Abstract
Surface solar irradiance (SSI) nowcasting (0–3 h) is an effective way to overcome the intermittency of solar energy and to ensure the safe operation of grid-connected solar power plants. In this study, an SSI estimate and nowcasting system was established using the near-infrared channel of Fengyun-4A (FY-4A) geostationary satellite. The system is composed of two key components: The first is a hybrid SSI estimation method combining a physical clear-sky model and an empirical cloudy-sky model. The second component is the SSI nowcasting model, the core of which is the derivation of the cloud motion vector (CMV) using the block-matching method. The goal of simultaneous estimation and nowcasting of global horizontal irradiance (GHI) and direct normal irradiance (DNI) is fulfilled. The system was evaluated under different sky conditions using SSI measurements at Xianghe, a radiation station in the North China Plain. The results show that the accuracy of GHI estimation is higher than that of DNI estimation, with a normalized root-mean-square error (nRMSE) of 22.4% relative to 45.4%. The nRMSE of forecasting GHI and DNI at 30–180 min ahead varied within 25.1%–30.8% and 48.1%–53.4%, respectively. The discrepancy of SSI estimation depends on cloud occurrence frequency and shows a seasonal pattern, being lower in spring—summer and higher in autumn—winter. The FY-4A has great potential in supporting SSI nowcasting, which promotes the development of photovoltaic energy and the reduction of carbon emissions in China. The system can be improved further if calibration of the empirical method is improved.
摘要
碳中和目标背景下,中国未来将显著增加光伏等新能源在能源结构中的占比。光伏太阳能的间接性和不稳定性是太阳能并网利用中的重大挑战之一。除了发展新能源储能等技术之外,发展太阳能短临预报技术是提高太阳能利用率的经济有效途径。我国新一代静止气象卫星风云四号(FY-4A)的发射给太阳能短临预报(<3小时)提供了新的观测手段。本文利用FY-4A多通道反射率数据建立了地表太阳辐照度估算和短临预报系统。该系统由两个关键部分组成,第一部分为基于物理晴天模型和经验云天模型构建的地表太阳辐照度混合估算方法,第二部分为地表太阳辐照度短临预报模型,其核心是通过块状匹配法推导出云运动矢量,进而预报未来3小时内地表太阳辐照度场。该系统目前能够同时实现水平面总辐射和法向直接辐射的估算和短临预报。验证结果表明:水平面总辐射估算值的准确性高于法向直接辐射,二者归一化均方根误差分别为22.4%和45.4%。30-180分钟预测范围内水平面总辐射和法向直接辐射预测值的归一化均方根误差分别在25.1%-30.8%和48.1%-53.4%之间。地表太阳辐照度估算结果的准确性取决于云的出现频率,即春夏较低,秋冬较高。本研究工作表明新一代静止气象卫星在地表太阳辐射短时临近预报中的广阔应用前景,将显著促进我国光伏太阳能能源发展和利用。该系统在华北地区具备良好的性能,未来进一步改进将侧重于对地表太阳辐照度估算模型的校准并推广其在整个中国地区的适用性。
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References
Amillo, A. G., T. Huld, and R. Müller, 2014: A new database of global and direct aolar radiation using the eastern meteosat satellite, models and validation. Remote Sensing, 6, 8165–8189, https://doi.org/10.3390/rs6098165.
Antonanzas, J., N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres, 2016: Review of photovoltaic power forecasting. Solar Energy, 136, 78–111, https://doi.org/10.1016/j.solener.2016.06.069.
Antonanzas-Torres, F., R. Urraca, J. Polo, O. Perpiñán-Lamigueiro, and R. Escobar, 2019: Clear sky solar irradiance models: A review of seventy models. Renewable and Sustainable Energy Reviews, 107, 374–387, https://doi.org/10.1016/j.rser.2019.02.032.
Arbizu-Barrena, C., J. A. Ruiz-Arias, F. J. Rodríguez-Benítez, D. Pozo-Vázquez, and J. Tovar-Pescador, 2017: Short-term solar radiation forecasting by advecting and diffusing MSG cloud index. Solar Energy, 155, 1092–1103, https://doi.org/10.1016/j.solener.2017.07.045.
Bai, B., Y. H. Wang, C. Fang, S. Q. Xiong, and X. M. Ma, 2021: Efficient deployment of solar photovoltaic stations in China: An economic and environmental perspective. Energy, 221, 119834, https://doi.org/10.1016/j.energy.2021.119834.
Beyer, H. G., J. P. Martinez, M. Suri, J. L. Torres, E. Lorenz, S. C. Müller, C. Hoyer-Klick, and P. Ineichen, 2009: D 1.1.3 Report on Benchmarking of Radiation Products. Management and Exploitation of Solar Resource Knowledge. Available from http://www.mesor.org/docs/MESoR_Benchmarking_of_radiation_products.pdf.
Burandt, T., B. Xiong, K. Löffler, and P.-Y. Oei, 2019: Decarbonizing China’s energy system — Modeling the transformation of the electricity, transportation, heat, and industrial sectors. Applied Energy, 255, 113820, https://doi.org/10.1016/j.apenergy.2019.113820.
Chen, X. M., Y. Li, and R. Z. Wang, 2020: Performance study of affine transformation and the advanced clear-sky model to improve intra-day solar forecasts. Journal of Renewable and Sustainable Energy, 12, 043703, https://doi.org/10.1063/5.0009155.
Cros, S., M. Albuisson, M. Lefèvre, C. Rigollier, and L. Wald, 2004: HelioClim: A long-term database on solar radiation for Europe and Africa. Proceedings of Eurosun 2004, Freiburg, Germany, PSE GmbH.
Cros, S., N. Sébastien, O. Liandrat, and N. Schmutz, 2014: Cloud pattern prediction from geostationary meteorological satellite images for solar energy forecasting. Proceedings of SPIE 9242, Remote Sensing of Clouds and the Atmosphere XIX; and Optics in Atmospheric Propagation and Adaptive Systems XVII, Amsterdam, Netherlands, SPIE, https://doi.org/10.1117/12.2066853.
Damiani, A., and Coauthors, 2018: Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements. Atmospheric Measurement Techniques, 11, 2501–2521, https://doi.org/10.5194/amt-11-2501-2018.
Gallucci, D., and Coauthors, 2018: Nowcasting surface solar irradiance with AMESIS via motion vector fields of MSGSEVIRI Data. Remote Sensing, 10, 845, https://doi.org/10.3390/rs10060845.
Gueymard, C. A., 2008: REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation — Validation with a benchmark dataset. Solar Energy, 82, 272–285, https://doi.org/10.1016/j.solener.2007.04.008.
Gueymard, C. A., and R. George, 2005: Gridded aerosol optical depth climatological datasets over continents for solar radiation modeling. Proceedings of Solar World Congress, Orlando, USA, International Solar Energy Society. [Available online from https://www.semanticscholar.org/paper/GRIDDED-AEROSOL-OPTICAL-DEPTH-CLIMATOLOGICALOVER-Gueymard-George/a3e7dad6035e6a35afdccf9bf4b98319436c3014]
Hammer, A., D. Heinemann, E. Lorenz, and B. Lückehe, 1999: Short-term forecasting of solar radiation: A statistical approach using satellite data. Solar Energy, 67, 139–150, https://doi.org/10.1016/S0038-092X(00)00038-4.
Huang, C. L., J. Z. Li, W. W. Sun, Q. X. Chen, Q.-J. Mao, and Y. Yuan, 2021: Long-term variation assessment of aerosol load and dominant types over Asia for air quality studies using multi-sources aerosol datasets. Remote Sensing, 13, 3116, https://doi.org/10.3390/rs13163116.
Huang, G. H., Z. Q. Li, X. Li, S. L. Liang, K. Yang, D. D. Wang, and Y. Zhang, 2019: Estimating surface solar irradiance from satellites: Past, present, and future perspectives. Remote Sensing of Environment, 233, 111371, https://doi.org/10.1016/j.rse.2019.111371.
IRENA, 2020: Renewable Capacity Statistics 2020: International Renewable Energy Agency (IRENA), Abu Dhabi. [Available online from https://irena.org/publications/2020/Mar/Renewable-Capacity-Statistics-2020]
Jia, D. Y., J. J. Hua, L. P. Wang, Y. T. Guo, H. Guo, P. P. Wu, M. Liu, and L. W. Yang, 2021: Estimations of global horizontal irradiance and direct normal irradiance by using Fengyun-4A satellite data in northern China. Remote Sensing, 13, 790, https://doi.org/10.3390/rs13040790.
Jiang, H., N. Lu, J. Qin, W. J. Tang, and L. Yao, 2019: A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data. Renewable and Sustainable Energy Reviews, 114, 109327, https://doi.org/10.1016/j.rser.2019.109327.
Kallio-Myers, V., A. Riihelä, P. Lahtinen, and A. Lindfors, 2020: Global horizontal irradiance forecast for Finland based on geostationary weather satellite data. Solar Energy, 198, 68–80, https://doi.org/10.1016/j.solener.2020.01.008.
Kleissl, J., 2013: Solar Energy Forecasting and Resource Assessment. Academic Press, https://doi.org/10.1016/C2011-0-07022-9.
Lamsal, D., V. Sreeram, Y. Mishra, and D. Kumar, 2018: Kalman filter approach for dispatching and attenuating the power fluctuation of wind and photovoltaic power generating systems. IET Generation, Transmission & Distribution, 12, 1501–1508, https://doi.org/10.1049/iet-gtd.2017.0663.
Letu, H., T. Y. Nakajima, T.X. Wang, H. Z. Shang, R. Ma, K. Yang, A. J. Baran, J. Riedi, H. Ishimoto, M. Yoshida, C. Shi, P. Khatri, Y. H. Du, L. F. Chen, and J. C Shi, 2021: A new benchmark for surface radiation products over the East Asia-Pacific region retrieved from the Himawari-8/AHI next-generation geostationary satellite. Bull. Amer. Meteor. Soc, 103, E873–888, https://doi.org/10.1175/BAMS-D-20-0148.1.
Li, M. Q., E. Virguez, R. Shan, J. L. Tian, S. Gao, and D. Patiño-Echeverri, 2022: High-resolution data shows China’s wind and solar energy resources are enough to support a 2050 decarbonized electricity system. Applied Energy, 306, 117996, https://doi.org/10.1016/j.apenergy.2021.117996.
Li, T., A. Li, and X. P. Guo, 2020: The sustainable development-oriented development and utilization of renewable energy industry-A comprehensive analysis of MCDM methods. Energy, 212, 118694, https://doi.org/10.1016/j.energy.2020.118694.
Liu, M. Q., X. A. Xia, D. S. Fu, and J. Q. Zhang, 2021: Development and validation of machine-learning clear-sky detection method using 1-min irradiance data and sky imagers at a polluted suburban site, Xianghe. Remote Sensing, 13, 3763, https://doi.org/10.3390/rs13183763.
Mouhamet, D., A. Tommy, A. Primerose, and L. Laurent, 2018: Improving the Heliosat-2 method for surface solar irradiation estimation under cloudy sky areas. Solar Energy, 169, 565–576, https://doi.org/10.1016/j.solener.2018.05.032.
Nonnenmacher, L., and C. F. M. Coimbra, 2014: Streamline-based method for intra-day solar forecasting through remote sensing. Solar Energy, 108, 447–459, https://doi.org/10.1016/j.solener.2014.07.026.
Peng, Z., and Coauthors, 2020: Estimation of shortwave solar radiation using the artificial neural network from Himawari-8 satellite imagery over China. Journal of Quantitative Spectroscopy and Radiative Transfer, 240, 106672, https://doi.org/10.1016/j.jqsrt.2019.106672.
Pfeifroth, U., S. Kothe, J. Trentmann, R. Hollmann, P. Fuchs, J. Kaiser, and M. Werscheck, 2019: Surface Radiation Data Set — Heliosat (SARAH) — Edition 2.1. Available from https://doi.org/10.5676/EUM_SAF_CM/SARAH/V002_01.
Prăvălie, R., C. Patriche, and G. Bandoc, 2019: Spatial assessment of solar energy potential at global scale. A geographical approach. Journal of Cleaner Production, 209, 692–721, https://doi.org/10.1016/j.jclepro.2018.10.239.
Randles, C. A., and Coauthors, 2017: The MERRA-2 aerosol reanalysis, 1980 Onward. Part I: System description and data assimilation evaluation. J. Climate, 30, 6823–6850, https://doi.org/10.1175/JCLI-D-16-0609.1.
Razagui, A., K. Abdeladim, K. Bouchouicha, N. Bachari, S. Semaoui, and A. Hadj Arab, 2021: A new approach to forecast solar irradiances using WRF and libRadtran models, validated with MERRA-2 reanalysis data and pyranometer measures. Solar Energy, 221, 148–161, https://doi.org/10.1016/j.solener.2021.04.024.
Rigollier, C., M. Lefèvre, and L. Wald, 2004: The method Heliosat-2 for deriving shortwave solar radiation from satellite images. Solar Energy, 77, 159–169, https://doi.org/10.1016/j.solener.2004.04.017.
Senatla, M., and R. C. Bansal, 2018: Review of planning methodologies used for determination of optimal generation capacity mix: The cases of high shares of PV and wind. IET Renewable Power Generation, 12, 1222–1233, https://doi.org/10.1049/iet-rpg.2017.0380.
Shi, H. R., and Coauthors, 2021: Surface brightening in eastern and central China since the implementation of the clean air action in 2013: Causes and implications. Geophys. Res. Lett., 48, e2020GL091105, https://doi.org/10.1029/2020GL091105.
Sun, X. X., J. M. Bright, C. A. Gueymard, X. Y. Bai, B. Acord, and P. Wang, 2021: Worldwide performance assessment of 95 direct and diffuse clear-sky irradiance models using principal component analysis. Renewable and Sustainable Energy Reviews, 135, 110087, https://doi.org/10.1016/j.rser.2020.110087.
Wang, F., Z. Zhen, C. Liu, Z. Q. Mi, B.-M. Hodge, M. Shafie-Khah, and J. P. S. Catalão, 2018: Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting. Energy Conversion and Management, 157, 123–135, https://doi.org/10.1016/j.enconman.2017.11.080.
Wang, P., R. van Westrhenen, J. F. Meirink, S. van der Veen, and W. Knap, 2019: Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations. Solar Energy, 177, 47–58, https://doi.org/10.1016/j.solener.2018.10.073.
Wild, M., D. Folini, F. Henschel, N. Fischer, and B. Müller, 2015: Projections of long-term changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems. Solar Energy, 116, 12–24, https://doi.org/10.1016/j.solener.2015.03.039.
Xian, D., P. Zhang, L. Gao, R. J. Sun, H. Z. Zhang, and X. Jia, 2021: Fengyun meteorological satellite products for earth system science applications. Adv. Atmos. Sci., 38, 1267–1284, https://doi.org/10.1007/s00376-021-0425-3.
Yang, L. W., X. Q. Gao, Z. C. Li, D. Y. Jia, and J. X. Jiang, 2019: Nowcasting of surface solar irradiance using Fengyun-4 satellite observations over China. Remote Sensing, 11, 1984, https://doi.org/10.3390/rs11171984.
Yang, L. W., X. Q. Gao, J. J. Hua, P. P. Wu, Z. C. Li, and D. Y. Jia, 2020: Very short-term surface solar irradiance forecasting based on Fengyun-4 geostationary satellite. Sensors, 20, 2606, https://doi.org/10.3390/s20092606.
Zhang, J. Q., X. A. Xia, H. R. Shi, X. M. Zong, and J. Li, 2020: Radiation and aerosol measurements over the Tibetan Plateau during the Asian summer monsoon period. Atmospheric Pollution Research, 11, 1543–1551, https://doi.org/10.1016/j.apr.2020.06.017.
Zhu, T. T., H. Zhou, H. K. Wei, X. Zhao, K. J. Zhang, and J. X. Zhang, 2019: Inter-hour direct normal irradiance forecast with multiple data types and time-series. Journal of Modern Power Systems and Clean Energy, 7, 1319–1327, https://doi.org/10.1007/s40565-019-0551-4.
Zou, L., L. C. Wang, J. R. Li, Y. B. Lu, E. Gong, and Y. Niu, 2019: Global surface solar radiation and photovoltaic power from Coupled Model Intercomparison Project Phase 5 climate models. Journal of Cleaner Production, 224, 304–324, https://doi.org/10.1016/j.jclepro.2019.03.268.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 42030608, 41805021, and 51776051), the Beijing Natural Science Foundation (Grant No. 8204072), and Beijing Nova Program (Grant No. Z211100002121077). We would like to thank the National Satellite Meteorological Center and the MERRA-2 teams for providing the data used in this study. The FY-4A data were collected from http://satellite.nsmc.org.cn/portalsite/default.aspx (accessed on 20 June 2021). The MERRA-2 data were collected from http://disc.sci.gsfc.nasa.gov/ (accessed on 12 September 2021).
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Article Highlights
• A Fengyun-4A-based solar energy estimate and forecasting system is established.
• A hybrid method with the capability of simultaneous estimation of GHI and DNI is developed.
• The application of this system in the North China Plain shows good performance in forecasting solar energy.
This paper is a contribution to the special issue on Carbon Neutrality: Important Roles of Renewable Energies, Carbon Sinks, NETs, and non-CO2 GHGs.
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Huang, C., Shi, H., Gao, L. et al. Fengyun-4 Geostationary Satellite-Based Solar Energy Nowcasting System and Its Application in North China. Adv. Atmos. Sci. 39, 1316–1328 (2022). https://doi.org/10.1007/s00376-022-1464-0
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DOI: https://doi.org/10.1007/s00376-022-1464-0