碳中和背景下甘肃省光伏发电量的未来潜力评估

doi:10.12006/j.issn.1673-1719.2026.016

摘要/Abstract

摘要：

甘肃作为我国重要的光伏基地，已成为保障国家能源安全、实现“双碳”目标的关键压舱石。文中融合CARE高分辨率辐射产品、甘肃站点观测及CMIP6模拟数据，评估了甘肃理论光伏发电量（TPG）的时空演变特征。结果表明：CMIP6模式模拟能力总体良好但存在差异，MRI-ESM2-0表现最优；未来TPG变化依赖于社会经济发展路径所主导的气候响应机制。甘肃省TPG的未来演变强烈依赖于排放路径。在SSP2-4.5中等强迫情景下，TPG 进入快速增长通道（21世纪中叶增幅达 2.55 kW∙h/(m²∙(10 a)))；相反，在SSP5-8.5高强迫情景下，极端升温带来的组件“温度惩罚”将大幅抵消辐射红利，导致远期（2050—2070年）甘肃西部荒漠区出现显著衰退（局部至-9 kW∙h/(m²∙(10 a)))；在可持续发展路径（SSP1-2.6）下，TPG增长幅度有限，21世纪中叶后增长近乎停滞。本研究为区域光伏潜力评估与战略规划提供了可靠的数据和决策支持。

关键词: 理论光伏发电量（TPG）, 甘肃省, CMIP6, 多源观测, 潜力评估

Abstract:

As a crucial photovoltaic base in China, Gansu province has become a key anchor for safeguarding national energy security and achieving the “dual carbon” goals. The CARE high resolution radiation product, site observations from Gansu, and CMIP6 simulations are integrated in this study to systematically evaluate future spatiotemporal evolution characteristics of theoretical photovoltaic power generation (TPG) in Gansu. The results indicate that the CMIP6 models demonstrate generally good simulation capabilities but with differences, with MRI-ESM2-0 performing the best. Future changes in TPG depend on the climate response mechanisms driven by socioeconomic development pathways. The future evolution of TPG in Gansu province is strongly dependent on emission pathways. Under the moderate forcing SSP2-4.5 scenario, TPG enters a rapid growth trajectory (reaching an increase of 2.55 kW∙h/(m²∙(10 a)) by 21st mid-century). In contrast, under the high forcing SSP5-8.5 scenario, the “temperature penalty” on PV modules induced by extreme warming substantially offsets the radiative benefits, leading to a significant decline in the desert areas of western Gansu in the long term (locally up to -9 kW∙h/(m²∙(10 a))). Under the sustainable development pathway (SSP1-2.6), TPG shows limited growth, with increases nearly stagnating after 21st mid-century. This study provides reliable data and decision support for regional photovoltaic potential assessment and strategic planning.

Key words: Theoretical photovoltaic power generation (TPG), Gansu province, CMIP6, Multi-source observation, Potential assessment

王菲菲, 苏路豪, 韩颖, 李媛, 刘艳, 王孝三, 茹建波, 王天河. 碳中和背景下甘肃省光伏发电量的未来潜力评估[J]. 气候变化研究进展, 2026, 22(4): 439-449.

WANG Fei-Fei, SU Lu-Hao, HAN Ying, LI Yuan, LIU Yan, WANG Xiao-San, RU Jian-Bo, WANG Tian-He. Future potential assessment of Gansu’s photovoltaic power generation under carbon neutrality[J]. Climate Change Research, 2026, 22(4): 439-449.

图/表 6

表1 12个CMIP6全球模式基本信息

Table 1 Basic information of the 12 CMIP6

表2 不同情景下CMIP6各子模式泰勒综合评分

Table 2 Taylor comprehensive scores for individual CMIP6 sub-models under different scenarios

图1 不同情景下CMIP6各子模式的平均CARE评分、站点评分和综合评分(a)以及CARE评分和站点评分对比图(b)

Fig. 1 Average CARE scores, station-based scores, and comprehensive scores of the 12 sub-model ensemble under different CMIP6 scenarios (a), and comparison of CARE scores and station-based scores (b) across different CMIP6 models under various scenarios

图2 CMIP6模拟的TPG相对于卫星数据(a)和站点数据(b) QM偏差订正前后对比泰勒图

Fig. 2 Taylor diagrams comparing the CMIP6 TPG against satellite (a) and station (b) before and after QM bias correction

图3 2016—2020年基于CARE、站点和CMIP6模式的月TPG时间序列图(a)及基于CARE和MRI-ESM2-0模式的年均TPG空间分布（b~e）注：该图基于自然资源部标准地图服务网站下载的审图号为GS（2024）0650的标准地图绘制，底图界线无修改，下同。（a)图中201601表示2016年1月，以此类推。（b~e）图中红色实线为甘肃省界线。

Fig. 3 Monthly TPG time series based on CARE products, station observations, and the optimal CMIP6 model simulation (a), spatial distribution of the annual mean TPG (2016-2020) based on CARE products and the MRI-ESM2-0 model simulation (b-e)

图4 MRI-ESM2-0模式模拟的甘肃年均TPG的未来不同时期的演变(a)及其在甘肃及其周边地区的(b~j)趋势空间分布注：图中黑色实线为甘肃省界线，黑点表示通过了0.10的显著性检验。

Fig. 4 Evolution of the annual mean TPG in Gansu under the MRI-ESM2-0 models based on comprehensive scores across different future periods (a), and spatial distribution of trends in Gansu and its surrounding areas (b-j)

参考文献 32

[1]	IPCC. Climate change 2023: synthesis report, summary for policymakers[R]. Geneva, Switzerland: IPCC, 2023
[2]	Hugonnet R, McNabb R, Berthier E, et al. Accelerated global glacier mass loss in the early twenty-first century[J]. Nature, 2021, 592 (7856): 726-731 doi: 10.1038/s41586-021-03436-z
[3]	Zheng D, Yan X, Tong D, et al. Strategies for climate-resilient global wind and solar power systems[J]. Nature, 2025, 643 (8074): 1263-1270 doi: 10.1038/s41586-025-09266-7
[4]	禹湘, 娄峰, 谭畅. 基于CIE-CEAM模型的中国工业“双碳”路径模拟[J]. 中国人口∙资源与环境, 2022, 32 (7): 49-56.
	Yu X, Lou F, Tan C. A simulation study of the pathway of achieving the “dual carbon” goals in China’s industrial sectors based on the CIE-CEAM model[J]. China Population, Resources and Environment, 2022, 32 (7): 49-56 (in Chinese)
[5]	Cheng H, Huang H, Yang J, et al. Research on thermal environment analysis of photovoltaic power plants based on remote sensing images[J]. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2023, 48: 1343-1350
[6]	Li P, Gao X, Li Z, et al. Effect of the temperature difference between land and lake on photovoltaic power generation[J]. Renewable Energy, 2022, 185: 86-95 doi: 10.1016/j.renene.2021.12.011 URL
[7]	Che H Z, Shi G Y, Zhang X Y, et al. Analysis of 40 years of solar radiation data from China, 1961-2000[J]. Geophysical Research Letters, 2005, 32 (6)
[8]	Fernández E F, Cruz F A, Mallick T K, et al. Effect of spectral irradiance variations on the performance of highly efficient environment-friendly solar cells[J]. IEEE Journal of Photovoltaics, 2015, 5 (4): 1150-1157 doi: 10.1109/JPHOTOV.2015.2434593 URL
[9]	Mostefaoui M, Ziane A, Bouraiou A, et al. Effect of sand dust accumulation on photovoltaic performance in the Saharan environment: southern Algeria (Adrar)[J]. Environmental Science and Pollution Research, 2018, 26 (1): 259-268 doi: 10.1007/s11356-018-3496-7
[10]	Yu J, Ma H, Fu S M, et al. Long-term variations of the solar energy in different subregions of Northwest China and associated mechanisms[J]. Atmospheric and Oceanic Science Letters, 2025, 18 (3): 100515 doi: 10.1016/j.aosl.2024.100515 URL
[11]	Qi Q, Wu J, Gueymard C A, et al. Mapping of 10-km daily diffuse solar radiation across China from reanalysis data and a Machine-Learning method[J]. Scientific Data, 2024, 11 (1): 756 doi: 10.1038/s41597-024-03609-1 pmid: 38992050
[12]	刘孝敏, 刘叶瑞, 谢伟雪, 等. 甘肃省太阳能资源特征及光热应用潜力分析[J]. 安徽农业科学, 2012, 40 (27): 13529-13530.
	Liu X M, Liu Y R, Xie W X, et al. Analysis of solar energy resources characteristics and solar thermal application potential in Gansu province[J]. Journal of Anhui Agricultural Sciences, 2012, 40 (27): 13529-13530 (in Chinese)
[13]	Feng X, Zhang T, Li G, et al. Synergistic planning method of renewable energy power base in Gobi desert considering cascade pumped storage power station water-electric coupling[C]. Singapore: Annual Conference of China Electrotechnical Society, 2024: 707-716
[14]	马维唯. “沙戈荒”地区千万千瓦级新能源基地“五位一体”系统化开发模式研究[J]. 中国煤炭, 2025, 51 (7): 86-92.
	Ma W W. Research on the “Five in One” systematic development model of ten million kilowatts level new energy bases in Desert, Gobi and Arid regions [J]. China Coal, 2025, 51 (7): 86-92 (in Chinese)
[15]	甘肃省统计局. 2023年甘肃省国民经济和社会发展统计公报[R/OL]. 2024 [2025-06-26]. https://www.gs.gov.cn/gsszf/gsyw/202403/173877932.shtml.
	Gansu Provincial Bureau of Statistics. Statistical bulletin on national economic and social development of Gansu province in 2023[R/OL]. 2024 [2025-06-26]. https://www.gs.gov.cn/gsszf/gsyw/202403/173877932.shtml (in Chinese)
[16]	张强, 杨金虎, 马鹏里, 等. 西北地区气候暖湿化增强东扩特征及其形成机制与重要环境影响[J]. 干旱气象, 2023, 41 (3): 351-358. doi: 10.11755/j.issn.1006-7639（2023）-03-0351
	Zhang Q, Yang J H, Ma P L, et al. The enhancement and eastward expansion of climate warming and humidification, formation mechanism and important environmental impacts in Northwest China[J]. Journal of Arid Meteorology, 2023, 41 (3): 351-358 (in Chinese)
[17]	姚玉璧, 郑绍忠, 董宏昌, 等. 中国西北地区太阳辐射时空分异特征[J]. 干旱区研究, 2023, 40 (6): 863-873. doi: 10.13866/j.azr.2023.06.02
	Yao Y B, Zheng S Z, Dong H C, et al. Spatio-temporal variation characteristics of solar radiation in Northwest China[J]. Arid Zone Research, 2023, 40 (6): 863-873 (in Chinese)
[18]	Adigun P, Dairaku K, Ogunrinde A T, et al. Climate change influence on solar photovoltaic energy production and its associated drivers in CMIP6 ensemble projections[J]. Journal of Geophysical Research: Atmospheres, 2025, 130 (18)
[19]	Saxena A, Brown C, Arneth A, et al. Modelling the global photovoltaic potential on land and its sensitivity to climate change[J]. Environmental Research Letters, 2023, 18 (10): 104017 doi: 10.1088/1748-9326/acf86f
[20]	Guo J, Chen Z, Meng J, et al. Picturing China’s photovoltaic energy future: insights from CMIP6 climate projections[J]. Renewable and Sustainable Energy Reviews, 2024, 189: 114026 doi: 10.1016/j.rser.2023.114026 URL
[21]	Husi L T, Ma R. East Asia-Pacific surface PAR, UV and SW data set (2016-2020)[DB/OL]. National Tibetan Plateau Data, National Tibetan Plateau Data Center, 2023 [2025-06-26]. https://cstr.cn/18406.11.Atmos.tpdc.272820
[22]	Ma R, Husi L T, Yang K, et al. Estimation of surface shortwave radiation from Himawari-8 satellite data based on a combination of radiation transfer and deep neural network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58 (8): 5304-5316 doi: 10.1109/TGRS.36 URL
[23]	Faiman D. Assessing the outdoor operating temperature of photovoltaic modules[J]. Progress in Photovoltaics: Research and Applications, 2008, 16 (4): 307-315 doi: 10.1002/pip.v16:4 URL
[24]	Taylor K E. Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research: Atmospheres, 2001, 106 (D7): 7183-7192 doi: 10.1029/2000JD900719 URL
[25]	Jiao B, Su Y, Li Q, et al. An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach[J]. Earth System Science Data, 2023, 15 (10): 4519-4535 doi: 10.5194/essd-15-4519-2023 URL
[26]	Yukimoto S, Kawai H, Koshiro T, et al. The Meteorological Research Institute Earth System Model Version 2.0, MRI-ESM2.0: description and basic evaluation of the physical component[J]. Journal of the Meteorological Society of Japan, 2019, 97 (5): 931-965 doi: 10.2151/jmsj.2019-051
[27]	He J, Hong L, Shao C, et al. Global evaluation of simulated surface shortwave radiation in CMIP6 models[J]. Atmospheric Research, 2023, 292: 106896 doi: 10.1016/j.atmosres.2023.106896 URL
[28]	Wu F, You Q, Zhang J, et al. Understanding of CMIP6 surface temperature cold bias over the westerly and monsoon regions of the Tibetan Plateau[J]. Climate Dynamics, 2024, 62 (5): 4133-4153 doi: 10.1007/s00382-024-07122-4
[29]	Winter K J P, Kotlarski S, Scherrer S C, et al. The Alpine snow-albedo feedback in regional climate models[J]. Climate Dynamics, 2017, 48 (3-4): 1109-1124 doi: 10.1007/s00382-016-3130-7 URL
[30]	Constantin S R, Stephanie F. What drives historical and future changes in photovoltaic power production from the perspective of global warming?[J]. Environmental Research Letters, 2024, 19 (1)
[31]	Ding S, Qiu B, Luo Y. Co-benefits of aerosol pollution mitigation in enhancing solar photovoltaic power generation in China[J]. Environmental Research Letters, 2025, 20 (10)
[32]	Zhang Y, Qin W, Wang L, et al. Enhancement of photovoltaic power potential in China from 2010 to 2020: the contribution of air pollution control policies[J]. Remote Sensing, 2023, 15 (1)