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Based on the STELLA platform, a system dynamic model was constructed based on technology iteration, service life, and other influencing factors. This model systematically analyzed and simulated wind turbine waste generation under various scenarios, while quantifying the recycling scale of wind turbine waste and its potential carbon emission reduction effects. The results showed that: (1) Under the design lifetime scenario, the new-installed capacity of wind turbines in China were found to be increasing rapidly from 2006 to 2038, reached a trough in 2047, and then increased again. The scale of wind turbine scrapping was rising rapidly, and the peak time of wind turbines wastes with different unit capacities gradually occurred later as the unit capacity increased. (2) Under the design lifetime scenario, the amounts of waste generation components of wind turbines in 2060 were identified as follows: steel(13.67 million tons), aluminum (197200 tons), copper (762300 tons), plastic (137700 tons), fiberglass (1.7644 million tons), electronic devices (162300 tons), permanent magnets (27700 tons), lubricating oil (11000 tons), and concrete (34.76 million tons), respectively. (3) From 2025 to 2060, the cumulative closed-loop recycling of decommissioned wind turbine materials could meet 49.46%, 41.13%, and 32.67% of the total material demand under the short lifetime, design lifetime, and the long lifetime scenario, respectively. The cumulative carbon emission reductions from 2025 to 2060 with 100% resource utilization of steel, aluminum, copper and permanent magnets in scrapped wind turbines under the short lifetime, design lifetime, and the long lifetime scenario were calculated as 246.54 million tons, 175.95 million tons and 122.18 million tons respectively. Extending the wind turbine lifespan, establishing and improving the recycling system for wind power equipment, strengthening the resource recycling capabilities, and promoting advanced recycling technologies such as steel remanufacturing would reduce greenhouse gas emissions effectively. These efforts are considered significant in achieving China’s goals of peak energy production before 2030 and carbon neutrality by 2060.

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基于Stella系统动力学建模平台,耦合风机单机容量、发电方式等技术要素以及使用寿命的影响,构建了我国退役风机报废量预测模型,系统分析与模拟不同情景下风机设备的报废量,并量化回收废旧风机设备资源化规模及其碳减排潜力.结果表明,(1)设计寿命情景下,我国风机安装量2006~2038年快速增长,2047年到达波谷后再次增长,风机报废规模快速上升,不同单机容量的风机报废量达峰时间随着单机容量的增大逐渐后移;(2)设计寿命情景下2060年风机及基础废弃物各组成成分产生量分别为:钢铁1367.24万t、铝19.72万t、铜76.23万t、塑料13.77万t、玻璃钢176.44万t、电子器件16.23万t、永磁体2.77万t、润滑油1.10万t、混凝土3476.36万t;(3)2025~2060年,短寿命、设计寿命、长寿命情景下报废风机材料累计闭环回收利用可分别满足总材料需求的49.5%、41.1%、32.7%,以报废风机中钢铁、铝、铜及永磁体为例,短寿命、设计寿命、长寿命情景下2025~2060年100%资源化利用累计碳减排量分别为24654.2,17594.7和12218.4万t.延长风机寿命,并根据直驱和双馈以及不同单机容量风机的报废结果建立健全风机设备回收体系,同时强化资源再生利用能力,提高资源循环利用效率,推进报废风机中钢铁等高值设备再制造,将有效降低风机产业生命周期温室气体排放,助力于我国碳达峰碳中和战略目标的实现.

, correspAuthors=唐守娟, 石磊, authorNote=null, correspAuthorsNote=
* 责任作者,讲师,;
** 教授,
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郭慧娟(1999-),女,江西赣州人,南昌大学硕士研究生,主要研究方向为能源系统转型与可持续发展..

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郭慧娟(1999-),女,江西赣州人,南昌大学硕士研究生,主要研究方向为能源系统转型与可持续发展..

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1-小于0.6MW;2-0.6MW;3-0.75MW;4-0.85MW;5-1.25MW;6-1.5MW;7-2.0MW;8-2.5MW;9-3.0MW;10-3.0-6.0MW;11-大于6MW;12-其它单机容量

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Material intensity of wind turbines material[30]

, figureFileSmall=null, figureFileBig=null, tableContent=
装机容量(kW)发电机风电机(t)风电机合计(t)基础
钢铁铜塑料玻璃钢电子器件永磁体混凝土钢铁
150齿轮箱16.30.30.30.22.40.2201928
250齿轮箱45.300.80.80.54.10.65228812
600齿轮箱70.61.21.20.87.218238415
750齿轮箱85.71.51.41111.210243217
850齿轮箱861.51.317.61.29944718
1250齿轮箱131.42.12.41.314.61.715455726
1500齿轮箱1943.23.1221.52.5227102341
直驱172.72.622.92212.20.99225102341
2000齿轮箱262.34.34.42.734.33.4312120047
直驱232.53.530.72.734.22.91.32308120047
2500齿轮箱300.14.94.83.1434360154453
直驱272.34.131.73.144.43.41.65361154453
3000齿轮箱399.96.66.64.148.85.3472179278
直驱353.75.442.74.149.14.51.98462179278
3600齿轮箱480.77.984.964.66.3573
直驱425.66.452.84.9655.42.38563
4000齿轮箱537.78.98.75.768.97.2638
直驱4817.458.35.769.46.22.64632
4500齿轮箱595.4109.66.380.48711
直驱533.68.263.76.38172.97704
5000齿轮箱645.310.910.17.1908.8773
直驱602.59.277.67.192.27.83.3801
), ArticleFig(id=1241408752591622507, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408737232081657, language=CN, label=表1, caption=

风机材料强度[30]

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装机容量(kW)发电机风电机(t)风电机合计(t)基础
钢铁铜塑料玻璃钢电子器件永磁体混凝土钢铁
150齿轮箱16.30.30.30.22.40.2201928
250齿轮箱45.300.80.80.54.10.65228812
600齿轮箱70.61.21.20.87.218238415
750齿轮箱85.71.51.41111.210243217
850齿轮箱861.51.317.61.29944718
1250齿轮箱131.42.12.41.314.61.715455726
1500齿轮箱1943.23.1221.52.5227102341
直驱172.72.622.92212.20.99225102341
2000齿轮箱262.34.34.42.734.33.4312120047
直驱232.53.530.72.734.22.91.32308120047
2500齿轮箱300.14.94.83.1434360154453
直驱272.34.131.73.144.43.41.65361154453
3000齿轮箱399.96.66.64.148.85.3472179278
直驱353.75.442.74.149.14.51.98462179278
3600齿轮箱480.77.984.964.66.3573
直驱425.66.452.84.9655.42.38563
4000齿轮箱537.78.98.75.768.97.2638
直驱4817.458.35.769.46.22.64632
4500齿轮箱595.4109.66.380.48711
直驱533.68.263.76.38172.97704
5000齿轮箱645.310.910.17.1908.8773
直驱602.59.277.67.192.27.83.3801
), ArticleFig(id=1241408752675508590, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408737232081657, language=EN, label=Table 2, caption=

Install resource demand and decommission waste generation of wind power equipment from 2025 to 2060(万t)

, figureFileSmall=null, figureFileBig=null, tableContent=
材料短寿命情景设计寿命情景长寿命情景
装机资源需求量废弃物产生量装机资源需求量废弃物产生量装机资源需求量废弃物产生量
风机合计63056.4631185.8954137.8022267.2347337.0815466.50
钢铁50292.5425040.8043174.0917922.3437737.5512485.81
814.86406.04699.44290.62611.17202.35
3204.901467.702756.791019.592423.73686.53
塑料570.05280.60489.40199.96427.88138.43
玻璃钢7304.043563.036270.882529.885482.931741.93
电子器件671.08332.79576.06237.77503.43165.15
永磁体117.0452.41100.7436.1188.7424.11
), ArticleFig(id=1241408752759394672, tenantId=1146029695717560320, journalId=1234093305789726721, articleId=1241408737232081657, language=CN, label=表2, caption=

2025~2060年风电设备装机资源需求量及退役废弃物产生量(万t)

, figureFileSmall=null, figureFileBig=null, tableContent=
材料短寿命情景设计寿命情景长寿命情景
装机资源需求量废弃物产生量装机资源需求量废弃物产生量装机资源需求量废弃物产生量
风机合计63056.4631185.8954137.8022267.2347337.0815466.50
钢铁50292.5425040.8043174.0917922.3437737.5512485.81
814.86406.04699.44290.62611.17202.35
3204.901467.702756.791019.592423.73686.53
塑料570.05280.60489.40199.96427.88138.43
玻璃钢7304.043563.036270.882529.885482.931741.93
电子器件671.08332.79576.06237.77503.43165.15
永磁体117.0452.41100.7436.1188.7424.11
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技术要素耦合作用下我国退役风电设备资源化潜力研究
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郭慧娟 1 , 唐守娟 1, 2, * , 孔令强 3 , 郭云 4 , 刘广鑫 5 , 张力小 6 , 石磊 1, 2, **
中国环境科学 | 环境影响评价与管理 2025,45(4): 2358-2368
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中国环境科学 | 环境影响评价与管理 2025, 45(4): 2358-2368
技术要素耦合作用下我国退役风电设备资源化潜力研究
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郭慧娟1 , 唐守娟1, 2, * , 孔令强3, 郭云4, 刘广鑫5, 张力小6, 石磊1, 2, **
作者信息
  • 1.南昌大学资源与环境学院,江西 南昌 330031
  • 2.南昌大学流域碳中和教育部工程研究中心,江西 南昌 330031
  • 3.浙江财经大学公共管理学院,浙江 杭州 310018
  • 4.浙江省嘉兴生态环境监测中心,浙江 嘉兴 314001
  • 5.浙江清华长三角研究院,浙江 嘉兴 314006
  • 6.北京师范大学环境学院环境模拟与污染控制国家重点联合实验室,北京 100875
  • 郭慧娟(1999-),女,江西赣州人,南昌大学硕士研究生,主要研究方向为能源系统转型与可持续发展..

通讯作者:

* 责任作者,讲师,;
** 教授,
The resource utilization potential of decommissioned wind power equipment under the coupling of technical elements in China
Hui-juan GUO1 , Shou-juan TANG1, 2, * , Ling-qiang KONG3, Yun GUO4, Guang-xin LIU5, Li-xiao ZHANG6, Lei SHI1, 2, **
Affiliations
  • 1.School of Resources and Environment, Nanchang University, Nanchang 330031, China
  • 2.Engineering Research Center of Watershed Carbon Neutrality of Ministry of Education, Nanchang University, Nanchang 330031, China
  • 3.School of Public Administration, Zhejiang University of Finance and Economics, Hangzhou 310018, China
  • 4.Jiaxing Ecological and Environmental Monitoring Center of Zhejiang Province, Jiaxing 314001, China
  • 5.Yangtze Delta Region Institute of Tsinghua University, Jiaxing 314006, China
  • 6.State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
出版时间: 2025-04-20
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基于Stella系统动力学建模平台,耦合风机单机容量、发电方式等技术要素以及使用寿命的影响,构建了我国退役风机报废量预测模型,系统分析与模拟不同情景下风机设备的报废量,并量化回收废旧风机设备资源化规模及其碳减排潜力.结果表明,(1)设计寿命情景下,我国风机安装量2006~2038年快速增长,2047年到达波谷后再次增长,风机报废规模快速上升,不同单机容量的风机报废量达峰时间随着单机容量的增大逐渐后移;(2)设计寿命情景下2060年风机及基础废弃物各组成成分产生量分别为:钢铁1367.24万t、铝19.72万t、铜76.23万t、塑料13.77万t、玻璃钢176.44万t、电子器件16.23万t、永磁体2.77万t、润滑油1.10万t、混凝土3476.36万t;(3)2025~2060年,短寿命、设计寿命、长寿命情景下报废风机材料累计闭环回收利用可分别满足总材料需求的49.5%、41.1%、32.7%,以报废风机中钢铁、铝、铜及永磁体为例,短寿命、设计寿命、长寿命情景下2025~2060年100%资源化利用累计碳减排量分别为24654.2,17594.7和12218.4万t.延长风机寿命,并根据直驱和双馈以及不同单机容量风机的报废结果建立健全风机设备回收体系,同时强化资源再生利用能力,提高资源循环利用效率,推进报废风机中钢铁等高值设备再制造,将有效降低风机产业生命周期温室气体排放,助力于我国碳达峰碳中和战略目标的实现.

风电  /  报废量预测  /  系统动力学模型  /  资源化潜力  /  碳减排

Based on the STELLA platform, a system dynamic model was constructed based on technology iteration, service life, and other influencing factors. This model systematically analyzed and simulated wind turbine waste generation under various scenarios, while quantifying the recycling scale of wind turbine waste and its potential carbon emission reduction effects. The results showed that: (1) Under the design lifetime scenario, the new-installed capacity of wind turbines in China were found to be increasing rapidly from 2006 to 2038, reached a trough in 2047, and then increased again. The scale of wind turbine scrapping was rising rapidly, and the peak time of wind turbines wastes with different unit capacities gradually occurred later as the unit capacity increased. (2) Under the design lifetime scenario, the amounts of waste generation components of wind turbines in 2060 were identified as follows: steel(13.67 million tons), aluminum (197200 tons), copper (762300 tons), plastic (137700 tons), fiberglass (1.7644 million tons), electronic devices (162300 tons), permanent magnets (27700 tons), lubricating oil (11000 tons), and concrete (34.76 million tons), respectively. (3) From 2025 to 2060, the cumulative closed-loop recycling of decommissioned wind turbine materials could meet 49.46%, 41.13%, and 32.67% of the total material demand under the short lifetime, design lifetime, and the long lifetime scenario, respectively. The cumulative carbon emission reductions from 2025 to 2060 with 100% resource utilization of steel, aluminum, copper and permanent magnets in scrapped wind turbines under the short lifetime, design lifetime, and the long lifetime scenario were calculated as 246.54 million tons, 175.95 million tons and 122.18 million tons respectively. Extending the wind turbine lifespan, establishing and improving the recycling system for wind power equipment, strengthening the resource recycling capabilities, and promoting advanced recycling technologies such as steel remanufacturing would reduce greenhouse gas emissions effectively. These efforts are considered significant in achieving China’s goals of peak energy production before 2030 and carbon neutrality by 2060.

wind power  /  waste generation forecasting  /  system dynamic model  /  resource potential  /  carbon emission reduction
郭慧娟, 唐守娟, 孔令强, 郭云, 刘广鑫, 张力小, 石磊. 技术要素耦合作用下我国退役风电设备资源化潜力研究. 中国环境科学, 2025 , 45 (4) : 2358 -2368 .
Hui-juan GUO, Shou-juan TANG, Ling-qiang KONG, Yun GUO, Guang-xin LIU, Li-xiao ZHANG, Lei SHI. The resource utilization potential of decommissioned wind power equipment under the coupling of technical elements in China[J]. China Environmental Science, 2025 , 45 (4) : 2358 -2368 .
风能是可再生能源技术中最成熟、规划开发条件最好、最具商业发展前景的发电方式之一[1-4].根据全球风能理事会(GWEC)统计,2023年全球范围内风力发电装机总量已达到1021GW,其中我国风电装机规模居全球首位,约占全球装机规模的43%[5].在“双碳”战略目标背景下,我国风电装机规模仍将保持快速增长[6-8].随着风机服役年限增长、产业升级和技术迭代,风电设备将面临批量退役和循环利用挑战[9].精确预测风机设备退役和报废回收规模,量化风机设备回收所产生的资源化潜力及其生态环境效益,是可再生能源设备循环利用体系构建和相关政策制定的基础.
风电系统在能源基础设施建设阶段消耗了大量的金属和矿产资源,达到退役年限后,经过适当的处理和加工,报废风机中的钢铁、铜、稀土等材料可重新进入生产循环,从而减少原生材料及一次资源开采量[10].然而,受限于风电设备大型化、回收技术成熟度等因素影响,精确量化和评估资源化规模和减排效益是一项具有挑战的工作.动态物质流分析(DMFA)是目前广泛使用的废弃物产生量预测方法[11-12],根据质量守恒原则,基于历史和未来装机容量,可量化风电系统中的物质流量和存量.采用该方法,在全球、国家、省级尺度上展开了大量案例研究.其中在全球尺度,Liu等[13]、Lefeuvre等[14]分别对全球风电行业的风机叶片、碳纤维增强复合材料(CFRP)废弃物产生量及分布区域进行了深入分析,指出中国地区将是风机废弃物的主要来源国家之一.在国家尺度,相关研究主要聚焦在中国[15]、美国[16]、丹麦[17]、瑞典[18]、加拿大[19]等风机的物质代谢.Cooperman等[16]预测了美国风机叶片的废弃物产生量,指出风机寿命对风机叶片废弃物产生量具有显著影响;Andersen等[18]量化了瑞典风电行业的废弃物产生量并探讨了风机及其零件循环利用情景,表明良好的再生利用将会延迟风机废弃物带来的影响.Tazi等[20]量化分析了法国退役风机废弃物产生量及其材料流动,结果显示,废弃物材料主要为黑色和有色金属、聚合物材料(塑料)、玻璃纤维及混凝土.省级尺度上,Heng等[19]对加拿大国家和省级风机叶片废弃物进行了估算;Chen等[11]在高、中、低装机容量预测情景下量化了广东省风电行业的废弃物产生量.近年来温室气体排放已成为全球关注的焦点,对退役风机设备的研究不再局限于废弃物产生量的核算,逐渐延伸至废弃处置及回收产生的环境影响[21].Yang等[15]、Cong[6]等预测了未来中国的风机叶片废弃物产生量,并评估了不同处置方法产生的碳排放,相比传统的填埋和焚烧,回收将大大减少处置阶段产生的碳排放.
目前我国对风电行业废弃物质流的研究未充分考虑技术迭代对风机设备寿命和机型的影响、风机大型化变化趋势和直驱机组与双馈机组对材料强度系数的影响,忽视了不同单机容量机型的异质性,导致未来关键材料需求、报废风机设备的复合材料组成比例等预测结果存在显著偏差.基于此,以陆上风机为对象,本研究充分考虑了陆上风机设备技术迭代、使用寿命和风机大型化变化趋势等影响因素,采用Stella软件构建了我国风机设备报废规模预测模型,并基于历史数据验证了模型的精确性,分析了不同使用寿命情景下,不同型号风机设备装机量-存量-报废量发展演化趋势.此外,进一步量化了风机设备回收过程中各种材料资源化利用规模及其温室气体减排潜力,研究结果将为我国可再生能源设备循环利用管理体系建设和助力于“双碳”战略目标实现提供数据支撑.
风机设备运行存量的动态变化取决于风机新安装和报废拆除两个流量过程.Stella正是基于存量-流量建模范式的动态模拟软件平台,它通过诸如累积质量、能量、原料等库来描述存量,不同的库间用各种流进行连接,这些流可以使不同的库之间进行互相转移和变化.这种建模范式,使Stella在进行风机设备流量-存量动态模拟方面独具优势,展现出较好的交互性和通用性.本文以我国陆上风机新装机-运行-报废为系统边界,构建了我国风机流量-存量系统动力学模型,如图1所示.其中,模型示意图中数字1、2、3……分别代表了不同单机容量的机型.受限于1989年以前我国风机整体装机规模较小,装机量系统性数据统计始于1989年,并以此作为本研究时间系统边界的起始年份,以双碳目标设定的2060年作为模型运行区间的终止时间.
基于我国风电历史装机数据[22]、未来2060年的风机装机容量预测值[23-24],本研究采用Logistic函数模拟我国风机累计装机容量的动态变化(图2).
模型中的当年新安装风机等于当年新增需求风机与当年报废风机之和[25](式1).风机设备的设计寿命一般为20~25年[26-28],相关研究[29]发现其寿命曲线符合正态分布(式2),由风机寿命概率函数可建立拆除曲线(式3),从而获得拆除流量.
式中:Inflow(ti,ji单机容量型号j技术类型的风机在t年的新装机容量,Stock(ti,ji单机容量型号j技术类型的风机在t年的累计装机容量,Outflow(ti,ji单机容量型号j技术类型的风机在t年的报废量.ftt’)i,jt’年安装的i单机容量型号j技术类型的风机在t年报废的概率.
根据风机材料强度系数(Material Intensity)(表1),可核算报废风机废弃物中钢铁、铝等材料的含量,评估其回收潜力(式4).
式中:Wt,xt年退役风机中x材料的废弃物产生量,t;Ci,ji单机容量型号j技术类型风机的单机容量,kW;Ii,j,xi单机容量型号j技术类型风机中x材料的材料强度系数,t/台.
循环利用风机废弃物材料能够减少温室气体排放,产生良好的资源环境效益[31].风机材料的回收效率将随着回收技术的进步而提高,评估理想情况潜力最大值,可为分析风机设备回收利用及其资源环境效益提供参考.本研究仅探讨回收过程理想状态下100%资源化利用方案的环境影响,而实际回收效率受回收技术等多因素影响.假设报废风机设备中所含资源能够被100%回收利用,根据资源化利用废弃风机材料的碳减排量[21],结合上述研究得到的报废风机中每种材料的产生量,可量化其资源化利用环境效益(式6).
式中:Wxx材料的累积废弃物产生量,t;EFxx材料资源化循环利用减少碳排放的环境系数,tCO2eq/t;GHGxx材料资源循环利用累积减少的碳排放量,t.
风机设备寿命是本模型构建的关键参数之一,实际风机服役寿命受技术、设备质量、使用环境、运营维护状态等因素影响[16,32].在生产制造过程中,由于机型更新迭代较快,一方面,技术升级可延长风机寿命;另一方面,由于产业工人的培养无法跟上技术迭代速度,可能导致人工无效检查等问题,进而造成制造质量偏差,影响设备寿命.在运输与安装环节,风电机组大型化给运输安装带来了诸多挑战,例如运输过程中造成部分设备损伤难以及时发现、叶片吊装不当损坏,进而影响设备使用.在运行过程中,外部环境是影响设备寿命的关键因素,高温、高湿及极端天气等环境会加速风机的老化和损坏,已有研究[13]探讨了极端大风天气等环境因素将导致叶片故障断裂,减少设备服役时间.在风电机组运行后期,达到设计退役年限时,一些检查评估合格的风电机组可延长使用寿命,例如部分风电机组在更换叶片、发动机等部件进行改造升级后可继续运行.
现有研究通常假设风电设备寿命服从正态分布[15,29],通过寿命概率分布函数,可得到安装的风机在运行某一年的拆除概率.本研究充分考虑了以上影响因素对风机寿命的影响,设置了短寿命、设计寿命、长寿命3种模拟情景,可以囊括风电设备使用和报废所涉及的各种复杂因素.设计寿命情景设定风机寿命为25年.然而,在实际风机运行中,存在早期风电设备产品质量参差不齐、环境条件作用及后期维护不良等情况,导致风机实际寿命短于设计寿命,设定短寿命情景为20年.随着风电产业的大规模发展,技术水平不断提升,安装维护趋于规范化、标准化,风机预期寿命得到延长,设定长寿命情景为30年.在三种情景下,本研究分别假设风机寿命服从平均使用年限为20、25、30年的正态分布(式2 ),并据此建立风机的报废曲线.
我国1989~2022年不同单机容量型号风机历史新增装机容量、累计装机容量及市场份额数据来源于中国风能协会(CWEA)发布的报告,未来风电累计装机容量预测基于《中国2060年前碳中和研究报告》的碳中和实现路径规划.风电技术演变随时间呈现产品大型化的特点和趋势[33],我国陆上风电机组单机容量平均功率从上世纪八十年代至今由0.15MW增长至6.0MW以上,不同单机容量型号的风机材料强度[30]、及报废风机设备材料资源化利用碳减排量[21]来源于文献调研数据.国内市场中的风电机组主要为高速双馈风电机(齿轮箱)与直驱风电机,两种风电机组之间消耗的材料强度存在差异,早期及目前安装较多的为高速双馈风电机,由于直驱机组具有效率高、故障率低等特点,近年来其市场份额不断增长,两种风机的历史与未来市场份额参考杨举华[34]和Nassar等[24]的研究.
运行模型后,可得到我国风机运行存量的动态变化结果,如图3所示.将模型模拟值与统计值进行比较,发现模拟值与统计值存在较好的一致性,模型具有较高的可靠性.但统计值略大于模拟值,可能是因为模型输出的存量值考虑了风机使用寿命的影响,少量风机已经进入了报废周期,报废后输出了系统,而统计风机存量是累计的装机量,没有考虑风机的报废量.
我国风机安装量-存量-报废量模拟结果如图4所示,其中包括了双馈机组、直驱机组以及总机组(双馈+直驱)模拟结果.由模拟结果可知,2006年前我国风机新安装量总体上增长缓慢;2006年后,随着可再生能源法和一系列可再生能源产业政策的颁布和实施[35],以及风机生产技术的进步和生产成本的降低,风机新安装量呈现波动上升趋势,我国风电装机规模实现快速增长;特别是2020年后,在“双碳”战略的驱动下,我国风电装机规模仍将持续快速上升.短寿命情景下,新安装风机快速上升且处在高值区间,2059年达峰,峰值为130.84GW.设计寿命情景下于2038年达到第一个峰值(108.46GW),长寿命情景下于2037年达到第一个峰值(107.43GW).直驱机组和双馈机组的模拟结果变化趋势的幅度略有差异.
风机报废模拟结果显示,2020年前我国风机报废规模处于低位值且增长缓慢,整体呈上升趋势,在设计寿命情景下表现为两个快速上升期,第一个快速上升期为2020~2038年,报废规模将从0.55GW增长至26.74GW,此后进入短暂的平稳增长阶段(2038~2045年),2045年后,我国将迎来更大规模的风机报废量,报废量由2045年的30.62GW迅速增长至97.00GW,报废量的迅速增加驱动了风机安装量再次上升.相较于其他两种情景,短寿命情景下,风机报废量持续快速增长,因而风机安装量也呈现为快速增长趋势,直至2060年增速有所放缓,模拟结果表明2060年报废规模将达到116.33GW.长寿命情景下,2037年前我国风机报废量趋势与设计寿命情景差异不大,随后由于风机服役时间更久,报废规模小幅度下降,到2060年报废规模将达到76.50GW.可见延长风机使用寿命,我国风机报废规模的增长速度将有效缓解.需要强调的是,三种寿命情景下风机运行存量的模拟结果相同,模拟期内逐渐饱和.
双馈机组和直驱机组安装量-存量-报废量在不同寿命情景下的流量-存量变化趋势总体相似.短寿命情景下,双馈机组新装机量在2058年达峰,峰值为70.40GW;直驱机组新装机量在2059年达峰,峰值为60.50GW.设计寿命情景下,双馈机组新装机量于2036年达到峰值(60.24GW),直驱机组新装机量于2039年达到峰值(48.72GW).长寿命情景下,双馈机组新装机量于2036年达到峰值(59.89GW),直驱机组新装机量于2037年达到峰值(47.97GW).需要说明的是,由于早期双馈机组的市场份额占比高,其安装量在2038年前增长迅速,变化趋势相对更加明显;直驱机组市场份额自2005年起逐渐增大,2038年后安装量下降幅度较小.三种寿命情景下不同类型机组的新安装量模拟结果可以为后续材料需求量的核算提供基础,进而预测未来资源需求量.
众所周知,随着风机技术的迭代衍生出不同单机容量、发电机类型的风电设备,彼此间材料强度系数存在显著差异,进而影响了不同型号报废风机材料产生量.目前已有研究[20]对报废风机设备废弃物中不同材料的产生量进行了粗略估计,但未充分考虑风电设备大型化发展趋势下,单机容量增加以及风电机组发电方式比例变化等因素,对报废风机材料的种类、体量等结果的影响,即精细化考虑技术迭代对模型输出结果的影响.本研究充分考虑了两个参数,并将其嵌入模型之中,精细化考虑了二者对风机设备物质材料强度产生的影响.图5分别展示了不同单机容量直驱机组、双馈机组以及总机组(双馈+直驱)报废量的变化趋势.就总机组报废量而言,模拟结果表明单机容量小的风机报废量率先达峰,单机容量大的风机报废量达峰时间逐渐后移,值得注意的是,随着我国风机技术迭代速度的加快,单机容量大的风机报废量达峰时间的间距逐渐缩短,特别是,单机容量6MW的风机在2060年左右报废量才会达到峰值,且达峰速率相对较慢,达到峰值的规模也最大.这主要取决于近年我国不同单机容量机组的快速迭代、快速推向市场,以及在可再生能源政策驱动下,市场上单机容量较大的风机装机规模占风机总装机规模的比重逐渐增大.对比直驱和双馈机组的模拟结果,2020年之前双馈机组的市场份额占有率较高,因此,双馈机组的风机报废量远大于直驱机组,且报废风机的单机容量较小.随着风机大型化以及直驱机组市场份额的提升,模拟期内,直驱机组的报废量逐渐变大.
随着安装的风机设备陆续达到退役阶段,风机存量中的金属、永磁体等高附加值材料将逐渐转变为二次资源[36].不同寿命情景下我国退役风机设备废弃物产生量结果如图6所示,在不同使用寿命影响下,风机报废规模逐渐呈现分异性.到2060年,短寿命、设计寿命、长寿命情景下我国退役风机废弃物产生量分别为1827.57,1523.06,1200.47万t.
进一步解析风机设备废弃物中的组成成分,如钢铁、铝、铜、塑料、玻璃钢、电子器件、永磁体、润滑油以及混凝土,其动态变化结果如图7所示.需要解释说明的是,三种寿命情景下9种物质报废量模拟结果变化趋势整体上相似,由于直驱机组不需要润滑油且市场安装份额逐渐提高,导致润滑油的报废量增加速度较缓.此外,就混凝土报废结果而言,由于直驱和双馈机组混凝土材料强度系数相同,且随着单机容量的提升变化不大,因此混凝土的报废量增加速度也相对较缓.
在设计寿命情景下,报废风机废弃物产生总量自2020年开始迅速增长,且9种物质的报废量变化趋势相似,到2060年,风机及基础废弃物各组成成分产生量分别为:钢铁1367.24万t、铝19.72万t、铜76.23万t、塑料13.77万t、玻璃钢176.44万t、电子器件16.23万t、永磁体2.77万t、润滑油1.10万t、混凝土3476.36万t,相较于2023分别增长约44.72,47.34,92.37,50.67,60.33,48.98,133.76,33.09,26.48倍.铜和永磁体废弃物产生量增长倍数明显高于其他材料,主要是因为在风机的大型化和技术迭代过程中,单位装机铜和永磁体的材料消耗强度系数在上升,增长速度加快.短寿命情景下,风机废弃物产生量将持续快速增长,到2060年,风机及基础废弃物各组成成分产生量分别为:钢铁1638.51万t、铝23.60万t、铜93.50万t、塑料16.52万t、玻璃钢211.68万t、电子器件19.44万t、永磁体3.42万t、润滑油1.29万t、混凝土4169.44万t.相较于设计寿命情景,长寿命情景下风机废弃物产生量显著减少,2038年左右风机废弃物产生量趋势下降幅度更加明显,到2060年,风机及基础废弃物各组成成分产生量分别为:钢铁1079.65万t、铝15.60万t、铜58.15万t、塑料10.86万t、玻璃钢139.10万t、电子器件12.83万t、永磁体2.09万t、润滑油0.90万t、混凝土2741.93万t.
基于上述风机安装量-存量-报废量模拟结果,核算了2025~2060年风电设备装机累计资源需求量及退役废弃物产生量(表2).若报废风机材料实现闭环回收,短寿命、设计寿命和长寿命情景下将可满足生产风机总材料需求的49.46%、41.13%和32.67%,风机寿命从设计值25年延长到30年,将累计减少12.56%的材料需求.
资源化利用可再生能源设备中的钢铁、铝等材料,使其重新进入生产循环将显著减少环境影响.如前所述,风机材料的回收效率将随着回收技术的进步而提高,评估理想情况潜力最大值,可为分析风机设备回收利用及其资源环境效益提供参考.图8显示了2025~2060年在不同情景下100%资源化利用报废风机设备的累计碳减排量,以报废风机中钢铁、铝、铜以及永磁体为例,设计寿命情景下碳减排量共17594.74万t,其中钢铁碳减排贡献力度最大,为15054.77万t,占比85.56%.此外,材料铝、铜以及永磁体累计碳减排量分别为1028.81万t、897.24万t和613.92万t.短寿命情景下及长寿命情景下碳减排量分别为24654.22万t和12218.40万t.短寿命情景下的碳减排累计值最高,长寿命情景下碳减排累计值最低,主要是因为短寿命情景下风电设备更新速度更快,在模拟期内,报废量持续快速增长,没有平稳过渡阶段,导致短寿命情景下风机设备产生量规模最大,可资源化利用的量也最大,进而碳减排量更高.但就可再生能源设备全生命周期分析而言,延长寿命整体碳排放更低,而不能仅关注报废阶段的碳减排效益.
3.1 设计寿命情景下,我国风机安装量2006~2038年间快速增长,2047年到达波谷后又再次增长.风机报废趋势表现为两个快速上升期,分别为2020~2038年和2045~2060年,且第二阶段上升速度更快,中间调整期应充分做好废旧资源处置技术积累与管理体系建设.不同单机容量的风机报废量达峰时间随着单机容量的增大逐渐后移,单机容量6MW的风机在2060年左右报废量才会达到峰值,且达峰速率相对较慢,达到峰值的规模也最大.
3.2 报废风机设备产生量分析结果显示,2060年短寿命、设计寿命、长寿命情景下我国退役风机废弃物产生量分别为1827.57,1523.06 1200.47万t.在设计寿命情景下,2060年风机及基础废弃物各组成成分产生量分别为:钢铁1367.24万t、铝19.72万t、铜76.23万t、塑料13.77万t、玻璃钢176.44万t、电子器件16.23万t、永磁体2.77万t、润滑油1.10万t、混凝土3476.36万t,资源回收潜力巨大.在技术迭代的影响下,相对2023年,2060年铜和永磁体废弃物产生量的增长倍数明显高于其他材料.
3.3 2025~2060年,短寿命、设计寿命、长寿命情景下报废风机材料累计闭环回收利用可分别满足总材料需求的49.46%、41.13%、32.67%;若风机寿命从设计值25年延长到30年,将有助于减少12.56%的累计材料需求.以报废风机中钢铁、铝、铜及永磁体为例,设计寿命情景下100%资源化利用累计碳减排量共17594.74万t,其中钢铁累计碳减排贡献力度最大(15054.77万t),铝、铜及永磁体累计碳减排量则分别为1028.81,897.24和613.92万t.短寿命情景下及长寿命情景下累计碳减排量分别为24654.22,12218.40万t.短寿命情景下的碳减排累计值最高,长寿命情景下碳减排累计值最低,但就可再生能源设备全生命周期分析而言,延长寿命整体碳排放更低.
  • 国家重点研发计划项目(2022YFE020850002)
  • 国家自然科学基金项目(52200215; 52270182)
  • 江西省社会科学基金项目(24ZXST06)
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2025年第45卷第4期
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  • 接收时间:2024-10-23
  • 首发时间:2026-03-19
  • 出版时间:2025-04-20
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  • 收稿日期:2024-10-23
基金
国家重点研发计划项目(2022YFE020850002)
国家自然科学基金项目(52200215; 52270182)
江西省社会科学基金项目(24ZXST06)
作者信息
    1.南昌大学资源与环境学院,江西 南昌 330031
    2.南昌大学流域碳中和教育部工程研究中心,江西 南昌 330031
    3.浙江财经大学公共管理学院,浙江 杭州 310018
    4.浙江省嘉兴生态环境监测中心,浙江 嘉兴 314001
    5.浙江清华长三角研究院,浙江 嘉兴 314006
    6.北京师范大学环境学院环境模拟与污染控制国家重点联合实验室,北京 100875

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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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