticleFig(id=1241137063899222068, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=EN, label=Tab.4, caption=

B-SESS unit parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
SC并联个数5
SC额定容量/F30.00
锂电池额定电压/V300.00
锂电池额定容量/(Ah)100.00
电压控制比例系数10.00
电压控制积分系数2.00
SC电流控制比例系数0.70
SC电流控制积分系数500.00
锂电池电流控制比例系数0.70
锂电池电流控制积分系数1 000.00
), ArticleFig(id=1241137063991496761, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=CN, label=表4, caption=

B-SESS单元参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
SC并联个数5
SC额定容量/F30.00
锂电池额定电压/V300.00
锂电池额定容量/(Ah)100.00
电压控制比例系数10.00
电压控制积分系数2.00
SC电流控制比例系数0.70
SC电流控制积分系数500.00
锂电池电流控制比例系数0.70
锂电池电流控制积分系数1 000.00
), ArticleFig(id=1241137064104742975, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=EN, label=Tab.5, caption=

EL unit parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
阳极交换电流密度/(A·cm–2)1×10–6
阴极交换电流密度/(A·cm–2)1×10–3
电压控制比例系数0.90
电压控制积分系数100.00
电流控制比例系数0.10
电流控制积分系数100.00
), ArticleFig(id=1241137064213794883, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1222513216404575024, language=CN, label=表5, caption=

EL单元参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目数值
阳极交换电流密度/(A·cm–2)1×10–6
阴极交换电流密度/(A·cm–2)1×10–3
电压控制比例系数0.90
电压控制积分系数100.00
电流控制比例系数0.10
电流控制积分系数100.00
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基于氢储能的直流微电网系统功率分配策略研究
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王宇轩 , 江路毅 , 范雪飞
热力发电 | 新能源发电技术专题 2023,52(11): 105-114
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热力发电 | 新能源发电技术专题 2023, 52(11): 105-114
基于氢储能的直流微电网系统功率分配策略研究
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王宇轩 , 江路毅, 范雪飞
作者信息
  • 上海发电设备成套设计研究院有限责任公司,上海 200240
  • 王宇轩(1997),男,硕士研究生,主要研究方向为分布式发电技术,

Research on power distribution strategy of DC microgrid system based on hydrogen energy storage
Yuxuan WANG , Luyi JIANG, Xuefei FAN
Affiliations
  • Shanghai Power Equipment Research Institute Co, Ltd, Shanghai 200240, China
出版时间: 2023-11-25 doi: 10.19666/j.rlfd.202305090
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基于光伏发电、锂电池-超级电容混合储能、电解水制氢和燃氢微型燃气轮机耦合的直流微电网系统,提出了一种综合考虑锂电池荷电状态(SOC)和储氢罐氢状态(LOH)的功率分配策略。构建了光伏-制氢-微型燃气轮机直流微电网系统模型;设计了协调控制层功率判断模块的分配逻辑,给出了直流网内存在剩余功率时的3种运行模式。使用MATLAB/ SIMULINK软件对该功率分配策略进行仿真验证,结果表明,基于氢储能的直流微电网系统功率分配策略能够使得锂电池荷电状态逐渐趋于合理存储区间,可以提升锂电池的使用寿命。

直流微电网  /  荷电状态  /  微型燃气轮机  /  功率分配策略

Based on a DC microgrid system coupled with photovoltaic power generation, lithium battery-supercapacitor hybrid energy storage, electrolysistank and hydrogen-burning micro gas turbine, a power allocation strategy that integrates the lithium battery state of charge (SOC) and hydrogen storage tank hydrogen state (LOH) is proposed. A PV-electrolysistank-micro gas turbine DC microgrid system model is constructed. The allocation logic of the power judgment module of the coordination control layer is designed, and three operation modes are given when the residual power exists in the DC network. The power allocation strategy is simulated and verified using MATLAB/Simulink software. The simulation results show that the power allocation strategy of DC microgrid system based on hydrogen energy storage can make the lithium battery charge state gradually converge to a reasonable storage interval and can improve the service life of lithium battery.

DC microgrid  /  state of charge  /  micro gas turbine  /  power allocation strategy
王宇轩, 江路毅, 范雪飞. 基于氢储能的直流微电网系统功率分配策略研究. 热力发电, 2023 , 52 (11) : 105 -114 . DOI: 10.19666/j.rlfd.202305090
Yuxuan WANG, Luyi JIANG, Xuefei FAN. Research on power distribution strategy of DC microgrid system based on hydrogen energy storage[J]. Thermal Power Generation, 2023 , 52 (11) : 105 -114 . DOI: 10.19666/j.rlfd.202305090
在国家“碳达峰”和“碳中和”的背景下,光伏发电快速发展。光伏发电的间歇性、随机性和波动性会导致电力供应不稳定,光伏发电与储能装置组合构成微电网系统是解决上述问题的有效手段[1-2]。氢能作为未来理想的能源之一,利用光伏电解水制氢可以实现光伏发电与燃氢微型燃气轮机发电的互补转化,同时可以改善弃光问题。
为了保证直流微电网系统稳定运行,国内外学者对光氢储微电网系统的功率分配与协调控制方法开展了一系列研究工作。文献[3]搭建了光伏-超级电容-制氢-并网混合系统模型,并对其运行控制方法进行了研究,给出了3种运行工况及对应控制策略,利用超级电容(super capacitor,SC)平抑系统功率,但没有对储氢装置进行建模。文献[4]搭建了PEM电解槽和储氢罐的数学模型,仿真验证了光、氢、储耦合系统能够稳定制氢、储氢,但功率分配没有考虑储氢罐氢状态。文献[5]构建了包括光伏发电设备、电解槽、高压储氢罐以及燃料电池在内的光伏发电制氢储能系统仿真模型,对系统内部气液两相传输以及能量传输效率进行了预测和分析,但没有考虑系统内储能装置的状态。
文献[6-9]提出了微电网系统在并/离网运行模式切换时储能设备与直流母线的协调控制方法,设计了功率分配型二阶滤波控制策略,结合储能元件的荷电状态来控制对应变换器的工作状态。文献[10]提出风、储、氢、燃混合孤岛系统双层协调控制方法,以高风速、低风速以及无风3种情况划分了8种运行模式,并考虑了负荷管理策略。文献[11]使用双重遗传算法优化直接耦合系统工作温度,保持接近光伏阵列最大功率点的运行条件,最小化过剩发电量。文献[12]理论分析了光伏阵列和电解槽的实验特性,实现了电解槽和光伏阵列伏安特性相匹配。
上述文献对光伏耦合制氢系统的研究主要集中在其结构设计上,而对调节策略分析得较少,导致系统无法适应太阳能的随机性和波动性,动态调节性差,光伏消纳功率不稳定。传统方法在直流微电网内功率不平衡时,由电解水制氢消纳全部剩余功率,锂电池平抑系统功率,没有兼顾锂电池荷电状态(state of charge,SOC)及储氢罐氢状态(level of hydrogen,LOH)[13-14]。同时,固定的电解槽运行数量导致膜电极电流存在较大波动,影响电解槽寿命[15-17]。为了维持直流微电网系统稳定运行,同时延长微电网内装置使用寿命,本文提出一种综合考虑锂电池SOC和储氢罐LOH的功率分配策略,防止锂电池长期工作在深度充/放电区域,延长锂电池的使用寿命;同时,根据直流微电网内剩余功率确定投运电解槽数量,以减小电解槽膜电极电流波动范围,延长电解槽使用寿命。
基于氢储能的直流微电网系统如图1所示。
B-SESS包括锂电池、超级电容以及对应的双向DC/DC变换器;HESS包括质子交换膜(proton exchange membrane,PEM)电解槽以及储氢罐,PEM电解槽同样配置了双向DC/DC变换器,电解槽消纳剩余功率制得的氢气储存在储氢罐中。微型燃气轮机发电单元通过AC/DC变换器连接到直流母线,当光伏发电单元停止工作时,由微型燃气轮机燃氢为用电负荷供电。
光伏的输出电流特性表达式[7]以及锂电池和超级电容的数学模型[6]为:
{I=ISC{1C1[exp(UC2UOC)1]}C1=(1ImpISC)exp(UmpC2UOC)C2=(UmpUOC1)[ln(UmpC2UOC)]1
{Ubat=UOCIR0Uc1Uc2I=Uc1R1+C1dUc1dtSOCt=SOCt11CtUbat,tPbat,tdt
{CSC=BACfr=ABrf
式中:C1C2为光伏特定参数;ISC为光伏电池短路电流;U为光伏电池输出电压;UOC为光伏电池开路电压;Imp为最大功率点处的工作电流;Ump为最大功率点处的工作电压;R0为动态电路内阻;Uc1Uc2为2个串联RC电路两端电压;SOCt、SOCt–1为对应tt1时刻的储能荷电状态;CtUbat,tPbat,t分别为锂电池t时刻的标定电容、电池电压、电池功率;AB分别为SC串联和并联个数;CSC为SC等效总电容;Cf为SC的单体电容;r为SC等效总电阻;rf为SC单体电阻。
电解槽端电压Uel由可逆电压、欧姆过电势、活化过电势和扩散过电势组成:
Uel=Urev+Uohm+Uact+Udiff
式中:Urev为可逆电压;Uohm为欧姆过电势;Uact为活化过电势;Udiff为扩散过电势。
可逆电压、欧姆过电势、活化过电势和扩散过电势的表达式见文献[8]。电解槽单元内部各电解单元参数一致,数学模型为:
{Ustack=nsUelIstack=npIel
式中:npns分别为电解槽并联数和串联数;Uel为单个电解槽端电压;Iel为流过单个电解槽的电流;Ustack为电解槽单元端电压;Istack为流过电解槽单元的电流。
图2为PEM电解槽模型的工作特性曲线,即PEM电解槽端电压随膜电极电流密度的变化。
根据文献[8],储氢罐氢状态的表达式为:
LOH(t)=nH2(t)nH2,max×100%
式中:nH2(t)t时刻储氢罐的储氢量;nH2,max为储氢罐最大储氢量。
燃氢微型燃气轮机采用回热循环方式可提高能量利用率。微型燃气轮机数学模型为[9]
{T2=T1(1+πk1k1ηc)T3=T0+Gacpa(T2aT0)+GfHuηb+Gfcpf(TfT0)GgcpgT4=T3[1(1πtkb1kb)ηt]Pt=GgcpgT3(1πtkb1kb)ηt
式中:T1T2分别为压气机的入口和出口温度;k为空气比热比;ηc为压气机的绝热效率;T3为燃烧室出口温度;T2a为回热器出口空气温度;cpf为氢气的定压比热容;T0Tf分别为环境温度和氢气的进口温度;cpg为燃气的定压比热容;πt为膨胀比;ηt为透平效率;T4为透平排烟温度;kb为燃气比热比;Pt为透平输出功率。
直流微电网控制分为协调层控制和底层控制。协调层控制是分布式电源和储能单元协同工作;底层控制是各设备与直流母线之间相互独立的控制方式[18-21]
协调控制层负责控制模式切换和系统功率分配。
混合系统包括4种运行工况。
1)工况1 PV发电且出力大于负荷用电需求,定义系统剩余功率为:
PC=PPVPLoad
电解槽消纳全部剩余功率电解水制氢,B-SESS维持直流母线电压稳定。此时PEM电解槽消纳功率为:
PEL=PPVPLoad
式中:PC为剩余功率;PPV为PV发出的功率;PLoad为用电负荷所需功率;PEL为PEM电解槽消纳功率。
2)工况2 PV阵列发电且出力大于负荷用电需求,电解槽以设计工况点功率运行,不完全消纳剩余功率。此时,B-SESS的运行情况分为2种:功率剩余时用于B-SESS充电;功率缺额时由B-SESS放电补足。此时,混合储能系统吸收功率为:
PB-SESS=PPVPLoadPNEL
式中:PB-SESS为混合储能系统吸收功率;PNEL为投运的电解槽设计工况点总功率。工况1和工况2由锂电池SOC和储氢罐LOH确定。
3)工况3 白天短时遮阳PV出力小于用电负荷需求时,由B-SESS补足缺额功率,维持直流母线电压稳定,此时混合储能系统吸收功率为:
PB-SESS=PPVPLoad
4)工况4 长时间阴雨天或夜晚时,微型燃气轮机出力并且跟随用电负荷需求改变。此时微型燃气轮机的功率PGT为:
PGT=PLoad
上述运行工况系统管理流程如图3所示。
基于工况2的2种电能流动路径,功率分配总体架构如下。
投运电解槽数NN+1的计算规则:N为满足N×PNEL_1<PC的最大值。其中,PNEL_1为单个PEM电解槽的设计工况点功率。
1)当锂电池SOC和储氢罐LOH均处于合理存储区间时,剩余功率全部用于电解水制氢,投运电解槽数由PC确定,单个电解槽的膜电极电流不会产生大范围波动。
2)当锂电池SOC较低时,需要根据储氢罐LOH进行功率分配。当储氢罐LOH处于合理存储区间或LOH较高时,单个电解槽以设计工况点功率运行,电解槽单元总功率小于剩余功率,B-SESS充电使锂电池SOC上升;当储氢罐LOH较低时,N个电解槽消纳全部剩余功率。
3)当锂电池SOC较高时,同样根据储氢罐LOH进行功率分配。当LOH处于合理存储区间或LOH较低时,N+1个电解槽以设计工况点功率运行,电解槽单元总功率大于剩余功率,B-SESS放电使锂电池SOC降低;当LOH较高时,N个电解槽消纳全部剩余功率。
直流微电网功率分配方法如图4所示。
PC>0时,开关接入通路1,根据单个电解槽的设计工况点功率,由剩余功率计算取整得到运行电解槽数N。随后进入功率判断模块,该模块根据锂电池SOC和储氢罐LOH判断选择3种输出结果:N个电解槽以设计工况点功率运行,B-SESS充电;N+1个电解槽以设计工况点功率运行,B-SESS放电;电解槽单元完全消纳剩余功率。
PC<0时,开关接入通路2经过天气数据模块。若白天遮阳导致光照强度极低,B-SESS补充功率缺额;若长时间阴雨天或夜间导致光照强度极低,则由微型燃气轮机向用电负荷供电。
图4通路1中的功率判断模块逻辑见表1表1中:LS表示锂电池位于深度放电区,SOCBat_min< SOCBat≤SOCBat_L;MS表示锂电池处于合理存储区间,SOCBat_L<SOCBat<SOCBat_H;HS表示锂电池位于深度充电区,SOCBat_H≤SOCBat<SOCBat_max;LL表示储氢罐LOH在LOHmin和LOHL范围内;ML表示储氢罐位于合理存储区间,LOH在LOHL和LOHH范围内;HL表示LOH在LOHH和LOHmax范围内;LP表示N个电解槽以设计工况点功率运行,B-SESS充电;MP表示电解槽单元完全消纳剩余功率;HP表示N+1个电解槽以设计工况点功率运行,B-SESS放电。其中,SOCBat_min为锂电池最低放电值;SOCBat_L为锂电池合理存储区间下限设计值;SOCBat_H为锂电池合理存储区间上限设计值;SOCBat_max为锂电池最高充电值;LOHmin为储氢罐最低氢水平值;LOHL为储氢罐合理存储区间下限设计值;LOHH为储氢罐合理存储区间上限设计值;LOHmax为储氢罐最高氢水平值。
表1可知,当SOC为LS且LOH为ML或HL时,系统以LP模式运行,表明SOC接近下限值而LOH较高或处于合理区间时,系统分配剩余功率同时用于B-SESS充电和电解水制氢。同理,当SOC为HS且LOH为LL或ML时,系统以HP模式运行,表明SOC接近上限值而LOH较低或处于合理区间时,除剩余功率全部用于制氢外,B-SESS也放电用于制氢,使锂电池SOC趋近于合理存储区间。
当SOC为LS且LOH为LL时,系统以MP模式运行,表明SOC和LOH同时接近下限值时,优先保证储氢罐LOH趋向合理存储区间,全部剩余功率由电解槽消纳,在LOH位于合理区间后系统随之分配功率用于储能系统充电。同理,当SOC为HS且LOH为HL时,系统以MP模式运行,表明SOC和LOH同时接近上限值时,B-SESS维持直流母线电压稳定,电解槽消纳剩余功率。
当SOC为MS时,LOH的状态不会影响系统运行模式,运行模式为MP,电解槽消纳全部剩余功率,B-SESS维持直流母线电压稳定。
由上述不同的运行模式可知,系统存在SOCBat_L、SOCBat_H、LOHBat_L和LOHBat_H 4个临界点,电解水制氢使氢储量单向上升,而锂电池SOC可能在合理存储区间上/下限设计值处产生振荡,因此加入滞环环节(图4通路1)。LP、MP、HP 3种运行模式功率分配情况如图5图7所示。
直流微电网底层设备控制方式如图8所示[22-23]图8中:UPVIPV为光伏阵列输出电压和输出电流;Udc_refUdc为直流母线的参考电压和实际电压;DPV为变换器控制信号;Dbat1Dbat1为锂电池对应的双向DC/DC变换器的控制信号;DSC1DSC2为超级电容对应的双向DC/DC变换器的控制信号;UEL_refUEL为电解槽电压参考值和实际端电压值;IEL_refIELIEL为电解槽电流参考值和实际电流值;DEL为DC/DC变换器控制信号;T4T4_ref为透平排烟温度和透平排烟温度限值;nnref为微型燃气轮机实际转速和转速基准值;n˙ref为加速度基准值;GfGf_n为氢气实际基准流量。
图8a)可知,光伏发电单元包含最大功率跟踪(maximum power point tracking,MPPT)和恒压2种控制方式。锂电池承担长期与系统进行功率交换的任务,SC负责快速响应系统波动功率中的高频分量。由图8b)可知,将母线电压实时采集值与参考值的差值送入PI调节器中,经过低通滤波器分别得到锂电池和SC的电流参考值[6]。由图8c)可知,电解槽单元控制方式中的电压参考值由协调控制层获取。由图8d)可知,燃气轮机控制系统包括转速、温度和加速度控制系统。3种控制方式分别生成相应的氢气流量基准值,由最小值选择器选择最小流量值对应的控制方式,一般情况下,转速控制系统起主要作用。
本文基于MATLAB/SIMULINK仿真软件搭建了光-氢-微型燃气轮机系统模型。
基于图2电解槽工作特性曲线,PEM电解槽单片膜电极的设计工况点参数为1 A/cm2和2 V,单片膜电极的面积为0.25 m2,则单个PEM电解槽的设计工况点功率为5 kW。锂电池SOC和储氢罐LOH临界设计值见表2,光伏制氢系统各组成单元主要参数见表3表5
基于上述搭建的光伏MPPT模型,设置辐照度为750 W/m2,3 s时改变辐照度为900 W/m2,8 s时改变辐照度为800 W/m2,仿真时间10 s。辐照度变化及光伏MPPT控制结果如图9所示。
图9可知:当系统开始运行时,PV功率迅速上升稳定至75 kW左右;在3 s辐照度突变后,PV在短时间内跟踪至最大功率处且跟踪速度良好;在8 s辐照度再次改变,光伏功率迅速跟踪至80 kW。可见,当辐照度发生突变后,光伏系统也能在短时间内实现MPPT,验证了光伏MPPT功能。
设置直流母线电压为400 V;直流侧初始阻值为4 Ω即用电负荷40 kW,5 s时用电负荷突变至50 kW;SC和锂电池初始SOC均为50%,储氢罐LOH为50%;0~3 s辐照度为750 W/m2,3~8 s辐照度为900 W/m2,8 s时辐照度改变为800 W/m2,仿真时间10 s。工况1运行时直流母线电压及B-SESS的SOC仿真曲线如图10所示。由图10仿真结果可知:在3 s和8 s光伏输出功率发生波动时,直流母线电压基本保持400 V;在初始SOC为50%的条件下SC和锂电池的SOC比较稳定,没有明显变化趋势,工作稳定度、灵敏度良好。
图11为工况1运行的混合系统功率曲线。由图11可知,当锂电池SOC和储氢罐LOH均位于合理区间时,PEM电解槽消纳全部剩余功率,SC和锂电池运行功率基本为0,此时满足工况1的功率分配。
按照功率判断模块分配逻辑,当锂电池SOC为LS即处于深度放电区,储氢罐LOH上升超过40%由LL改变为ML,运行模式由MP改变至LP。设置直流母线电压为400 V,初始用电负荷40 kW, 5 s时增加10 kW用电负荷;锂电池初始SOC为29.997%,储氢罐初始LOH为39.999%;0~8 s辐照度为750 W/m2,8 s时辐照度改变为770 W/m2,仿真时间10 s。为了能够在10 s仿真时间内包括尽可能多的系统运行模式,设置功率分配模块中锂电池SOC连接的滞环阈值区间上限为30.005%,下限为29.999%。图12图13为深度放电区条件下本文方法和传统方法的仿真曲线。由图12可知:在0~4 s内,储氢罐LOH由39.99%上升,初始运行模式为LS-LL-MP;在4 s时,储氢罐LOH达到40%,运行模式改变为LS-ML-LP,N个电解槽以设计工况点功率运行,B-SESS充电,锂电池SOC上升明显;在6 s后,系统运行方式改变为MS-ML-MP,锂电池SOC再次趋于平缓。
图13传统方法结果可以看出,储氢罐LOH达到40%时,锂电池SOC、锂电池功率和SC功率没有明显变化趋势。对比图12图13可以看出,图12中4~6 s内电解槽消纳功率明显小于图13中电解槽消纳的功率,该部分差值功率用于锂电池充电,减少了锂电池处于深度放电区的时间。
按照功率分配模块逻辑,当锂电池SOC为HS即处于深度充电区,储氢罐LOH为LL,运行模式为HP,B-SESS放电,当锂电池SOC下降到MS,功率分配方式变为MP,N个电解槽完全消纳剩余功率。设置直流母线电压为400 V,直流侧初始用电负荷40 kW,5 s时增加10 kW用电负荷;锂电池初始SOC为70.004%,储氢罐LOH为40%;0~8 s辐照度为750 W/m2;8 s时辐照度改变为770 W/m2,仿真时间10 s。将功率分配模块中锂电池SOC连接的滞环阈值区间上限设置为70.002%,下限设置为69.998%。图14图15为深度充电区条件下本文方法和传统方法的仿真曲线。
图14可知:在0~3 s内,锂电池SOC高于滞环阈值区间下限69.998%,储氢罐LOH为ML,系统运行模式为HS-ML-HP,B-SESS放电,锂电池SOC下降明显;在3 s时,锂电池SOC达到滞环阈值区间下限69.998%,系统运行模式改变为MS-ML-MP,锂电池SOC趋于平缓。从图15传统方法的结果可以看出,锂电池始终处于深度充电区,SOC没有明显下降趋势。对比图14图15可以看出,图14中0~3 s内电解槽消纳功率明显高于图15中电解槽消纳的功率,该部分差额功率由锂电池放电补充,减少锂电池处于深度充电区的时间。
当光伏制氢系统停止工作时,直流微电网内电负荷需求由燃氢微型燃气轮机提供。因此,需要验证在电负荷突变时微型燃气轮机的输出响应。
设置初始用电负荷为35 kW,在10 s时用电负荷突变到50 kW,随后在20 s时突降到45 kW,得到燃气轮机所消耗的氢气燃料流量、透平出口温度、机组输出功率、机组热效率和转速的动态响应,结果如图16所示。图16中各参数值为额定工况相应的相对值。
图16可以看出,在微型燃气轮机提供负荷35 kW功率稳定后,10 s时用电负荷突变到50 kW,微型燃气轮机的转速先降低,在控制系统的调节下,相对氢气流量由35 kW工况下的0.886 1升高到额定工况下的1.000 0,燃烧室和透平出口温度升高,机组热效率升高。在20 s时,电负荷从50 kW突降到45 kW,微型燃气轮机转速小幅度升高,在控制系统的调节下,相对氢气流量减少到额定工况下的0.960 8,并使机组输出功率与电负荷平衡,微型燃气轮机转速重新稳定到额定转速,此时的机组热效率降低到额定工况的0.948 0。
1)计及锂电池SOC和储氢罐LOH通过协调控制层功率判断模块改变系统运行模式,能够实现B-SESS与HESS的功率优化分配。
2)锂电池处于深度充电区域且直流微电网内存在剩余功率时,使得PEM电解槽分配更高功率, B-SESS放电,迅速降低锂电池SOC。锂电池处于深度放电区域且直流微电网内存在剩余功率时,使得PEM电解槽分配更低功率,B-SESS充电,迅速升高锂电池SOC。
3)通过本文提出的基于氢储能的直流微电网系统功率分配策略,能够保证锂电池SOC向合理存储区间内调节,减少了锂电池位于深度充/放电区域的时间,延长锂电池使用寿命。
4)基于燃氢微型燃气轮机控制仿真模型,研究了负荷扰动时微型燃气轮机主要输出变量的动态响应特性。降负荷时微型燃气轮机转速先升高,在控制系统的作用下减小氢气流量,2 s后稳定至额定转速;升负荷时微型燃气轮机转速先降低,在控制系统的作用下增加氢气流量,转速升高,2 s后稳定至额定转速。当扰动存在时,控制系统能够使机组迅速达到新的稳定状态,满足直流微电网要求。
  • 航空发动机及燃气轮机基础科学中心项目(P2021-A-I-003-002)
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doi: 10.19666/j.rlfd.202305090
  • 接收时间:2023-05-25
  • 首发时间:2026-01-26
  • 出版时间:2023-11-25
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  • 收稿日期:2023-05-25
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Science Center for Gas Turbine Project(P2021-A-I-003-002)
航空发动机及燃气轮机基础科学中心项目(P2021-A-I-003-002)
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    上海发电设备成套设计研究院有限责任公司,上海 200240
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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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