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Article(id=1236714919559745677, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236714913599648374, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202407160, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1720540800000, receivedDateStr=2024-07-10, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772785413875, onlineDateStr=2026-03-06, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772785413875, onlineIssueDateStr=2026-03-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772785413875, creator=13701087609, updateTime=1772785413875, updator=13701087609, issue=Issue{id=1236714913599648374, tenantId=1146029695717560320, journalId=1210938733613449225, year='2025', volume='54', issue='3', pageStart='1', pageEnd='166', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772785412454, creator=13701087609, updateTime=1772785487409, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236715228050813334, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236714913599648374, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236715228050813335, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236714913599648374, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=43, endPage=50, ext={EN=ArticleExt(id=1236714920771899601, articleId=1236714919559745677, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Capacity optimal configuration of new off-grid wind-solar-storage microgrid system based on flywheel energy storage, columnId=1236714914522395257, journalTitle=Thermal Power Generation, columnName=Energy storage technology, runingTitle=null, highlight=null, articleAbstract=

To address the electricity consumption challenges in remote regions beyond the reach of power grid, a novel off-grid microgrid system integrating wind and solar energy with flywheel storage technology is introduced. Research is conducted on the optimal capacity configuration of distributed power sources, and power output models are developed for wind turbines, photovoltaic arrays, energy storage flywheels, and diesel generators. With economic and reliability indicators as objective functions, and environmental protection indicators as constraints, a capacity optimization simulation for the microgrid system is conducted. By employing the improved NSGA-II algorithm, a multi-objective bi-level coordinated optimization of the microgrid system is performed, resulting in a Pareto optimal solution through multi-objective optimization. The TOPSIS method is utilized for decision-making, ultimately identifying the optimal solution tailored for the microgrid system. The data and conclusions obtained from this study have reference value for engineering applications in related fields.

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基于飞轮储能技术的新型离网型风光储微电网系统，对分布式电源进行容量优化配置研究，建立风力发电机、光伏阵列、储能飞轮和柴油发电机相应的功率输出模型；以经济性和可靠性指标作为目标函数，环保性指标作为约束条件，进行微电网系统容量优化仿真，基于NSGA-Ⅱ改进算法对微电网系统进行多目标双层协调优化，得到基于多目标优化的帕累托最优解；采用TOPSIS方法进行方案决策，最终确定符合微网系统的最优方案。研究得到的数据和结论，对相关领域的工程应用具有指导作用。

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李文艳（1965），女，博士，教授，主要研究方向为储能技术，。
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王学申（1980），男，硕士，高级工程师，主要研究方向为电网规划与建设，。

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王学申（1980），男，硕士，高级工程师，主要研究方向为电网规划与建设，。

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王学申（1980），男，硕士，高级工程师，主要研究方向为电网规划与建设，。

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label=Tab.1, caption=

Micro source economic performance parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
项目风力机光伏电池飞轮储能柴油发电机
单机容量3.2 kW250 W5 kW·h待优化
初始投资费用6 800元/台375元/台15 000元/kW600元/kW
运行维护费用5.20元/(kW·a)8.6元/(kW·a)0.60元/(kW·a)0.02元/(kW·h)
置换费用330元/台600元/kW
寿命25 a13 a30 a15 000 h
其他切入风速3 m/s
切出风速18 m/s
额定风速10 m/s		最低转速3 600 r/min
最高转速12 000 r/min
), ArticleFig(id=1236714932251710144, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236714919559745677, language=CN, label=表1, caption=

微源部分经济性能参数

, figureFileSmall=null, figureFileBig=null, tableContent=
项目风力机光伏电池飞轮储能柴油发电机
单机容量3.2 kW250 W5 kW·h待优化
初始投资费用6 800元/台375元/台15 000元/kW600元/kW
运行维护费用5.20元/(kW·a)8.6元/(kW·a)0.60元/(kW·a)0.02元/(kW·h)
置换费用330元/台600元/kW
寿命25 a13 a30 a15 000 h
其他切入风速3 m/s
切出风速18 m/s
额定风速10 m/s		最低转速3 600 r/min
最高转速12 000 r/min
), ArticleFig(id=1236714932369150657, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236714919559745677, language=EN, label=Tab.2, caption=

Optimal solution related objective function indicators

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方案COE/(元·(kW·h)–1)负荷缺电率/%
方案11.411.035 4
方案21.530.370 2
方案31.760.070 5
方案41.780.056 2
方案51.830.032 8
), ArticleFig(id=1236714932474008264, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236714919559745677, language=CN, label=表2, caption=

最优方案相关目标函数指标

, figureFileSmall=null, figureFileBig=null, tableContent=
方案COE/(元·(kW·h)–1)负荷缺电率/%
方案11.411.035 4
方案21.530.370 2
方案31.760.070 5
方案41.780.056 2
方案51.830.032 8
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基于飞轮储能的离网型风光储微电网系统容量优化配置研究
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王学申 1 , 孙然 1 , 许军 1 , 李玉杰 2 , 张彬 3 , 刘畅 3 , 李文艳 2
热力发电 | 储能技术研究 2025,54(3): 43-50
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热力发电 | 储能技术研究 2025, 54(3): 43-50
基于飞轮储能的离网型风光储微电网系统容量优化配置研究
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王学申1 , 孙然1, 许军1, 李玉杰2, 张彬3, 刘畅3, 李文艳2
作者信息
  • 1.国网黑龙江省电力有限公司，黑龙江　哈尔滨　150090
  • 2.华北电力大学能源动力与机械工程学院，北京　102206
  • 3.贝肯新能源有限公司，天津　300000

    • 王学申（1980），男，硕士，高级工程师，主要研究方向为电网规划与建设，。

通讯作者:

李文艳（1965），女，博士，教授，主要研究方向为储能技术，。
Capacity optimal configuration of new off-grid wind-solar-storage microgrid system based on flywheel energy storage
Xueshen WANG1 , Ran SUN1, Jun XU1, Yujie LI2, Bin ZHANG3, Chang LIU3, Wenyan LI2
Affiliations
  • 1.State Grid Heilongjiang Electric Power Company Limited, Harbin 150090, China
  • 2.School of Energy Power and Mechanical Engineering, North China Electric Power University, Beijing 102206, China
  • 3.BC New Energy Co., Ltd., Tianjin 300000, China
出版时间: 2025-03-25 doi: 10.19666/j.rlfd.202407160
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摘要
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基于飞轮储能技术的新型离网型风光储微电网系统，对分布式电源进行容量优化配置研究，建立风力发电机、光伏阵列、储能飞轮和柴油发电机相应的功率输出模型；以经济性和可靠性指标作为目标函数，环保性指标作为约束条件，进行微电网系统容量优化仿真，基于NSGA-Ⅱ改进算法对微电网系统进行多目标双层协调优化，得到基于多目标优化的帕累托最优解；采用TOPSIS方法进行方案决策，最终确定符合微网系统的最优方案。研究得到的数据和结论，对相关领域的工程应用具有指导作用。

飞轮储能  /  微电网技术  /  容量优化配置  /  多目标优化

To address the electricity consumption challenges in remote regions beyond the reach of power grid, a novel off-grid microgrid system integrating wind and solar energy with flywheel storage technology is introduced. Research is conducted on the optimal capacity configuration of distributed power sources, and power output models are developed for wind turbines, photovoltaic arrays, energy storage flywheels, and diesel generators. With economic and reliability indicators as objective functions, and environmental protection indicators as constraints, a capacity optimization simulation for the microgrid system is conducted. By employing the improved NSGA-II algorithm, a multi-objective bi-level coordinated optimization of the microgrid system is performed, resulting in a Pareto optimal solution through multi-objective optimization. The TOPSIS method is utilized for decision-making, ultimately identifying the optimal solution tailored for the microgrid system. The data and conclusions obtained from this study have reference value for engineering applications in related fields.

flywheel energy storage  /  microgrid system  /  capacity optimization allocation  /  multi-objective optimization
王学申, 孙然, 许军, 李玉杰, 张彬, 刘畅, 李文艳. 基于飞轮储能的离网型风光储微电网系统容量优化配置研究. 热力发电, 2025 , 54 (3) : 43 -50 . DOI: 10.19666/j.rlfd.202407160
Xueshen WANG, Ran SUN, Jun XU, Yujie LI, Bin ZHANG, Chang LIU, Wenyan LI. Capacity optimal configuration of new off-grid wind-solar-storage microgrid system based on flywheel energy storage[J]. Thermal Power Generation, 2025 , 54 (3) : 43 -50 . DOI: 10.19666/j.rlfd.202407160
正文
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我国幅员辽阔并有漫长的边境线，且有大量的偏远农村、农场（林场）、通信基站、边防哨所不能与主干电网相连，导致存在大量的无电地区。急需开发一套具备快速部署、环保高效的离网型微电网系统，解决边远地区的供电难题，提高社会效益[1-4]。近年来，国内外纷纷开展了微电网技术的研究，在离网型风光储微电网系统研究中，整体系统的容量优化是一个重要的研究方向[5]。合理分布配置风电、光伏、储能装置的容量，使系统在满足可靠性约束的前提下，降低综合成本成为容量优化配置的目标[6]。对于离网型风光储微电网系统容量优化，最开始的研究只是根据国外相关案例进行简单的容量拼凑组合，并没有结合案例当地的自然气象数据来考虑各个微源的出力情况，也没有考虑风机、光伏和储能等模型的相互配合，导致其经济性和稳定性都不理想[7-8]。Meng等人[9]提出以最小年化投资和运行成本低为优化目标的微电网系统容量配置优化模型。Huang等人[10]通过建立离网型微电网时间序列模型，综合考虑储能系统和风电的输出特性，运用负荷跟随策略和储能系统控制策略，建立了微电网运行经济性评价算法，计算并对比了不同容量配置下的经济性。Gao等人[11]提出了风光储并网混合交直流微电网系统，建立了以降低微电网运行成本为目标的模型，给出其容量配置的最优方案。Shahnia[12]对偏远地区微电网进行了小信号稳定性分析，验证了系统拓扑结构对该微电网系统稳定性的影响。
目前，相关研究均选取电化学储能进行配置，大部分未采用真实的风、光数据。本文基于飞轮储能（安全、环保）进行微电网系统容量优化配置的研究，并以大兴安岭地区实际的风光数据作为分析的对象，首先构建了基于飞轮储能的离网型风光储微电网系统；其次建立风力发电机、光伏阵列、飞轮储能和柴油发电机相应的功率输出模型和微电网优化配置模型[13-16]；最后基于NSGA-Ⅱ改进算法和CPLEX编码器对微电网系统进行多目标双层协调优化，得到基于多目标优化的帕累托最优解，采用TOPSIS方法进行方案决策，最终确定符合微网系统的最优方案。以大兴安岭地区实际气象数据进行算例分析，得出微网系统中风机、光伏阵列、飞轮储能的最优方案配置，验证了模型和算法的合理性。本文研究得到的数据和结论，对相关研究和工程应用具有指导作用。
1　离网型微电网系统的构建和微源出力模型
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微电网系统集成了风电和光伏发电、柴油发电机、飞轮储能及负荷，整个系统通过源荷储之间的协调运行来合理利用能源，满足负荷需要。风电和光伏输出的功率与负荷需要的功率不可能完全匹配，因此需要通过飞轮储能来实现能量的时移和功率调控。微电网系统拓扑结构如图1所示。
1.1　飞轮储能系统的原理
飞轮储能是通过飞轮转子旋转时产生的转速变化带动电机进行能量转换。在电机转速持续上升的过程中存储能量，此时为充电状态；在电机转速持续下降的过程中释放能量，此时为放电状态。当交流母线与飞轮电机之间没有能量交换时，转子在真空环境中自由旋转，处于待机状态[17]。飞轮存储的能量大小与飞轮转子的转动惯量和转速有关。
E=12Jω2
式中：E为飞轮所存储的能量，J；J为飞轮转子的转动惯量，kg·m2；ω为飞轮转子的工作转速，rad/s。
由式(1)可知，飞轮系统本身所存储能量取决于工作转速，因此通过计算差值可以得出飞轮储能充放电的能量Emax。
Emax=12J(ωmax2−ωmin2)
式中：ωmax为飞轮转子允许的最大工作转速，rad/s；ωmin为飞轮转子允许的最小工作转速，rad/s。
1.2　各微源出力模型
风力发电机模型功率输出与风速可表示为[6]：
Pwt(t)={0v(t)＜vi|v(t)＞vcPr(v(t)2－vi2)(vr2－vi2)vi≤v(t)＜vrPrvr≤v(t)≤vc
式中：vi为风力机的切入风速，m/s；vc为风力机的切出风速，m/s；vr为风力机的额定风速，m/s；Pr为风力机的额定输出功率，kW。
辐照度与光伏输出功率之间的数学关系[18-19]，具体表达式为：
PPV(t)=fPVPSTCGc(t)GSTC[1+k(Tc(t)−TSTC)]
式中：fPV为功率降额因数，一般取0.9；PSTC为标准条件下的额定输出功率，kW；GSTC为标准条件下太阳辐照度，W/m2；Gc(t)为t时刻实际太阳辐照度，W/m2；k为功率温度系数；Tc(t)为t时刻工作点温度，℃；TSTC为标准测试条件下温度，℃。
规定飞轮储能充电状态时功率为负值；放电状态时功率为正值。飞轮实时的荷电状态为：
SOCt=SOC0−∫0tPtFdtERatedF
式中：SOCt为飞轮储能在t时刻的荷电状态；SOC0为初始时刻飞轮储能的荷电状态；ERatedF为飞轮储能存储的总能量；PtF为飞轮储能输出功率。
柴油发电机输出功率主要和消耗柴油量有关，可表示为[20]：
F=f0Rgen+f1Pgen
式中：F为柴油机消耗柴油耗量，L；f0、f1为燃料曲线截距系数；Rgen为柴油机额定容量，kW；Pgen为柴油机实际输出功率，kW。
微电网系统正常工作时任意时刻都应当满足系统有功功率的平衡，当风电和光伏输出的功率之和大于负荷需求时，多余能量存入飞轮；当风电和光伏输出的功率之和小于负荷需求时，储能释放能量补充缺额功率；当风电、光伏、储能三者输出的功率之和无法满足重要负荷需求时，启动备用柴油发电机，补充缺额功率。
PW(t)+PPV(t)+PFd(t)+PG(t)+Ploss(t)=Pload(t)+PFc(t)
式中：PW(t)为风电在t时刻输出的功率；PPV(t)为光伏在t时刻输出的功率；PG(t)为柴油机在t时刻输出的功率；PFd(t)为飞轮储能在t时刻的放电功率；PFc(t)为飞轮储能在t时刻的充电功率；Pload(t)为用户负荷在t时刻需要的功率；Ploss(t)为当微源不能满足负荷时在t时刻的功率缺额。
2　微电网系统容量优化配置模型
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2.1　目标函数
以经济性和可靠性指标作为目标函数。一方面是保证微电网系统的综合成本最低；另一方面是保证系统的稳定可靠性最高，即年负荷缺电率最低。目标函数为：
F=min(NPC, LPSP)
式中：NPC为总净现存成本；LPSP为负荷缺电率（load power shortage probability，LPSP）。
NPC表示系统的生命周期成本，为项目工程生命周期内可能发生的所有成本和收入的总和。则NPC的函数表达式为[21]：
NPC=Ctz+Cwh+Cth+Cfuel+Cpf−Cs
式中：Ctz为初始设备投资成本；Cwh为运行和维护成本；Cth为设备替换成本；Cfuel为燃料消耗成本；Cpf为柴油发电机污染排放折算成本；Cs为可再生能源发电补贴。
采用年负荷缺电率对系统的整体可靠性进行评估，其函数表达式为：
LPSP=∑t=18 760Ploss(t)∑t=18 760Pload(t)
2.2　约束条件
微电网系统作为一个需要时刻动态平衡的系统，需要制定一些约束条件，其仿真结果才能更贴近实际工程应用。
各个微源有功功率出力的上下限为：
0≤Pi≤Pimax
式中：Pi为i种微源有功出力；Pimax为i种微源有功出力上限。
各个微源数量上下限为：
0≤Ni≤Nimax
式中：Ni为第i种微源的使用数量；Nimax为第i种微源的最大使用数量。
飞轮储能转速约束及荷电状态约束为：
ωmin≤ω(t)≤ωmax
SOCmin≤SOC(t)≤SOCmax
式中：SOCmin为飞轮储能的最小荷电状态约束；SOCmax为飞轮储能的最大荷电状态约束。
2.3　微电网系统运行策略
本文采用的控制策略为优先使用可再生能源风力、光伏发电，其次利用飞轮储能系统放电，当不能满足负荷需求时，启用柴油发电机作为备用电源保障供电，微网系统运行策略如图2所示。当风光资源过剩时给飞轮充电，通过储能手段提高对新能源的利用。当负荷需求较大时，需要配置容量较大的飞轮储能，虽然这种配置前期投资较大，但减少了柴油发电机的运行时间，方案设计更加绿色环保。
2.4　基于算法优化的微电网系统多目标容量配置
采用双层优化配置模型对微源容量进行分析和求解，上层优化通过NSGA-Ⅱ改进算法在分析用户侧电负荷需求的基础上，结合风力、光伏等可再生能源出力特性，对各个微源的装机容量进行优化配置，从而得到各个微源的容量配置组合；下层优化主要借助CPLEX编码器对上层得到的微源容量以典型日形式进行日调度出力优化。CPLEX编码器可以在MATLAB程序中直接调用工具包，以实现多目标问题的最优规划，具有解决较为复杂优化问题的能力。将24 h典型日计算得出的每时段微源出力传递回上层优化模型，得出优化后的微源出力配置方案，优化流程如图3所示。
NSGA-Ⅱ算法主要是通过对帕累托最优解进行分层迭代，然后进行非支配排序的一种遗传算法[22]。在借助NSGA-Ⅱ多目标算法优化容量问题后，其结果会输出符合当前目标函数的多个最优解，共同组成了帕累托最优解集[23-24]。
通过上述优化算法所求得的解是由多个非支配解组成的帕累托解集，这就需要解决多目标方案决策的问题。采用TOPSIS方法来解决配置方案决策问题，是一种逼近于目标函数到理想解的多目标决策方法。首先需要对决策数据进行归一化处理，确定理想解和负理想解；然后通过比较决策方案到2个理想化目标的距离，对比不同方案到理想解的相对接近程度，当偏离各目标函数最近时即为最佳决策方案[25]。
3　算例分析
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选定大兴安岭地区某林场作为项目实施地点，调取所在地自然气象数据，平均风速为5.4 m/s，平均光照强度为3.62 kW·h/(m2·d)。通过软件拟合得到该地区近一年的风速和光照强度预测数据，如图4所示。
由图4可见，风速全年呈现随机性分布，光照资源则有着较为明显的规律，主体为夏季辐射量强、冬季辐射量短的特征，同时冬季平均日照时长也少于夏季，但夏季晴空指数偏低，其指标也影响着光伏发电系统的效率。
微电网系统主要为该林场的工作生活区供电，具体负荷如图5所示。由图5可知，用电负荷高峰时段主要集中在12:00与18:00，早晚为用电平峰期。
3.1　参数设定
设定系统的正常寿命周期为25 a，年负荷缺电率为5.0%，其经济性能参数具体见表1。
3.2　仿真分析
利用MATLAB软件对微电网系统进行仿真求解，得出的帕累托解集前沿分布趋势如图6所示。
由图6可见，得到64组对应于经济性和可靠性目标的最优解组成了帕累托最优解集，其中每个解都对应着一组最优方案。其解集在2个目标函数的共同影响下，结果呈现出一定的互斥性，迭代在右下侧的解集可靠性指标更小，则对应的决策方案可靠稳定性优于其经济性，同理在左上侧的解集对应的方案经济性在综合系统中优势更加明显。
通过借助CPLEX编码器对算法得到的微源容量以典型日形式进行日调度出力优化，其结果如图7所示。
应用TOPSIS方法对符合各个目标的帕累托最优解进行方案决策。根据求解的理想解情况计算64组方案分别与理想解的贴近程度，最终通过距离贴近程度确定不同赋值下的最优配置方案，各方案的平均发电成本和负荷缺电率见表2。
最优方案对应的微源容量配置如图8所示。由图8可见：方案1到方案5由经济性目标向可靠性目标倾斜，方案1与方案5每度电的发电成本对比相差0.42元，同时负荷缺电率升高1.0%，2种方案都是只考虑了经济性或可靠性的单目标方案决策，没有将2个目标的权重结合到一起；方案2比方案1增加了对光伏的容量配置，使系统在以经济性为主体的前提下降低了超过0.6%的缺电率，兼顾了系统的整体稳定性；方案3则是将2个目标的权重兼顾且权重相同，在适当提升发电成本同时，将负荷缺电率降到0.07%，此时微电网系统的经济性与可靠性两方面都较为突出；方案4则在方案3基础上继续增大对可靠性指标的赋权，在容量配置方面就体现为提升对光伏的出力配置，进一步提高了系统稳定性；方案5只以可靠性指标为目标函数进行优化，其光伏出力配置占比是5个系统中最高的，虽然年负荷缺电率为方案中最优的，但其得到的综合总成本也是最高的。
综上所述，针对该林场的实际情况适合选择方案4作为最终的最优方案，即微网系统中风机、光伏阵列、飞轮储能的最优方案出力配置分别为22 kW、14 kW和30 kW·h；此时系统平均发电成本为1.78元/(kW·h)，年负荷缺电率为0.06%。作为离网式风光储微电网系统，在考虑经济性的前提下最大限度的保障了当地用户的用电质量，为分散的负荷提供了稳定的绿色供电系统。
4　结语
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边远地区由于地理位置偏僻和经济发展不充分，这些区域电力容量需求往往十分有限，难以支撑传统电网的建设投资和运维成本，尤其针对林场等对防火要求极高的场所其初装以及后期维护投资更大，还面临着传统电网的建设技术难题，导致我国还存在大量的无电地区，严重影响这些地区经济社会发展。
本文以大兴安岭地区某林场为案例进行仿真分析，通过不同方案对比分析得出适合该场景的最优配置方案。对离网型风光储微电网的系统组成进行结构分析，建立风力机、光伏阵列、飞轮储能和柴油发电机各个微源的功率输出模型。建立了离网型风光储微电网系统的单目标和多目标容量优化模型，设计了微网系统的运行策略。基于NSGA-Ⅱ改进算法和CPLEX编码器，设计并优化了微电网系统多目标协调双层优化配置流程，再通过对符合系统平均发电成本最小和年负荷缺电率最低2个目标的帕累托最优解采用TOPSIS方法进行方案决策，最终得到微电网系统容量优化配置方案。验证了本文方法与模型的有效性与可行性。
随着风力、光伏等可再生能源发电技术的快速发展，其建设成本大幅下降，边远地区拥有着大量的风能和太阳能资源，但其本身的间歇性与波动性导致供电可靠性低。基于飞轮储能技术的离网型风光储微电网系统，安全环保，可以解决边远地区的供电难题。
基金
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  • 国家电网黑龙江省电力公司科技资金资助
文献
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doi: 10.19666/j.rlfd.202407160
  • 接收时间：2024-07-10
  • 首发时间：2026-03-06
  • 出版时间：2025-03-25
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  • 收稿日期：2024-07-10
基金
Science and Technology Fund of State Grid Heilongjiang Electric Power Company
国家电网黑龙江省电力公司科技资金资助
作者信息
    1.国网黑龙江省电力有限公司，黑龙江　哈尔滨　150090
    2.华北电力大学能源动力与机械工程学院，北京　102206
    3.贝肯新能源有限公司，天津　300000

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李文艳（1965），女，博士，教授，主要研究方向为储能技术，。
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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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