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利用光伏功率模型集合评估中国太阳能潜力。

Assessment of solar energy potential in China using an ensemble of photovoltaic power models.

机构信息

Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China.

Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology (NUIST), Nanjing 210044, China.

出版信息

Sci Total Environ. 2023 Jun 15;877:162979. doi: 10.1016/j.scitotenv.2023.162979. Epub 2023 Mar 21.

Abstract

Development of solar energy is one of the key solutions towards carbon neutrality in China. The output of solar energy is dependent on weather conditions and shows distinct spatiotemporal characteristics. Previous studies have explored the photovoltaic (PV) power potential in China but with single models and low-resolution radiation data. Here, we estimated the PV power potential in China for 2016-2019 using an ensemble of 11 PV models based on hourly solar radiation at the resolution of 5 km retrieved by the Himawari-8 geostationary satellite. On the national scale, the ensemble method revealed an annual average PV power potential of 242.79 kWh m with the maximum in the west (especially the Tibetan Plateau) and the minimum in the southeast (especially the Sichuan Basin). The multi-model approach shows inter-model spreads of 6 %-7 % distributed uniformly in China, suggesting a robust spatial pattern predicted by these models. The seasonal variation in general shows the largest PV power generation in summer months except for Tibetan Plateau, where the peak value appears in spring because the high cloud coverage dampens the regional solar radiation in summer. On the national scale, the deseasonalized PV power potential shows a high correlation with cloud coverage (R = 0.71, p < 0.01) but a low correlation with aerosol optical depth (R = 0.08, p < 0.05). Sensitivity experiments show that national PV power potential increases by 0.55 % per 1 W m increase of radiation and 0.79 % per 1 m s increase of wind speed, but decreases by 0.46 % per 1 °C increase of air temperature. These sensitivities provide a solid foundation for the future projection of PV power potential in China under climate change.

摘要

太阳能的发展是中国实现碳中和的关键解决方案之一。太阳能的输出取决于天气条件,并呈现出明显的时空特征。以前的研究已经探索了中国的光伏(PV)电力潜力,但使用的是单一模型和低分辨率的辐射数据。在这里,我们使用基于 Himawari-8 地球静止卫星每小时太阳辐射的分辨率为 5km 的 11 个光伏模型的集合,估计了 2016-2019 年中国的光伏潜力。在全国范围内,该集合方法显示出 242.79kWh/m 的年平均光伏潜力,最大值在西部(特别是青藏高原),最小值在东南部(特别是四川盆地)。多模型方法显示出模型间的差异在全国范围内均匀分布在 6%-7%,表明这些模型预测的空间格局是稳健的。总的来说,季节性变化一般显示夏季月的光伏发电量最大,但青藏高原除外,由于高云量抑制了夏季该地区的太阳辐射,其峰值出现在春季。在全国范围内,去季节化的光伏潜力与云量高度相关(R=0.71,p<0.01),与气溶胶光学深度低度相关(R=0.08,p<0.05)。敏感性实验表明,全国光伏潜力每增加 1W/m 的辐射增加 0.55%,每增加 1m/s 的风速增加 0.79%,但每增加 1°C 的空气温度减少 0.46%。这些敏感性为未来气候变化下中国光伏潜力的未来预测提供了坚实的基础。

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