Article(id=1154037279438918454, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1154037268550509325, articleNumber=null, orderNo=null, doi=10.13234/j.issn.2095-2805.2024.4.182, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1683734400000, receivedDateStr=2023-05-11, revisedDate=1688572800000, revisedDateStr=2023-07-06, acceptedDate=1691510400000, acceptedDateStr=2023-08-09, onlineDate=1753073528394, onlineDateStr=2025-07-21, pubDate=1722268800000, pubDateStr=2024-07-30, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753073528394, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753073528394, creator=13701087609, updateTime=1753073528394, updator=13701087609, issue=Issue{id=1154037268550509325, tenantId=1146029695717560320, journalId=1146031654075715584, year='2024', volume='22', issue='4', pageStart='1', pageEnd='338', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753073525798, creator=13701087609, updateTime=1753780979931, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1157004546338804561, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1154037268550509325, language=EN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1157004546338804562, tenantId=1146029695717560320, journalId=1146031654075715584, issueId=1154037268550509325, language=CN, specialIssueTitle=, coverIllustrator=, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=182, endPage=191, ext={EN=ArticleExt(id=1154037280558797626, articleId=1154037279438918454, tenantId=1146029695717560320, journalId=1146031654075715584, language=EN, title=Research on Inertia Support Performance of New Energy Storage Microgrid, columnId=1152281492550987902, journalTitle=Journal of Power Supply, columnName=Renewable Energy System, runingTitle=null, highlight=null, articleAbstract=

Aimed at the problem that the traditional virtual inertia control method cannot restore the DC bus voltage to its rated value at the frequency recovery stage, a virtual inertia control method using a high-pass filter is proposed. MATLAB/Simulink simulation results show that the changes in DC bus voltage can reflect the microgrid frequency in real-time, and the novel virtual inertia control strategy can ensure the frequency stability in the entire changing process. At the frequency recovery stage, the virtual inertia is reduced to accelerate the frequency recovery, providing sufficient time margin for subsequent frequency adjustment. The high-pass filter virtual inertia control method can restore the DC bus voltage to its initial value and maintain the DC bus voltage stability of the new energy storage microgrid without changing the inertia support of the super capacitor energy storage.

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针对传统虚拟惯量控制无法在频率恢复阶段使直流母线电压恢复至额定的问题,提出1种采用高通滤波器的虚拟惯量控制方法。MATLAB/Simulink仿真结果表明,直流母线电压变化可以反应微电网实时频率,新型虚拟惯量控制策略可以保证频率变化全过程稳定,在频率恢复阶段减小虚拟惯量加快频率恢复,为后续频率调节提供充足时间裕度。高通滤波器虚拟惯量控制方法可以使直流母线电压恢复至初值,在不改变超级电容储能惯量支撑的基础上,使新能源储能微电网保持直流母线电压稳定。

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卢军(1973-),男,硕士,副教授。研究方向:储能电气技术。E-mail: 13548583346@126.com。

杨沛豪(1993-),男,通信作者,博士,工程师。研究方向:储能电气技术。E-mail:yangpeihao@tpri.com.cn。

何凯琳(1994-),女,硕士,工程师。研究方向:储能发电技术。E-mail: 474918828@qq.com。

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杨沛豪(1993-),男,通信作者,博士,工程师。研究方向:储能电气技术。E-mail:yangpeihao@tpri.com.cn。

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Virtual inertia control strategy for grid-tied photovoltaic power generation system[J]. High Voltage Engineering, 2020. 46(11): 3743-3751 (in Chinese)., articleTitle=Virtual inertia control strategy for grid-tied photovoltaic power generation system, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1154048232146587865, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, xref=1, ext=[AuthorCompanyExt(id=1154048232154976473, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, companyId=1154048232146587865, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 General Education Center Changsha Social Work College Changsha 410004 China), AuthorCompanyExt(id=1154048233652343032, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, companyId=1154048232146587865, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1 长沙民政职业技术学院 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区域 ${\Delta \omega }$ $\mathrm{d}\omega /\mathrm{d}t$ ${\Delta \omega }\left({\mathrm{d}\omega /\mathrm{d}t}\right)$ ${H}_{\mathrm{c}}$
I - - + 增大
II - + - 减小
III + + + 适当增大
IV + - - 适当减小
), ArticleFig(id=1154048241227256318, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, language=CN, label=表1, caption=调频区域划分及惯量调节, figureFileSmall=null, figureFileBig=null, tableContent=
区域 ${\Delta \omega }$ $\mathrm{d}\omega /\mathrm{d}t$ ${\Delta \omega }\left({\mathrm{d}\omega /\mathrm{d}t}\right)$ ${H}_{\mathrm{c}}$
I - - + 增大
II - + - 减小
III + + + 适当增大
IV + - - 适当减小
), ArticleFig(id=1154048241302753797, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, language=EN, label=Tab. 2, caption=Simulation parameters of new energy storage microgrid, figureFileSmall=null, figureFileBig=null, tableContent=
参数 数值
光伏额定功率/MW 20
风电额定功率/MW 30
超容额定容量/MW 10
光伏额定电压/V 380
风电额定电压/V 380
超容交流线电压/V 400
单位电阻/$\left({\Omega /\mathrm{{km}}}\right)$ 0.141
光伏功率电流/kA 22.8
直流母线电压/V 580
单位电感/(mH/km) 0.931
风电额定转速/(r/min) 1800
超容充电极限 90%
超容放电极限 10%
线路长度/km 35
), ArticleFig(id=1154048241361474054, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, language=CN, label=表2, caption=新能源储能微电网仿真参数, figureFileSmall=null, figureFileBig=null, tableContent=
参数 数值
光伏额定功率/MW 20
风电额定功率/MW 30
超容额定容量/MW 10
光伏额定电压/V 380
风电额定电压/V 380
超容交流线电压/V 400
单位电阻/$\left({\Omega /\mathrm{{km}}}\right)$ 0.141
光伏功率电流/kA 22.8
直流母线电压/V 580
单位电感/(mH/km) 0.931
风电额定转速/(r/min) 1800
超容充电极限 90%
超容放电极限 10%
线路长度/km 35
), ArticleFig(id=1154048241449554441, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, language=EN, label=Tab. 3, caption=Parameters of grid-connected converter control system, figureFileSmall=null, figureFileBig=null, tableContent=
参数 数值
初始虚拟惯量/$\left({\mathrm{{kg}}\cdot {\mathrm{m}}^{2}}\right)$ 1.2
频率变化率阈值 0.1
阻尼系数 1.6
虚拟惯量高通增益系数 500
频率偏差阈值/Hz 0.1
最大虚拟惯量/$\left({\mathrm{{kg}}\cdot {\mathrm{m}}^{2}}\right)$ 25
虚拟惯量调节系数 0.9
高通滤波器截止频率/(rad/s) 300
), ArticleFig(id=1154048241592160779, tenantId=1146029695717560320, journalId=1146031654075715584, articleId=1154037279438918454, language=CN, label=表3, caption=并网换流器控制系统参数, figureFileSmall=null, figureFileBig=null, tableContent=
参数 数值
初始虚拟惯量/$\left({\mathrm{{kg}}\cdot {\mathrm{m}}^{2}}\right)$ 1.2
频率变化率阈值 0.1
阻尼系数 1.6
虚拟惯量高通增益系数 500
频率偏差阈值/Hz 0.1
最大虚拟惯量/$\left({\mathrm{{kg}}\cdot {\mathrm{m}}^{2}}\right)$ 25
虚拟惯量调节系数 0.9
高通滤波器截止频率/(rad/s) 300
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新能源储能微电网惯量支撑性能研究
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卢军 1 , 杨沛豪 2, 3 , 何凯琳 4
电源学报 | 新能源系统 2024,22(4): 182-191
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电源学报 | 新能源系统 2024, 22(4): 182-191
新能源储能微电网惯量支撑性能研究
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卢军1 , 杨沛豪2, 3 , 何凯琳4
作者信息
  • 1 长沙民政职业技术学院 通识教育中心 长沙 410004
  • 2 西安交通大学 电气工程学院 西安 710049
  • 3 西安热工研究院有限公司 西安 710054
  • 4 中国能源建设集团西北电力试验研究院有限公司 西安 710054
  • 卢军(1973-),男,硕士,副教授。研究方向:储能电气技术。E-mail: 13548583346@126.com。

    杨沛豪(1993-),男,通信作者,博士,工程师。研究方向:储能电气技术。E-mail:yangpeihao@tpri.com.cn。

    何凯琳(1994-),女,硕士,工程师。研究方向:储能发电技术。E-mail: 474918828@qq.com。

Research on Inertia Support Performance of New Energy Storage Microgrid
Jun LU1 , Peihao YANG2, 3 , Kailin HE4
Affiliations
  • 1 General Education Center Changsha Social Work College Changsha 410004 China
  • 2 School of Electrical Engineering Xi'an Jiaotong University Xi'an 710049 China
  • 3 Xi'an Thermal Research Institute Co., Ltd Xi'an 710054 China
  • 4 Northwest Electric Power Test and Research Institute Co., Ltd, China Energy Construction Group Xi'an 710054 China
出版时间: 2024-07-30 doi: 10.13234/j.issn.2095-2805.2024.4.182
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针对传统虚拟惯量控制无法在频率恢复阶段使直流母线电压恢复至额定的问题,提出1种采用高通滤波器的虚拟惯量控制方法。MATLAB/Simulink仿真结果表明,直流母线电压变化可以反应微电网实时频率,新型虚拟惯量控制策略可以保证频率变化全过程稳定,在频率恢复阶段减小虚拟惯量加快频率恢复,为后续频率调节提供充足时间裕度。高通滤波器虚拟惯量控制方法可以使直流母线电压恢复至初值,在不改变超级电容储能惯量支撑的基础上,使新能源储能微电网保持直流母线电压稳定。

新能源储能微电网  /  超级电容  /  惯量支撑  /  虚拟惯量  /  高通滤波器

Aimed at the problem that the traditional virtual inertia control method cannot restore the DC bus voltage to its rated value at the frequency recovery stage, a virtual inertia control method using a high-pass filter is proposed. MATLAB/Simulink simulation results show that the changes in DC bus voltage can reflect the microgrid frequency in real-time, and the novel virtual inertia control strategy can ensure the frequency stability in the entire changing process. At the frequency recovery stage, the virtual inertia is reduced to accelerate the frequency recovery, providing sufficient time margin for subsequent frequency adjustment. The high-pass filter virtual inertia control method can restore the DC bus voltage to its initial value and maintain the DC bus voltage stability of the new energy storage microgrid without changing the inertia support of the super capacitor energy storage.

New energy storage microgrid  /  super capacitor  /  inertia support  /  virtual inertia  /  high-pass filter
卢军, 杨沛豪, 何凯琳. 新能源储能微电网惯量支撑性能研究. 电源学报, 2024 , 22 (4) : 182 -191 . DOI: 10.13234/j.issn.2095-2805.2024.4.182
Jun LU, Peihao YANG, Kailin HE. Research on Inertia Support Performance of New Energy Storage Microgrid[J]. Journal of Power Supply, 2024 , 22 (4) : 182 -191 . DOI: 10.13234/j.issn.2095-2805.2024.4.182
为响应 “碳达峰、碳中和” 宏观能源政策, 能源领域正着力构建以新能源为主体的新型电力系统[1-2]。新能源发电需要用到大量电力电子设备, 传统电力电子设备无法为电力系统提供如旋转电机具备的转动惯量, 从而造成电力系统对频率调节能力的严重削弱, 无法抑制因功率波动而造成的频率扰动, 甚至造成新能源大面积脱网, 严重影响电力系统的稳定性[3-5]
储能与风电、光伏等新能源电力系统采用直流组网形式, 可使储能与新能源之间的互连更简单和高效, 同时直流组网形式无需考虑无功优化问题, 也不存在谐波污染[6-8]。在新能源储能微电网 DC/AC换流器中采用诸如虚拟惯量控制、下垂控制和虚拟同步机等策略, 可以为微电网提供虚拟惯量支撑并使其具备频率调节能力[9-11]。文献[12]在微电网换流器频率控制环节增加虚拟惯量响应模块, 保证微电网输出频率与大电网频率变化偏差率一致, 通过微电网中储能吸收/释放能量做到频率偏差瞬时响应; 文献[13]为了提升系统同步惯量支撑能力, 在储能换流器控制系统内引入频率微分信号实现惯量模拟, 进一步添加鲁棒控制器来改善储能输出频率特性; 文献[14]分析送端、受端因功率不平衡造成的区域电网频率振荡机理, 采用虚拟同步机控制策略, 将虚拟惯量用频差变化率表征, 从而提升虚拟同步机对电网的惯量支撑性能。
直流微电网中电力电子设备之间通过直流母线连接, 直流电压稳定在一定范围内, 在面对功率波动时具备电压调节鲁棒性, 是并网换流器惯量控制领域的一个研究重点[15-17]。文献[18]提出 1 种模拟虚拟直流电机的惯量控制方案, 将虚拟惯量用直流电压变化率进行表征, 自适应调节虚拟惯量的加速电压恢复至稳态;文献[19]通过分析储能系统在电压跌落故障前后的功角特性, 以减小加速面积为控制目标, 提出一系列措施来维持储能输出电压在故障期间稳定; 文献[20]将新能源微网直流母线电压作为负反馈信号, 引入并网换流器电压外环中积分环节, 将线路阻抗用可调虚拟阻抗代替, 实现了直流电压动态可调。
为了提升新能源储能微电网惯量支撑性能, 本文提出 1 种采用高通滤波器的自适应虚拟惯量控制方法。首先, 建立新能源储能微电网换流器直流电压一频率虚拟惯量控制系统;其次, 对传统惯量控制进行改进, 提出 1 种频率上升/下降幅度自动调节虚拟惯量方法, 提出超级电容储能惯量支撑最佳配置方案; 然后, 为了在频率恢复阶段使直流母线电压快速复原, 采用高通滤波器提取角频率高频分量进行惯量模拟; 最后, 通过仿真验证所提方案的有效性和可行性。
图1为典型含有超级电容储能的新能源直流微电网拓扑结构,其中光伏板通过$\mathrm{{DC}}/\mathrm{{DC}}$ 换流装置与直流母线连接,${P}_{\mathrm{{PV}}}$ 为光伏板的输出功率; 风机通过$\mathrm{{AC}}/\mathrm{{DC}}$ 整流装置与直流母线连接,${P}_{\mathrm{W}}$ 为风机的输出功率;超级电容通过 DC/DC 换流装置与直流母线连接,${P}_{\mathrm{C}}$ 为超级电容的吸收/释放功率; 直流微电网中配置直流负荷;直流母线通过$\mathrm{{DC}}/\mathrm{{AC}}$ 换流器及工频升压变与交流主网连接, 在交流输电线路上配置有交流负荷,$P$ 为直流微电网与交流电网功率交换。负荷投/切会引起直流微电网并网 DC/AC 换流器频率波动, 进一步引起直流母线电压波动, 对直流微电网稳定运行造成影响。通过配置超级电容储能快速释放/吸收电能,同时在并网 DC/AC换流器采用模拟惯量算法, 可以提升直流微电网惯量支撑能力。
本文在微电网并网 DC/AC 换流器采用模拟旋转同步电机惯量控制方法。旋转同步电机转子旋转动能${E}_{\mathrm{k}}$
${E}_{\mathrm{k}}= \frac{1}{2{p}^{2}}{J}_{\mathrm{{sg}}}{\omega }_{\mathrm{{sg}}}^{2}$
式中:$p$ 为电机极对数;${J}_{\mathrm{{sg}}}\text{、}{\omega }_{\mathrm{{sg}}}$ 分别为电机转动惯量、机械角速度。
旋转同步电机惯量$H$
$ H =\frac{{E}_{\mathrm{k}}}{{S}_{\mathrm{N}}}= \frac{{J}_{\mathrm{{sg}}}{\omega }_{\mathrm{n}}^{2}}{2{p}^{2}{S}_{\mathrm{N}}}$
式中,${\omega }_{\mathrm{n}}\text{、}{S}_{\mathrm{N}}$ 分别为电机额定机械角速度、额定容量。由式(2)可知,旋转同步电机惯量$H$ 仅与电机额定转速运行时的动能及额定容量有关, 不受功率变化的影响。
旋转动能${E}_{\mathrm{k}}$ 对时间求导,并引入惯量$H$,可得旋转同步电机转子运动方程
$ P ={P}_{\mathrm{m}}- {P}_{\mathrm{e}}= \frac{2H}{{\omega }_{0}}\frac{\mathrm{d}\omega }{\mathrm{d}t}$
式中:$P\text{、}{P}_{\mathrm{m}}\text{、}{P}_{\mathrm{e}}$ 分别为旋转同步电机输出功率、 机械功率、电磁功率;${\omega }_{0}\text{、}\omega$ 分别为初始机械角速度、当前机械角速度。
在直流微电网中, 配置超级电容可以提供模拟惯量所需的动能, 超级电容通过电压变压实现释放/ 吸收电能,其充放电功率${P}_{\mathrm{c}}$
${P}_{\mathrm{c}}= C\frac{{u}_{\mathrm{c}}}{{S}_{\mathrm{c}}}\frac{\mathrm{d}{u}_{\mathrm{c}}}{\mathrm{d}t}$
式中,$C\text{、}{S}_{\mathrm{c}}$${u}_{\mathrm{c}}$ 分别为超级电容储能电容值、额定容量和输出电压。
超级电容储能充/放电可以模拟旋转同步电机转子运动方程, 即联立式(3)和式(4), 可得超级电容惯量支撑方程
$ C\frac{{u}_{\mathrm{c}}}{{S}_{\mathrm{c}}}\frac{\mathrm{d}{u}_{\mathrm{c}}}{\mathrm{d}t}= \frac{2{H}_{\mathrm{c}}}{{\omega }_{\mathrm{c}0}}\frac{\mathrm{d}{\omega }_{\mathrm{c}}}{\mathrm{d}t}$
式中:${H}_{\mathrm{c}}$ 为超级电容储能虚拟惯量;${\omega }_{\mathrm{c}0}$${\omega }_{\mathrm{c}}$ 分别为超级电容储能初始虚拟角频率和当前虚拟角频率,接入运行时${\omega }_{\mathrm{c}0}= {\omega }_{0}\text{、}{\omega }_{\mathrm{c}}= {\omega }_{0}$
为了通过检测直流电压还原微电网频率变化, 本文在新能源储能微电网 DC/AC 换流器中采用直流电压-频率虚拟惯量控制方案, 可表示为
${U}_{\mathrm{{dc}}}= {K}_{\mathrm{{dc}}}\left({\omega -{\omega }_{0}}\right)+ {U}_{\mathrm{{dc}}0}$
式中:${U}_{\mathrm{{dc}}}\text{、}{U}_{\mathrm{{dc}}0}$ 分别为直流母线电压当前值、初始值;$\omega$${\omega }_{0}$ 分别为交流侧电网当前角频率、初始角频率;${K}_{\mathrm{{dc}}}$ 为直流惯量增益。
超级电容储能通过 DC/DC 换流装置与直流母线连接, 其输出电压与直流母线电压一致, 即${u}_{\mathrm{c}}= {U}_{\mathrm{{dc}}}$。联立式(5)和式(6),可得直流虚拟惯量增益为
${K}_{\mathrm{{dc}}}= 2{H}_{\mathrm{c}}\frac{{S}_{\mathrm{c}}{\omega }_{0}}{C{U}_{\mathrm{{dc}}}^{2}{U}_{\mathrm{{dc}}0}}$
在新能源储能微电网 DC/AC 换流器中采用直流电压一频率虚拟惯量控制方案, 可以为电力系统提供惯量支撑, 但负荷投/切造成的频率扰动不尽相同, 设置固定虚拟惯量增益, 无法实现模拟惯量根据频率变化动态响应, 尤其在频率恢复阶段, 较大的虚拟惯量不利于频率恢复。如图2所示, 本文将负荷投入造成的频率下降及频率恢复分为 4 个区域, 区域 I~IV 分别对应频率下降、频率恢复、 频率超调及频率稳定。
为了解决恒定虚拟惯量带来的频率调节不灵活问题, 对传统虚拟惯量控制进行改进。根据频率上升/下降幅度自动增大虚拟惯量,大虚拟惯量可以抑制频率波动 (区域 I 、区域 III ), 在频率恢复阶段减小虚拟惯量, 加快频率恢复(区域 II、区域IV)。调频区域划分及惯量调节如表1所示。
根据表1, 建立新型虚拟惯量为
${H}_{\mathrm{c}}= \left\{\begin{array}{ll}{H}_{\mathrm{c}0}& \left({\left|{\Delta \omega }\right|\leq \alpha }\right)\cap \left({\left|\frac{\mathrm{d}\omega }{\mathrm{d}t}\right|\leq \beta }\right)\\{H}_{\mathrm{c}0}+ {k}_{\mathrm{H}}\left|\frac{\mathrm{d}\omega }{\mathrm{d}t}\right|& \left|{\Delta \omega }\right|> \alpha \end{array}\right.$
式中:${H}_{\mathrm{c}0}$ 为初始虚拟惯量;${\Delta \omega }$ 为虚拟频率调节量;$\alpha$$\beta$ 分别为频率偏差阈值、频率变化率阈值,$\alpha$$\beta$ 取值根据换流器特性决定;${k}_{\mathrm{H}}$ 为虚拟惯量调节系数。
为了对初始虚拟惯量和虚拟惯量调节系数参数进行设定, 本文通过阻尼比进行分析。
直流电压一频率虚拟惯量控制可等效为 1 个典型的二阶传递函数,控制系统阻尼比$\zeta$
$\zeta =\frac{D}{2}\sqrt{\frac{X{\omega }_{0}}{{H}_{\mathrm{c}0}{S}_{\mathrm{c}}{U}_{\mathrm{{dc}}}{U}_{\mathrm{s}}{U}_{0}\cos {\delta }_{0}}}$
式中:$D$ 为虚拟阻尼;$X$ 为交流线路等效电抗;${U}_{\mathrm{s}}$ 为交流线路电压;${U}_{0}$ 为新能源储能微电网$\mathrm{{DC}}/\mathrm{{AC}}$ 换流器交流侧电压;${\delta }_{0}$ 为电压功角初始值,其值近似为 0,则有$\cos {\delta }_{0}\approx 1$
电力系统频率调节时,要求阻尼比$\zeta$ 处于$\lbrack {0.1}$, 0.8],则初始虚拟惯量${H}_{\mathrm{c}0}$ 取值范围为
${0.04}\frac{{D}^{2}X{\omega }_{0}}{{S}_{\mathrm{c}}{U}_{\mathrm{{dc}}}{U}_{\mathrm{s}}{U}_{0}}\leq {H}_{\mathrm{c}0}\leq {0.256}\frac{{D}^{2}X{\omega }_{0}}{{S}_{\mathrm{c}}{U}_{\mathrm{{dc}}}{U}_{\mathrm{s}}{U}_{0}}$
根据式 (8),虚拟惯量调节系数${k}_{\mathrm{H}}$ 满足
$\left|\frac{\mathrm{d}\omega }{\mathrm{d}t}\right|= \frac{{H}_{\text{cmax }}- {H}_{\mathrm{c}0}}{{k}_{\mathrm{H}}}$
式中,${H}_{\text{cmax }}$ 为超级电容储能最大虚拟惯量。
新型虚拟惯量控制系统频率调节过渡到稳态时, 角频率偏差为
$\left|\frac{\mathrm{d}\omega }{\mathrm{d}t}\right|= \Delta {U}_{\mathrm{{dc}}}D $
式中,$\Delta {U}_{\mathrm{{dc}}}$ 为直流母线电压调节量。
将式(11)代入式(12), 可得虚拟惯量调节系数${k}_{\mathrm{H}}$
${k}_{\mathrm{H}}= \frac{{H}_{\mathrm{{cmax}}}- {H}_{\mathrm{c}0}}{\Delta {U}_{\mathrm{{dc}}}}D $
本文提出 1 种根据超级电容储能电压变化范围及最大虚拟惯量, 配置超级电容储能容量方案, 直流母线电压调节范围为
$\sqrt{\frac{4{S}_{\mathrm{c}}{H}_{\mathrm{{cmax}}}\Delta {\omega }_{\min }}{C{\omega }_{0}}+ {U}_{\mathrm{{dc}}0}^{2}}\leq \Delta {U}_{\mathrm{{dc}}}\leq \sqrt{\frac{4{S}_{\mathrm{c}}{H}_{\mathrm{{cmax}}}\Delta {\omega }_{\max }}{C{\omega }_{0}}+ {U}_{\mathrm{{dc}}0}^{2}}$
式中,$\Delta {\omega }_{\min }$$\Delta {\omega }_{\max }$ 分别为虚拟频率最小量、最大量。
根据式(12)可得超级电容储能电容值最优配置公式为
$ C =\frac{4{S}_{\mathrm{c}}{H}_{\mathrm{{cmax}}}\Delta {\omega }_{\max }}{{\omega }_{0}\left({\Delta {U}_{\mathrm{{dcmax}}}^{2}- \Delta {U}_{\mathrm{{dcmin}}}^{2}}\right)} $
式中:$\Delta {U}_{\mathrm{{dc}}\max }^{2}= \frac{4{S}_{\mathrm{c}}{H}_{\mathrm{{cmax}}}\Delta {\omega }_{\max }}{C{\omega }_{0}}+ {U}_{\mathrm{{dc}}0}^{2};\;\Delta {U}_{\mathrm{{dcmin}}}^{2}=$ $\frac{4{S}_{\mathrm{c}}{H}_{\mathrm{{cmax}}}\Delta {\omega }_{\min }}{C{\omega }_{0}}+ {U}_{\mathrm{{dc}}0}^{2}\circ$
根据直流母线电压变化范围及最大虚拟惯量配置超级电容储能电容, 可以在满足虚拟惯量的基础上实现容值最优配置。
新能源储能微电网 DC/AC 换流器面对负荷投/ 切造成的频率波动时, 通过直流侧超级电容储能可以提供暂态虚拟惯量支撑。在频率恢复阶段, 如果波动一直存在, 直流母线电压无法恢复至初始值, 且在低电压/高电压状态下,如果继续投/切负荷, 会因为电压过低/过高,造成新能源储能微电网脱网, 严重影响电力系统稳定运行。为了在频率恢复阶段使直流母线电压快速复原, 本文提出 1 种基于高通滤波器的高频分量惯量模拟方法, 在不改变超级电容储能惯量支撑的基础上, 使新能源储能微电网保持直流母线电压稳定。
在新能源储能微电网 DC/AC 换流器直流电压- 频率虚拟惯量控制系统中采用一阶高通滤波器, 则直流虚拟惯量增益传递函数可表示为
${K}_{\mathrm{{dc}}}\left( s\right)= \frac{{K}_{\mathrm{g}}s}{s +{\omega }_{\mathrm{g}}}$
式中:${K}_{\mathrm{g}}$ 为虚拟惯量高通增益系数;${\omega }_{\mathrm{g}}$ 为高通滤波器截止频率。将式(16)代入式(6), 可得到含有高通滤波环节的虚拟惯量控制方程
${U}_{\mathrm{{dc}}}= \frac{{K}_{\mathrm{g}}s}{s +{\omega }_{\mathrm{g}}}\left({\omega -{\omega }_{0}}\right)+ {U}_{\mathrm{{dc}}0}$
由式 (17)可知, 在惯量控制中引入高频滤波器, 面对频率调节仅处理高频分量, 可以忽略低频分量对直流母线电压调节的影响,即对负荷投/切造成频率波动时, 含有高通滤波环节的虚拟惯量控制系统仅提取交流侧电网角频率${\omega }_{\mathrm{g}}$ 进行惯量调节。 当${\omega }_{\mathrm{g}}$ 趋于稳定,高通滤波器退出,直流母线电压恢复至初始值。联立式(17)和式(7),可得含有高通滤波环节的虚拟转动惯量为
${H}_{\mathrm{{cg}}}= \frac{C{U}_{\mathrm{{dc}}}^{2}{U}_{\mathrm{{dc}}0}}{2{S}_{\mathrm{c}}{\omega }_{0}}\frac{{K}_{\mathrm{g}}s}{s +{\omega }_{\mathrm{g}}}$
参考式(9), 可得引入高通滤波器后直流电压- 频率虚拟惯量控制系统的阻尼比${\zeta }_{\mathrm{H}}$
${\zeta }_{\mathrm{H}}= \frac{D}{2}\sqrt{\frac{X{\omega }_{0}}{{H}_{\mathrm{c}0}{S}_{\mathrm{c}}\left({{U}_{\mathrm{{dc}}}+ {U}_{\mathrm{{dcH}}}}\right){U}_{\mathrm{s}}{U}_{0}\cos {\delta }_{0}}}$
式中,${U}_{\mathrm{{dcH}}}$ 为直流母线电压高通滤波增量。通过调节虚拟惯量高通增益系数可以改变控制系统阻尼比,有效降低频率扰动对直流母线电压干扰。将式(19)控制系统阻尼比${\zeta }_{\mathrm{H}}$ 设为最佳阻尼比,即${\zeta }_{\mathrm{H}}=$ 0.707,此时最优虚拟惯量高通增益${K}_{\mathrm{{gu}}}$
${K}_{\mathrm{{gu}}}= \frac{X{D}^{2}}{2{H}_{\mathrm{c}0}{S}_{\mathrm{c}}{U}_{\mathrm{s}}{U}_{0}\cos {\delta }_{0}}- \frac{{U}_{\mathrm{{dc}}}}{{\omega }_{0}}$
虚拟惯量高通增益最大值${K}_{\text{gmax }}$ 与新能源储能微电网 DC/AC 换流器最大调制比(0.5)及频率波动范围(±1%)有关,可表示为
${K}_{\mathrm{{gmax}}}= \frac{{0.5}- {U}_{\mathrm{{dc}}}/\omega }{0.01}$
根据式(20)和式(21)可以确定电压一频率虚拟惯量高通增益。
本文在新能源储能微电网 DC/AC 换流器外环采用新型虚拟惯量控制, 内环采用电流闭环控制。 虚拟惯量电流内环控制框图如图3所示,其中${I}_{d}^{* }$ 为有功电流参考值。电流内环的给定来自于新型虚拟惯量控制外环的输出$\Delta {U}_{\mathrm{{dc}}}$,电流内环分为${dq}$ 轴电流控制,${I}_{d}$ 对应有功功率,${I}_{q}$ 对应无功功率,在直流微电网中无需考虑无功功率,仅需对${I}_{d}$ 进行控制。忽略电流内环动态响应, 电流内环控制环节可表示为
${I}_{d}= -\left({{K}_{\mathrm{p}}+ \frac{{K}_{\mathrm{i}}}{s}}\right)\left({\Delta {U}_{\mathrm{{dc}}}+ {U}_{\mathrm{{dc}}0}- {U}_{\mathrm{{dc}}}}\right)$
式中,${K}_{\mathrm{p}}\text{、}{K}_{\mathrm{i}}$ 分别为电流内环比例、积分系数。
有功功率电流${I}_{d}$ 在电压矢量下可表示为
${I}_{d}= \frac{{U}_{\mathrm{s}}}{X}\sin \delta $
式中,$\delta$ 为电压功角。联立式(6)、式(22)和式(23), 可得
$\frac{{U}_{\mathrm{s}}}{X}\sin \delta =- \left({{K}_{\mathrm{p}}+ \frac{{K}_{\mathrm{i}}}{s}}\right)\left({{K}_{\mathrm{{dc}}}\Delta {\omega }_{0}+ {U}_{\mathrm{{dc}}0}- {U}_{\mathrm{{dc}}}}\right)$
根据式(24), 得到虚拟惯量控制传递函数为
$\frac{\Delta {U}_{\mathrm{{dc}}}\left( s\right)}{{\Delta \omega }\left( s\right)} =\frac{{U}_{\mathrm{s}}\sin {\delta }_{0}}{X\left({s{K}_{\mathrm{p}}+ {K}_{\mathrm{i}}}\right)} +{K}_{\mathrm{{dc}}}\left( s\right)$
将式(16)代入式(25),可以得到含有高通滤波器的虚拟惯量控制传递函数。在频率波动恢复过程中, 直流母线电压偏差响应曲线如图4所示。
为了验证本文所提新能源储能微电网惯量支撑性能控制策略的有效性, 在 MATLAB/Simulink 中搭建如图1所示的仿真模型。仿真模型中, 直流侧风机、光伏工作于最大功率模式, 交流侧采用模拟输电线路及用电负荷。硬件仿真参数和控制系统参数分别如表2表3所示。
为了验证本文所提新型虚拟惯量支撑方案可以有效提升频率稳定性, 使直流母线电压快速恢复。设置仿真时长为$3\mathrm{\;s}$,仿真工况: 初始负载为${15}\mathrm{{MW}},1\mathrm{\;s}$ 时刻负载功率分别突增${12}\mathrm{{MW}}$$5\mathrm{{MW}}$。负荷突增、突减工况下新能源储能微电网 DC/AC 换流器输出频率对比、直流母线电压对比的波形分别如图5图6所示。
根据图5(a)可知, 当交流侧负荷突增, 会引起新能源储能$\mathrm{{DC}}/\mathrm{{AC}}$ 换流器输出频率突减,通过对比含有储能和不含有储能输出频率波形, 可以得出不含有储能的控制系统无法提供模拟虚拟惯量支撑, 频率下降明显, 且在频率恢复阶段, 因为惯量缺失, 频率存在振荡现象, 频率偏差最大达到${0.22}\mathrm{\;{Hz}}$。在直流侧配置超级电容可以提供惯量支撑, 不论在频率跌落还是恢复阶段, 频率振荡均得到了较好的抑制, 对比传统虚拟惯量控制和本文所提新型虚拟惯量控制, 可以得出新型虚拟惯量控制可以灵活响应因为负荷突增造成的频率波动, 在频率跌落阶段提供大惯量, 相较于传统虚拟惯量控制,频率下跌量减小了${0.13}\mathrm{\;{Hz}}$。在频率恢复阶段减小惯量, 更有利于频率恢复, 相较于传统虚拟惯量控制, 减少了频率振荡频次, 改善了新能源储能 DC/AC 换流器控制系统动态响应性能。
根据图5(b)可知, 负荷突增会引起新能源储能微电网直流母线跌落, 采用新型惯量控制相较于传统惯量控制, 直流母线电压跌落幅度较小, 且可以快速恢复。采用本文所提高通滤波器虚拟的惯量控制系统, 可以使电压恢复至额定, 而传统虚拟惯量控制无法使直流母线恢复至额定, 严重时会造成直流微电网中设备因为电压过低切机。通过高通滤波器提取换流器输出频率进行惯量模拟, 可知当角频率稳定时, 直流母线电压可以恢复至初始值。
根据图6(a)可知, 当交流侧负荷突减, 会引起新能源储能$\mathrm{{DC}}/\mathrm{{AC}}$ 换流器输出频率突增,频率变化率与负荷功率大小有关。没有储能的换流器控制系统无惯量支撑能力, 输出频率无法自动调节, 面对负荷突减, 频率增大幅度较大, 且在频率恢复过程中存在振荡。对比传统虚拟惯量控制, 新型虚拟惯量控制因为引入新的虚拟惯量, 频率增加量减小${0.03}\mathrm{\;{Hz}}$,这是因为调节虚拟惯量自适应根据频率变化及频率变化率自动调节。在频率恢复阶段, 频率超调量得到了较好控制, 无频率振荡现象。可见本文所提新型虚拟惯量控制可以有效提高换流器控制系统对频率扰动的鲁棒性。
根据图6(b)可知, 负荷突减会引起新能源储能微电网直流母线电压上升, 相较于传统虚拟惯量控制, 新型虚拟惯量控制在电压上升阶段, 可以通过惯量调节减少电压上升量, 在电压恢复阶段, 因为采用高通滤波器, 仅响应高频负荷扰动, 可以根据频率恢复程度使直流母线电压恢复至额定, 克服了传统虚拟惯量控制不具备直流母线电压恢复控制的能力。
为了验证新能源储能微电网对系统惯量支撑能力,设置仿真时长为$3\mathrm{\;s}$,仿真工况:$1\mathrm{\;s}$ 时刻, 网侧发生三相电压跌落事故, 三相电压跌落幅度为${50}\%$,系统频率下跌${0.5}\mathrm{\;{Hz}};2\mathrm{\;s}$ 时刻,电压、频率恢复。新能源储能$\mathrm{{DC}}/\mathrm{{AC}}$ 换流器输出电流对比波形如图7所示, 直流母线电压波形如图8所示, 超级电容储能功率变化波形如图9所示。
根据图7可知, 当发生 50%三相电压跌落事故, 会引起电流瞬间增大, 电压恢复后电流存在暂态波动现象。图7(a)中, 采用传统虚拟惯量控制, 电流波动明显, 发生电压跌落后的最大冲击电流达到 2 p.u.,会对设备运行造成影响,故障切除后也无法迅速恢复至额定,电流波动明显;图7(b)中,采用新型虚拟惯量控制, 不论是电压跌落故障发生还是故障切除, 电流波动明显减少, 不会对设备造成过流风险,控制系统鲁棒性较高。
根据图8可知, 网侧电压跌落会引起新能源储能微电网直流母线电压波动, 直流母线电压最大跌落深度为${26}\mathrm{\;V}$,但因为在新能源微电网中配置了超级电容储能设备, 可以在电压跌落时提供暂态电压支撑, 在故障期间维持直流母线电压在额定, 避免直流微电网因为电压过低脱离主网; 在网侧电压恢复阶段, 也可以通过功率变化维持直流母线电压稳定。可见, 在直流微电网内配置超级电容储能, 可以提升整个系统电压抗扰动能力。
根据图9可知, 网侧发生三相电压跌落事故, 为了维持直流母线电压稳定, 超级电容需要吸收微电网内多余功率。在电压跌落期间, 可以通过灵活功率调节实现直流母线电压稳定, 并根据电压调节幅度动态吸收直流侧不平衡功率;在电压恢复阶段, 也可以根据功率缺额, 自动调节超级电容储能出力, 实现电压调节整个阶段功率支撑。可见, 根据电压调节范围配置超级电容储能, 可以实现电压调节阶段超级电容储能充/放电最优。
本文针对新能源储能微电网惯量支撑性能进行研究, 提出了 1 种采用高通滤波器的自适应虚拟惯量控制方法。对传统虚拟惯量控制进行改进, 根据频率变化幅度及变化率,自适应调节虚拟惯量。 根据电压变化范围及最大虚拟惯量, 配置超级电容储能, 提出 1 种采用高通滤波器的虚拟惯量控制方法, 在不改变超级电容储能惯量支撑的基础上, 使新能源储能微电网保持直流母线电压稳定。仿真结果表明, 本文所提新能源储能微电网能控制系统, 针对频率、电压调节动态性能良好, 可以有效提升频率稳定性, 使直流母线电压快速恢复至额定。在新能源微电网中配置超级电容储能设备, 在电压跌落时具备暂态电压支撑能力, 超级电容储能配置合理。本文所提新能源储能微电网策略具有一定的工程应用价值。
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2024年第22卷第4期
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doi: 10.13234/j.issn.2095-2805.2024.4.182
  • 接收时间:2023-05-11
  • 首发时间:2025-07-21
  • 出版时间:2024-07-30
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  • 收稿日期:2023-05-11
  • 修回日期:2023-07-06
  • 录用日期:2023-08-09
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    1 长沙民政职业技术学院 通识教育中心 长沙 410004
    2 西安交通大学 电气工程学院 西安 710049
    3 西安热工研究院有限公司 西安 710054
    4 中国能源建设集团西北电力试验研究院有限公司 西安 710054
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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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