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Quantifying the air pollution impacts on solar photovoltaic capacity factors and potential benefits of pollution control for the solar sector in China

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  • Song, Zhe
  • Cao, Sunliang
  • Yang, Hongxing

Abstract

Solar photovoltaic (PV) plays a crucial role in China's energy transition. However, air pollution diminishes solar radiation resources, thereby reducing PV power generation efficiency. This study aims to quantify the impacts of air pollution on PV capacity factors in China while emphasizing the geographically specific potential benefits of improved air quality for the future PV sector. Using a PV power generation evaluation model, this study estimated solar PV capacity factors from 1961 to 2016 under both all-sky and clear-sky scenarios. Also, the spatiotemporal patterns of air pollution-induced capacity factor changes were analyzed. The results indicate that the all-sky and clear-sky PV capacity factors showed consistent annual anomalies, suggesting that air pollution due to aerosol emissions drove PV capacity factor temporal evolutions and trends in China. From 1961 to 1990, the national average clear-sky PV capacity factors for fixed modules with optimal tilt angle showed a significant decreasing trend of −0.0053 per decade, whereas from 1990 to 2016, an upward trend of 0.0030 per decade was observed. Compared with fixed modules, tracking systems suffered from greater productivity losses due to air pollution. In comparison to 1961–1965 averages, the provincial PV capacity factors for 2012–2016 decreased by 0.48–13.54%, 1.15–11.40%, 1.06–14.83%, and 1.31–16.51% for fixed modules with optimal tilt angle, horizontal fixed, one-axis horizontal tracking, and two-axis tracking modules, respectively, with decreases of 7–17% in the central and southeast. Furthermore, according to the provincial PV installation targets projected in China's 14th Five-Year Plan, national solar PV power generation was expected to increase by 81.333 TWh to 1069.997 TWh in 2025 with average PV capacity factors for 1961–1965, compared to 2012–2016 averages. In a scenario of keeping grid parity, additional power generation would offer an economic benefit of approximately 30.102 billion CNY.

Suggested Citation

  • Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2024. "Quantifying the air pollution impacts on solar photovoltaic capacity factors and potential benefits of pollution control for the solar sector in China," Applied Energy, Elsevier, vol. 365(C).
  • Handle: RePEc:eee:appene:v:365:y:2024:i:c:s0306261924006445
    DOI: 10.1016/j.apenergy.2024.123261
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    1. Del Hoyo, Mirko & Rondanelli, Roberto & Escobar, Rodrigo, 2020. "Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile," Renewable Energy, Elsevier, vol. 148(C), pages 1137-1149.
    2. Jinyue Yan & Ying Yang & Pietro Elia Campana & Jijiang He, 2019. "City-level analysis of subsidy-free solar photovoltaic electricity price, profits and grid parity in China," Nature Energy, Nature, vol. 4(8), pages 709-717, August.
    3. Dev Millstein & Ryan Wiser & Mark Bolinger & Galen Barbose, 2017. "The climate and air-quality benefits of wind and solar power in the United States," Nature Energy, Nature, vol. 2(9), pages 1-10, September.
    4. Martin Wild, 2016. "Decadal changes in radiative fluxes at land and ocean surfaces and their relevance for global warming," Wiley Interdisciplinary Reviews: Climate Change, John Wiley & Sons, vol. 7(1), pages 91-107, January.
    5. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
    6. Song, Zhe & Ren, Zhigang & Deng, Qinli & Kang, Xin & Zhou, Mi & Liu, Daoru & Chen, Xin, 2020. "General models for estimating daily and monthly mean daily diffuse solar radiation in China's subtropical monsoon climatic zone," Renewable Energy, Elsevier, vol. 145(C), pages 318-332.
    7. Xiaoyuan Li & Denise L. Mauzerall & Mike H. Bergin, 2020. "Global reduction of solar power generation efficiency due to aerosols and panel soiling," Nature Sustainability, Nature, vol. 3(9), pages 720-727, September.
    8. 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.
    9. Huanbi Yue & Chunyang He & Qingxu Huang & Dan Yin & Brett A. Bryan, 2020. "Stronger policy required to substantially reduce deaths from PM2.5 pollution in China," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    10. Boland, John & Huang, Jing & Ridley, Barbara, 2013. "Decomposing global solar radiation into its direct and diffuse components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 749-756.
    11. Zhao, Qun & Yao, Wanxiang & Zhang, Chunxiao & Wang, Xiao & Wang, Yan, 2019. "Study on the influence of fog and haze on solar radiation based on scattering-weakening effect," Renewable Energy, Elsevier, vol. 134(C), pages 178-185.
    12. Liu, Yujun & Yao, Ling & Jiang, Hou & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2022. "Spatial estimation of the optimum PV tilt angles in China by incorporating ground with satellite data," Renewable Energy, Elsevier, vol. 189(C), pages 1249-1258.
    13. 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).
    14. Torres, J.L. & De Blas, M. & García, A. & de Francisco, A., 2010. "Comparative study of various models in estimating hourly diffuse solar irradiance," Renewable Energy, Elsevier, vol. 35(6), pages 1325-1332.
    15. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
    16. 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.
    17. Hafez, A.Z. & Soliman, A. & El-Metwally, K.A. & Ismail, I.M., 2017. "Tilt and azimuth angles in solar energy applications – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 147-168.
    18. Felix Creutzig & Peter Agoston & Jan Christoph Goldschmidt & Gunnar Luderer & Gregory Nemet & Robert C. Pietzcker, 2017. "The underestimated potential of solar energy to mitigate climate change," Nature Energy, Nature, vol. 2(9), pages 1-9, September.
    19. Ridley, Barbara & Boland, John & Lauret, Philippe, 2010. "Modelling of diffuse solar fraction with multiple predictors," Renewable Energy, Elsevier, vol. 35(2), pages 478-483.
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