IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v196y2022icp358-365.html
   My bibliography  Save this article

Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China

Author

Listed:
  • Cheng, Xinghong
  • Ye, Dong
  • Shen, Yanbo
  • Li, Deping
  • Feng, Jinming

Abstract

Photovoltaic (PV) power generation will become one of the main energy sources to achieve goals of carbon peak and carbon-neutral in China in the future. However, serious haze events have occurred frequently due to massive anthropogenic emissions in autumn and winter in China and resulted in a significant reduction of solar radiation reaching the Earth's surface. In this study, we conducted modifications to modelled solar radiation with the Weather Research and Forecasting (WRF)-Solar model by implementing the satellite-based aerosol optical depth (AOD) data into the model. The satellite-based AOD data have a higher spatial resolution and better accuracy. The studies were performed during the heavy haze period in the winter of 2016/2017 over North China (NC). Comparison with observations shows that the temporal-spatial variations of satellite-based AOD are consistent with those of AERONET and surface PM2.5 concentration, and satellite-based AOD at different sites or some months is slightly underestimated. Satellite-based AOD and surface PM2.5 concentrations in most of the southern areas of NC are significantly larger than in the northern parts. Simulated errors of Global horizontal irradiance (GHI) are reduced remarkably compared with those excluding the AOD data, especially in regions of the BTH (Beijing, Tianjin, and Hebei province), west of Shandong province, and north of Henan province. All RMSE and MAE of simulated GHI under three types of weather conditions declined by more than 20%, and 37%, respectively. Modelled errors of GHI on all-sky and cloudy-sky days are larger than those on clear-sky days due to the complicated aerosol-cloud-radiation interaction and simulated bias of cloud. Reductions of GHI caused by aerosols fall in between -140～-20 W m−2, and the spatial distribution is more refined and perform well compared to observations. Our results show that direct application of the satellite-based AOD data into the WRF-Solar model is very effective for improving the simulation of solar radiation and investigating the attenuation effect of solar radiation caused by aerosol in winter.

Suggested Citation

  • Cheng, Xinghong & Ye, Dong & Shen, Yanbo & Li, Deping & Feng, Jinming, 2022. "Studies on the improvement of modelled solar radiation and the attenuation effect of aerosol using the WRF-Solar model with satellite-based AOD data over north China," Renewable Energy, Elsevier, vol. 196(C), pages 358-365.
    • Handle: RePEc:eee:renene:v:196:y:2022:i:c:p:358-365
    DOI: 10.1016/j.renene.2022.06.141
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148122009818
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2022.06.141?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bart Sweerts & Stefan Pfenninger & Su Yang & Doris Folini & Bob Zwaan & Martin Wild, 2019. "Author Correction: Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data," Nature Energy, Nature, vol. 4(8), pages 718-718, August.
    2. Janjai, S. & Prathumsit, J. & Buntoung, S. & Wattan, R. & Pattarapanitchai, S. & Masiri, I., 2014. "Modeling the luminous efficacy of direct and diffuse solar radiation using information on cloud, aerosol and water vapor in the tropics," Renewable Energy, Elsevier, vol. 66(C), pages 111-117.
    3. Abhnil Amtesh Prasad & Merlinde Kay, 2020. "Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar," Energies, MDPI, vol. 13(2), pages 1-22, January.
    4. Polo, J. & Antonanzas-Torres, F. & Vindel, J.M. & Ramirez, L., 2014. "Sensitivity of satellite-based methods for deriving solar radiation to different choice of aerosol input and models," Renewable Energy, Elsevier, vol. 68(C), pages 785-792.
    5. Bart Sweerts & Stefan Pfenninger & Su Yang & Doris Folini & Bob Zwaan & Martin Wild, 2019. "Estimation of losses in solar energy production from air pollution in China since 1960 using surface radiation data," Nature Energy, Nature, vol. 4(8), pages 657-663, August.
    6. Zhang, Chunxiao & Shen, Chao & Yang, Qianru & Wei, Shen & Lv, Guoquan & Sun, Cheng, 2020. "An investigation on the attenuation effect of air pollution on regional solar radiation," Renewable Energy, Elsevier, vol. 161(C), pages 570-578.
    7. Carra, Elena & Marzo, Aitor & Ballestrín, Jesús & Polo, Jesús & Barbero, Javier & Alonso-Montesinos, Joaquín & Monterreal, Rafael & Abreu, Edgar F.M. & Fernández-Reche, Jesús, 2020. "Atmospheric extinction levels of solar radiation using aerosol optical thickness satellite data. Validation methodology with measurement system," Renewable Energy, Elsevier, vol. 149(C), pages 1120-1132.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xiao, Han & Song, Feng & Zheng, Xinye & Chen, Jiaying, 2023. "Community-based energy revolution: An evaluation of China's photovoltaic poverty alleviation Program's economic and social benefits," Energy Policy, Elsevier, vol. 177(C).
    2. Liu, Fa & Wang, Xunming & Sun, Fubao & Wang, Hong, 2022. "Correct and remap solar radiation and photovoltaic power in China based on machine learning models," Applied Energy, Elsevier, vol. 312(C).
    3. Song, Zhe & Liu, Jia & Yang, Hongxing, 2021. "Air pollution and soiling implications for solar photovoltaic power generation: A comprehensive review," Applied Energy, Elsevier, vol. 298(C).
    4. Kuang-Sheng Liu & Iskandar Muda & Ming-Hung Lin & Ngakan Ketut Acwin Dwijendra & Gaylord Carrillo Caballero & Aníbal Alviz-Meza & Yulineth Cárdenas-Escrocia, 2023. "An Application of Machine Learning to Estimate and Evaluate the Energy Consumption in an Office Room," Sustainability, MDPI, vol. 15(2), pages 1-14, January.
    5. Sadat, Seyyed Ali & Hoex, Bram & Pearce, Joshua M., 2022. "A Review of the Effects of Haze on Solar Photovoltaic Performance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    6. Zeng, Shihong & Tanveer, Arifa & Fu, Xiaolan & Gu, Yuxiao & Irfan, Muhammad, 2022. "Modeling the influence of critical factors on the adoption of green energy technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. Gueymard, Christian A. & Bright, Jamie M. & Lingfors, David & Habte, Aron & Sengupta, Manajit, 2019. "A posteriori clear-sky identification methods in solar irradiance time series: Review and preliminary validation using sky imagers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 412-427.
    8. Lu, Yunbo & Wang, Lunche & Zhu, Canming & Zou, Ling & Zhang, Ming & Feng, Lan & Cao, Qian, 2023. "Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    9. Arifa Tanveer & Shihong Zeng & Muhammad Irfan & Rui Peng, 2021. "Do Perceived Risk, Perception of Self-Efficacy, and Openness to Technology Matter for Solar PV Adoption? An Application of the Extended Theory of Planned Behavior," Energies, MDPI, vol. 14(16), pages 1-24, August.
    10. Ramedani, Zeynab & Omid, Mahmoud & Keyhani, Alireza & Shamshirband, Shahaboddin & Khoshnevisan, Benyamin, 2014. "Potential of radial basis function based support vector regression for global solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1005-1011.
    11. Dimitris Drikakis & Talib Dbouk, 2022. "The Role of Computational Science in Wind and Solar Energy: A Critical Review," Energies, MDPI, vol. 15(24), pages 1-20, December.
    12. Antonanzas-Torres, F. & Sanz-Garcia, A. & Martínez-de-Pisón, F.J. & Antonanzas, J. & Perpiñán-Lamigueiro, O. & Polo, J., 2014. "Towards downscaling of aerosol gridded dataset for improving solar resource assessment, an application to Spain," Renewable Energy, Elsevier, vol. 71(C), pages 534-544.
    13. Abhnil Amtesh Prasad & Merlinde Kay, 2021. "Prediction of Solar Power Using Near-Real Time Satellite Data," Energies, MDPI, vol. 14(18), pages 1-20, September.
    14. Dávid Matusz-Kalász & István Bodnár, 2021. "Operation Problems of Solar Panel Caused by the Surface Contamination," Energies, MDPI, vol. 14(17), pages 1-13, September.
    15. Kambezidis, H.D. & Psiloglou, B.E. & Karagiannis, D. & Dumka, U.C. & Kaskaoutis, D.G., 2017. "Meteorological Radiation Model (MRM v6.1): Improvements in diffuse radiation estimates and a new approach for implementation of cloud products," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 616-637.
    16. Georgios E. Arnaoutakis & Dimitris Al. Katsaprakakis, 2021. "Concentrating Solar Power Advances in Geometric Optics, Materials and System Integration," Energies, MDPI, vol. 14(19), pages 1-25, September.
    17. Li, Danny H.W. & Aghimien, Emmanuel I. & Tsang, Ernest K.W., 2022. "Application of artificial neural networks in horizontal luminous efficacy modeling," Renewable Energy, Elsevier, vol. 197(C), pages 864-878.
    18. Cornejo, Lorena & Martín-Pomares, Luis & Alarcon, Diego & Blanco, Julián & Polo, Jesús, 2017. "A through analysis of solar irradiation measurements in the region of Arica Parinacota, Chile," Renewable Energy, Elsevier, vol. 112(C), pages 197-208.
    19. Diane Palmer & Richard Blanchard, 2021. "Evaluation of High-Resolution Satellite-Derived Solar Radiation Data for PV Performance Simulation in East Africa," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    20. Mazorra Aguiar, L. & Polo, J. & Vindel, J.M. & Oliver, A., 2019. "Analysis of satellite derived solar irradiance in islands with site adaptation techniques for improving the uncertainty," Renewable Energy, Elsevier, vol. 135(C), pages 98-107.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:renene:v:196:y:2022:i:c:p:358-365. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.