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Article(id=1236345821202010817, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, articleNumber=null, orderNo=null, doi=10.19666/j.rlfd.202402040, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1708358400000, receivedDateStr=2024-02-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1772697413968, onlineDateStr=2026-03-05, pubDate=1729785600000, pubDateStr=2024-10-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772697413968, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772697413968, creator=13701087609, updateTime=1772697413968, updator=13701087609, issue=Issue{id=1236345813933289655, tenantId=1146029695717560320, journalId=1210938733613449225, year='2024', volume='53', issue='10', pageStart='1', pageEnd='162', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772697412236, creator=13701087609, updateTime=1772697498476, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236346175725556508, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236346175725556509, tenantId=1146029695717560320, journalId=1210938733613449225, issueId=1236345813933289655, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=66, endPage=71, ext={EN=ArticleExt(id=1236345821533360849, articleId=1236345821202010817, tenantId=1146029695717560320, journalId=1210938733613449225, language=EN, title=Economic study on wind-solar-storage systems technology considering time-of-use electricity prices, columnId=1236345815061557435, journalTitle=Thermal Power Generation, columnName=Long-term energy storage technology, runingTitle=null, highlight=null, articleAbstract=

Based on the time-of-use electricity price and the cost of wind-PV-energy storage system, technical and economic research of source-grid-load-storage system is studied. Firstly, a microgrid system model integrating renewable energy and energy storage system is proposed, which includes PV, wind power, energy storage system, grid, and load. Then, under the premise of ensuring reliable power supply to the load, an optimization model of the source-grid-load-storage system is established with the goal of optimizing the system economy based on load data, irradiation data, wind speed data, time-of-use electricity price data, and the costs of each unit of the system. Finally, the optimal capacity and economic feasibility of configuring a wind-PV-storage system in a certain region are analyzed in detail through a numerical example. The analysis results indicate that, the energy storage systems store energy at low electricity prices and release energy at high electricity prices, thereby avoiding users from purchasing electricity from the grid at high electricity prices and reducing the cost of purchasing electricity from the grid. The configuration of a wind-PV-energy storage system can effectively reduce the annual cost of purchasing electricity from the grid.

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基于分时上网电价和风光储系统成本对源-网-荷-储系统的技术经济性开展了研究。首先，提出了一种集成可再生能源和储能的微电网系统模型，该系统包括光伏、风电、储能、电网和用户负荷；然后，在满足对负荷可靠供电的前提下，根据负荷数据、辐照数据、风速数据、分时电价数据以及系统各单元的成本，以系统经济性最优为目标建立了源-网-荷-储系统的优化模型；最后，通过算例详细分析了某地区配置风光储系统的最优容量以及系统的经济性。分析结果表明，储能系统通过在低电价时储能，在高电价时释能，从而避免用户在高电价时从电网购电，进而降低用户从电网的购电成本。

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张新敬（1981），男，博士，研究员，主要研究方向为大规模压缩空气储能技术研发、储能与可再生能源耦合系统分析与实验研究，。
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王来增（1981），男，高级工程师，主要研究方向为新能源，。

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王来增（1981），男，高级工程师，主要研究方向为新能源，。

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王来增（1981），男，高级工程师，主要研究方向为新能源，。

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tableContent=null), ArticleFig(id=1236345827883536497, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=CN, label=图8, caption=系统各单元年发电量占比, figureFileSmall=zzpAkyR99o0/ZfPb8cE16Q==, figureFileBig=cVjzi16hHUmPPCBG88bmlg==, tableContent=null), ArticleFig(id=1236345827988394102, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=EN, label=Tab.1, caption=

The time-of-use electricity prices in the area

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时段电价/(元·(kW·h)–1)
00:00—08:000.374
08:00—09:000.870
09:00—11:001.266
11:00—13:000.374
13:00—17:001.266
17:00—22:000.870
22:00—24:000.374
), ArticleFig(id=1236345828063891578, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=CN, label=表1, caption=

该地分时电价

, figureFileSmall=null, figureFileBig=null, tableContent=
时段电价/(元·(kW·h)–1)
00:00—08:000.374
08:00—09:000.870
09:00—11:001.266
11:00—13:000.374
13:00—17:001.266
17:00—22:000.870
22:00—24:000.374
), ArticleFig(id=1236345828193915010, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=EN, label=Tab.2, caption=

Parameters of each unit in the system

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项目数值项目数值
风电系统单价/(元·kW–1)6 500锂电池充电效率/%95
风电系统寿命/a20锂电池放电效率/%95
光伏系统单价/(元·kW–1)4 000锂电池寿命/a20
光伏逆变器效率/%98储能PCS单价/(元·kW–1)200
光伏系统寿命/a20储能PCS充电效率/%98
光伏系统容配比1.1储能PCS放电效率/%98
锂电池单价/(元·(kW·h)–1)1 500储能PCS寿命/a20
系统规划年限/a20
), ArticleFig(id=1236345828302966919, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=CN, label=表2, caption=

系统各单元参数

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项目数值项目数值
风电系统单价/(元·kW–1)6 500锂电池充电效率/%95
风电系统寿命/a20锂电池放电效率/%95
光伏系统单价/(元·kW–1)4 000锂电池寿命/a20
光伏逆变器效率/%98储能PCS单价/(元·kW–1)200
光伏系统寿命/a20储能PCS充电效率/%98
光伏系统容配比1.1储能PCS放电效率/%98
锂电池单价/(元·(kW·h)–1)1 500储能PCS寿命/a20
系统规划年限/a20
), ArticleFig(id=1236345828596568205, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=EN, label=Tab.3, caption=

Optimization results

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组成部分配置容量数值
光伏部分配置容量/ kW4 600
风电部分配置容量/ kW12 000
锂电池储能部分配置容量/ (kW·h)24 000
储能PCS部分配置容量/ kW7 000
), ArticleFig(id=1236345828688842896, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=CN, label=表3, caption=

优化结果

, figureFileSmall=null, figureFileBig=null, tableContent=
组成部分配置容量数值
光伏部分配置容量/ kW4 600
风电部分配置容量/ kW12 000
锂电池储能部分配置容量/ (kW·h)24 000
储能PCS部分配置容量/ kW7 000
), ArticleFig(id=1236345828793700499, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=EN, label=Tab.4, caption=

Annual cost composition of the system

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系统组成初始投资
年成本		运行维护
年成本		购电
年成本		年总成本
锂电池313.272.0385.2
光伏160.136.8196.9
风电678.6156.0834.6
电网618.7618.7
储能PCS12.22.815.0
合计1 164.1267.6618.72 050.3
), ArticleFig(id=1236345828890169495, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=CN, label=表4, caption=

系统年总成本构成

, figureFileSmall=null, figureFileBig=null, tableContent=
系统组成初始投资
年成本		运行维护
年成本		购电
年成本		年总成本
锂电池313.272.0385.2
光伏160.136.8196.9
风电678.6156.0834.6
电网618.7618.7
储能PCS12.22.815.0
合计1 164.1267.6618.72 050.3
), ArticleFig(id=1236345829011804318, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=EN, label=Tab.5, caption=

Annual power generation and its proportion of each unit in the system

, figureFileSmall=null, figureFileBig=null, tableContent=
系统构成年产电量/((kW·h)·a–1)占比/%
光伏5 714 78511.2
风电33 935 45266.4
电网11 467 21322.4
合计51 117 451100.0
), ArticleFig(id=1236345829087301797, tenantId=1146029695717560320, journalId=1210938733613449225, articleId=1236345821202010817, language=CN, label=表5, caption=

系统各单元年发电量及其占比

, figureFileSmall=null, figureFileBig=null, tableContent=
系统构成年产电量/((kW·h)·a–1)占比/%
光伏5 714 78511.2
风电33 935 45266.4
电网11 467 21322.4
合计51 117 451100.0
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考虑分时电价的风光储系统技术经济性研究
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王来增 1 , 冯璐 2 , 张新敬 3
热力发电 | 长时储能技术研究专题 2024,53(10): 66-71
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热力发电 | 长时储能技术研究专题 2024, 53(10): 66-71
考虑分时电价的风光储系统技术经济性研究
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王来增1 , 冯璐2, 张新敬3
作者信息
  • 1.中国电力国际发展有限公司，北京　100080
  • 2.天津电气科学研究院有限公司，天津　300171
  • 3.中国科学院工程热物理研究所，北京　100190

    • 王来增（1981），男，高级工程师，主要研究方向为新能源，。

通讯作者:

张新敬（1981），男，博士，研究员，主要研究方向为大规模压缩空气储能技术研发、储能与可再生能源耦合系统分析与实验研究，。
Economic study on wind-solar-storage systems technology considering time-of-use electricity prices
Laizeng WANG1 , Lu FENG2, Xinjing ZHANG3
Affiliations
  • 1.China Power International Development Limited, Beijing 100080, China
  • 2.Tianjin Research Institute of Electric Science Co., Ltd., Tianjin 300171, China
  • 3.Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
出版时间: 2024-10-25 doi: 10.19666/j.rlfd.202402040
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摘要
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基于分时上网电价和风光储系统成本对源-网-荷-储系统的技术经济性开展了研究。首先，提出了一种集成可再生能源和储能的微电网系统模型，该系统包括光伏、风电、储能、电网和用户负荷；然后，在满足对负荷可靠供电的前提下，根据负荷数据、辐照数据、风速数据、分时电价数据以及系统各单元的成本，以系统经济性最优为目标建立了源-网-荷-储系统的优化模型；最后，通过算例详细分析了某地区配置风光储系统的最优容量以及系统的经济性。分析结果表明，储能系统通过在低电价时储能，在高电价时释能，从而避免用户在高电价时从电网购电，进而降低用户从电网的购电成本。

可再生能源  /  储能技术  /  容量优化  /  分时电价

Based on the time-of-use electricity price and the cost of wind-PV-energy storage system, technical and economic research of source-grid-load-storage system is studied. Firstly, a microgrid system model integrating renewable energy and energy storage system is proposed, which includes PV, wind power, energy storage system, grid, and load. Then, under the premise of ensuring reliable power supply to the load, an optimization model of the source-grid-load-storage system is established with the goal of optimizing the system economy based on load data, irradiation data, wind speed data, time-of-use electricity price data, and the costs of each unit of the system. Finally, the optimal capacity and economic feasibility of configuring a wind-PV-storage system in a certain region are analyzed in detail through a numerical example. The analysis results indicate that, the energy storage systems store energy at low electricity prices and release energy at high electricity prices, thereby avoiding users from purchasing electricity from the grid at high electricity prices and reducing the cost of purchasing electricity from the grid. The configuration of a wind-PV-energy storage system can effectively reduce the annual cost of purchasing electricity from the grid.

renewable energy  /  energy storage technology  /  capacity optimization  /  time-of-use electricity price
王来增, 冯璐, 张新敬. 考虑分时电价的风光储系统技术经济性研究. 热力发电, 2024 , 53 (10) : 66 -71 . DOI: 10.19666/j.rlfd.202402040
Laizeng WANG, Lu FENG, Xinjing ZHANG. Economic study on wind-solar-storage systems technology considering time-of-use electricity prices[J]. Thermal Power Generation, 2024 , 53 (10) : 66 -71 . DOI: 10.19666/j.rlfd.202402040
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可再生能源近年来发展迅速，在应对全球气候变化、保障电力供应安全和确保经济社会持续快速发展方面具有重要意义[1]。但由于其自身具有随机性、波动性和间歇性的特点，大规模接入电网会对电力系统运行产生不利影响。通过构建大规模耦合储能的可再生能源系统，利用储能系统对可再生能源存储和释放来平抑可再生能源的波动性和间歇性，提高可再生能源并网率并促进可再生能源的发展[2]。加入储能系统将增加投资成本，为了降低成本，有必要开展储能与可再生能源耦合系统的优化研究[3]。文献[4]指出增加储能系统可以最大限度地减少分布式可再生能源发电规模，并降低整个系统的投资成本。文献[5]考虑了ESS投资和运营年份对产能分配的影响，结果表明储能成本的降低和储能寿命的延长将提高系统的最优配置能力。文献[6]提出了一种基于可再生能源并网的分布式发电系统，通过仿真确定最优光伏和风电机组容量，以最小化分布式发电系统的生命周期成本，结果表明当平均风速在4.8 m/s以上时，利用风力发电机组作为可再生能源发电机组具有较好的经济效益。文献[7]考虑到太阳辐射和风速的不确定性，应用混合整数线性规划（MILP）来解决工业分时电价（TOU）用户BESS的最优调度问题。文献[8]和文献[9]分别以系统年投资最小和年电力收入最大为优化目标，优化了具有耦合储能的可再生能源系统的容量。文献[10]提出了生产过程中单机生产调度能耗成本最小化的数学模型，采用遗传算法来获得最优解，结果表明通过避免高能源价格时期生产可以显著降低能源成本。文献[11]以可再生能源和电池耦合系统并网的电力成本最小为优化目标，对风电和电池容量进行了敏感性分析，结果表明在分时电价下，夏季典型日电费可降低54.0%，而在冬季典型日电费可降低0.7%，通过优化调度每年可节省28.1%的电力成本。文献[12]为了使BESS的综合效益最大化，建立了同时考虑BESS容量配置和运行调度的多目标双层优化模型，采用基于信息熵法和二阶锥松弛法的多目标粒子群优化算法求解优化问题。规划和运行问题在风光储系统的优化配置中具有相互影响的作用，可通过将系统的容量配置模型和调度模型相结合进行优化[13]。文献[14]利用粒子群优化算法研究了可再生能源和电池系统耦合的优化运行策略，以最小化能源成本，结果表明在不同的电价方案下，该运营框架能够降低12.1%~58.3%的能源成本。文献[15]建立了PV时移和套利模型，在低电价时储能系统从光伏发电场或电网充电，在高电价时储能系统向电网放电，储能系统通过储释能电价差获得收益。
目前，大部分研究仅针对典型日的负荷数据、电价数据、风速数据和辐照数据进行分析，研究结果具有局限性；且仅对电价套利下储能系统的容量优化和技术经济性进行了研究；或是仅针对风光储微电网系统进行容量优化配置。针对以上问题，本文建立了一种将分布式风电、分布式光伏、锂电池储能系统和电网集成的源-网-荷-储模型；在保证储能系统充放电功率约束、储能系统荷电状态约束、风电和光伏发电功率约束的前提下，分析了风光储系统的经济效益；并在综合考虑风光储系统的投资成本、从电网购电成本及当地可再生能源资源和用户负荷等因素的影响，以风光储系统全生命周期年综合成本最低为优化目标，建立了源-网-荷-储系统优化模型和储能系统的充放电过程运行控制策略。
1　源-荷-储-网系统结构
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图1为本文提出的源-荷-储-网系统结构，该系统包含分布式风电、分布式光伏、锂电池储能系统、储能电力控制系统（PCS）以及用户负荷。目前，用户通过从电网购电来满足负荷需求，本研究通过给用户配置风光储系统来降低电网的购电成本，提高系统的经济性。源-荷-储-网系统工作过程为：白天低电价时，光伏系统、风电系统和电网共同给负荷供电，光伏系统、风电系统多余的电储存在储能系统中；白天高电价时，光伏系统、风电系统和储能系统共同给负荷供电，降低高电价时从电网的购电成本；晚上高电价时，储能系统储存的多余电能和风电系统、电网共同给负荷供电。
2　系统成本分析
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2.1　风光储系统初始投资年成本
风光储系统总成本折合年度成本的计算方法为：
Ccap,a=fwind,crPwind,rateCwind+fpv,crPpv,rateCpv+           fba,cr（Eba,rateCba,c+Pba,rateCba,p）
fcr=i(1+i)T(1+i)T−1
式中：Pwind,rate为风电功率，kW；Cwind为风电的单位成本，元/kW；fwind,cr为风电的折旧系数；Ppv,rate为光伏功率，kW；Cpv为光伏的单位成本，元/kW；fpv,cr为光伏的折旧系数；Eba,rate为电池的容量，kW·h；Cba,c为电池的容量成本，元/(kW·h)；Pba,rate为电池的功率，kW；Cba,p为电池的功率成本，元/kW；fba,cr为电池的折旧系数；i为折现率；T为系统的运行年限，年[16-19]。
2.2　风光储系统运行维护年成本
系统运行维护年成本按初始投资成本的2%计算[19]。
CO&M,a=0.02×（Pwind,rateCwind+Ppv,rateCpv+              Eba,rateCba， c+Pba,rateCba， p）
2.3　风光储系统替换年成本
CR,a,j=fcr⋅∑i=1r(1+i)−rτ⋅CR,j
CR,a=∑i=1jCR,a,j
式中：CR,j为设备j替换1次的费用，元；CR,a,j为设备j折算到每年的替换费用，元/a；r为设备j的替换次数；τ为替换间隔，年。
2.4　从电网购电年成本
电价低谷时，储能系统存储来自电网，年购电成本为：
Cgrid,a=∑i=1k(Pi×δ×EPi)
式中：Pi为风光储系统在第i时段从电网的取电功率，kW；δ为时间步长，取1 h；EPi为第i时段的上网电价，元/(kW·h)。
3　系统优化
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3.1　优化目标函数
源-荷-储-网系统的成本包括风光储系统的初始投资成本、运行维护成本、替换成本，以及从电网的购电成本。以源-荷-储-网系统年综合成本最小为目标函数对系统进行优化。优化目标函数为：
Ca=Ccap,a+CO&M,a+CR,a+Cgrid,a
3.2　相关约束
1）实时功率平衡约束
Pl,t=Pw,t+Ppv,t+Pba,t+Pgrid,t
式中：Pl,t、Pw,t、Ppv,t、Pba,t、Pgrid,t分别为时刻t下负荷需求功率、风力发电功率、光伏发电功率、储能系统充放电功率（放电功率为正，充电功率为负）以及从电网取电功率，kW。
2）释能过程功率约束
Psi,td≤Usi,tdPsid¯
式中：Psi,td为储能系统在时段t的释能功率，kW；Psid¯为释能过程最大释能功率，kW；Usi,td为释能时的运行状态，释能时取1，否则取0。
3）释能过程荷电状态约束
Esi,t-1−1/ηsid×t×Psi,td≥E¯si
式中：Esi,t为储能系统在时段t已存储的容量，kW·h；E¯si为储能系统最小存储容量，kW·h；ηsid为释能效率，%。
4）储能过程功率约束
Psi,tc≤Usi,tcPsic¯
式中：Psi,tc为储能系统在时段t的储能功率，kW；Psic¯为储能过程最大储能功率，kW；Usi,tc为储能时的运行状态，储能时取1，否则取0。
5）储能过程荷电状态约束
Esi,t-1+ηsic×t×Psi,tc≤E¯si
式中：E¯si为储能系统最大存储容量，kW·h；ηsic为储能效率，%。
4　优化结果及分析
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本研究根据某地区的历史负荷数据、历史辐照数据、历史风速数据、分时电价数据以及系统各设备的单价，在满足对负荷可靠供电的前提下，通过对系统进行优化配置使系统经济性最优。系统运行策略主要是储能系统通过在低电价时储能，在高电价时释能来降低用户的电量电费，可根据该地区现行的工商业分时电价和用户的实际用电情况确定。
4.1　分时电价数据
储能系统可以在低电价时储存电能，在高电价时释放电能，通过储释能电价差来获得收益。表1列出了该地分时电价情况。从表1可见，尖峰与低谷电价差值为0.892元/(kW·h)。根据表1中分时电价时段，1天中储能系统根据电价差可以进行2次储释能循环。尖峰电价时段都在白天，通过配置风-光系统，可以减少尖峰时段从电网购电，降低用户的用电成本。
图2给出了全年不同时刻的电价变化。从图2可见：1天中有3段时间为低电价，分别为00:00—08:00、11:00—13:00和22:00—24:00；有2段时间为高峰电价，分别为08:00—09:00和17:00—22:00；有2段时间为尖峰电价，分别为09:00—11:00和13:00—17:00。
4.2　可再生能源数据
1）光伏资源
根据国际可再生能源实验室的数据，得到该地区全年辐照度数据如图3所示。经计算，年平均每天辐照度为4.08 (kW·h)/m2。
2）风能资源
根据国际可再生能源实验室的数据，得到该地区全年风速数据如图4所示。经计算，年平均风速为5.5 m/s。
4.3　风光储系统参数
表2列出了系统各部分的成本、寿命和效率。光伏系统（包括逆变器）的成本为4 000元/kW，风电机组的成本为6 500元/kW，储能变流器的成本为200元/kW，锂电池储能的成本为1 500元/(kW·h)。系统规划使用年限为20年，光伏逆变器效率为98%，储能变流器储、释能效率都为98%，锂电池储、释能效率都为95%[20-25]。
4.4　负荷数据
用户的典型日历史负荷功率曲线如图5所示。从图5可见，峰值负荷为7 366 kW。为了模拟全年的负荷波动情况，将日间负荷变化率设为1%，日内负荷变化率设为1%。图6为用户全年小时级的负荷功率数据。
4.5　优化结果分析
优化后系统各部分的最优配置容量见表3。
1）系统成本分析
图7、表4给出了系统年总成本的构成。未配置光伏、风电和储能的情况下，每年从电网购电成本为2 634.69万元；配置光伏、风电和储能以后，每年从电网购电成本为618.66万元，每年购电成本降低2 016.03万元。
2）系统发电量分析
图8、表5给出了光伏、风电、电网的发电量和占比情况。其中，电网年发电量占比为22.4%，光伏年发电量占比为11.2%，风电年发电量占比为66.4%。
5　结论
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本文建立了一种集成光伏、风电、储能、电网和用户负荷的微电网模型，在保证用户可靠供电的前提下，根据负荷数据、辐照数据、风速数据、分时电价数据以及系统各单元的成本，对系统应用经济性进行优化分析。以源-网-荷-储系统的经济性最优为目标，综合考虑系统的实时功率平衡约束、储能系统的功率约束和荷电状态约束，建立了系统的优化模型。
1）增加风光储系统以后，一方面可以降低从电网的购电量；另一方面储能系统通过在低电价时储能，在高电价时释能，从而避免用户在高电价时从电网购电，进而降低用户从电网购电成本。
2）算例中未配置光伏、风电和储能的情况下，每年从电网购电成本为2 634.69万元；增加风光储系统以后，系统的年成本为2 050.33万元，用户每年的用电成本可降低584.36万元。
基金
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  • 国家自然科学基金项目(52376040)
  • 北京市科技新星计划(20230484479)
文献
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2024年第53卷第10期
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文章信息
doi: 10.19666/j.rlfd.202402040
  • 接收时间：2024-02-20
  • 首发时间：2026-03-05
  • 出版时间：2024-10-25
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  • 收稿日期：2024-02-20
基金
National Natural Science Foundation of China(52376040)
国家自然科学基金项目(52376040)
Beijing Nova Program(20230484479)
北京市科技新星计划(20230484479)
作者信息
    1.中国电力国际发展有限公司，北京　100080
    2.天津电气科学研究院有限公司，天津　300171
    3.中国科学院工程热物理研究所，北京　100190

通讯作者:

张新敬（1981），男，博士，研究员，主要研究方向为大规模压缩空气储能技术研发、储能与可再生能源耦合系统分析与实验研究，。
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
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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