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Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis

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  • Jiang, Hou
  • Lu, Ning
  • Yao, Ling
  • Qin, Jun
  • Liu, Tang

Abstract

Climate change alters the amount and spatiotemporal characteristics of solar radiation at the surface. How this affects the stability of solar energy has not yet been explored on a global scale. In this study, we combine ground observations and ERA5 reanalysis to calculate indicators of resource stability and solar intermittency to find evidence of changes in global solar energy stability over the past 20 years. The calculated results of ERA5 are highly consistent with those based on observations, allowing spatially continuous analysis using ERA5 estimates. We find that solar resource availability is on the rise in most regions, with a maximum decadal increase of up to 30 W/m2 and a rate of about 1–2 W/m2/year. The regions of significant downward trend are concentrated in the Northern Hemisphere. In addition, approximately 85% of the world's land is at risk of increasing intermittency, particularly in India, Central and Northern Africa, China, and the United States, which means that adaptation measures to mitigate intermittency should be coordinated in the context of climate change. This study contributes to understanding the climate impacts on solar energy stability and has practical value for future planning and development of solar energy.

Suggested Citation

  • Jiang, Hou & Lu, Ning & Yao, Ling & Qin, Jun & Liu, Tang, 2023. "Impact of climate changes on the stability of solar energy: Evidence from observations and reanalysis," Renewable Energy, Elsevier, vol. 208(C), pages 726-736.
  • Handle: RePEc:eee:renene:v:208:y:2023:i:c:p:726-736
    DOI: 10.1016/j.renene.2023.03.114
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    1. Voyant, Cyril & Notton, Gilles & Kalogirou, Soteris & Nivet, Marie-Laure & Paoli, Christophe & Motte, Fabrice & Fouilloy, Alexis, 2017. "Machine learning methods for solar radiation forecasting: A review," Renewable Energy, Elsevier, vol. 105(C), pages 569-582.
    2. Qin, Jun & Jiang, Hou & Lu, Ning & Yao, Ling & Zhou, Chenghu, 2022. "Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    3. L. Kruitwagen & K. T. Story & J. Friedrich & L. Byers & S. Skillman & C. Hepburn, 2021. "A global inventory of photovoltaic solar energy generating units," Nature, Nature, vol. 598(7882), pages 604-610, October.
    4. Siddharth Joshi & Shivika Mittal & Paul Holloway & Priyadarshi Ramprasad Shukla & Brian Ó Gallachóir & James Glynn, 2021. "High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation," Nature Communications, Nature, vol. 12(1), pages 1-15, December.
    5. Jiang, Hou & Lu, Ning & Qin, Jun & Tang, Wenjun & Yao, Ling, 2019. "A deep learning algorithm to estimate hourly global solar radiation from geostationary satellite data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    6. Jun Yin & Annalisa Molini & Amilcare Porporato, 2020. "Impacts of solar intermittency on future photovoltaic reliability," Nature Communications, Nature, vol. 11(1), pages 1-9, December.
    7. Ravestein, P. & van der Schrier, G. & Haarsma, R. & Scheele, R. & van den Broek, M., 2018. "Vulnerability of European intermittent renewable energy supply to climate change and climate variability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 497-508.
    8. Sarah Feron & Raúl R. Cordero & Alessandro Damiani & Robert B. Jackson, 2021. "Climate change extremes and photovoltaic power output," Nature Sustainability, Nature, vol. 4(3), pages 270-276, March.
    9. Gunnar Luderer & Zoi Vrontisi & Christoph Bertram & Oreane Y. Edelenbosch & Robert C. Pietzcker & Joeri Rogelj & Harmen Sytze Boer & Laurent Drouet & Johannes Emmerling & Oliver Fricko & Shinichiro Fu, 2018. "Residual fossil CO2 emissions in 1.5–2 °C pathways," Nature Climate Change, Nature, vol. 8(7), pages 626-633, July.
    10. Lai, Chun Sing & Jia, Youwei & Lai, Loi Lei & Xu, Zhao & McCulloch, Malcolm D. & Wong, Kit Po, 2017. "A comprehensive review on large-scale photovoltaic system with applications of electrical energy storage," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 439-451.
    11. Li, Jidong & Chen, Shijun & Wu, Yuqiang & Wang, Qinhui & Liu, Xing & Qi, Lijian & Lu, Xiuyuan & Gao, Lu, 2021. "How to make better use of intermittent and variable energy? A review of wind and photovoltaic power consumption in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    12. Rowlands, Ian H. & Kemery, Briana Paige & Beausoleil-Morrison, Ian, 2014. "Managing solar-PV variability with geographical dispersion: An Ontario (Canada) case-study," Renewable Energy, Elsevier, vol. 68(C), pages 171-180.
    13. Sánchez de la Nieta, Agustín A. & Paterakis, Nikolaos G. & Gibescu, Madeleine, 2020. "Participation of photovoltaic power producers in short-term electricity markets based on rescheduling and risk-hedging mapping," Applied Energy, Elsevier, vol. 266(C).
    14. Tripathy, Sujit Kumar & Mitra, Indradip & Heinemann, Detlev & Giridhar, Godugunur & Gomathinayagam, S., 2017. "Impact assessment of short-term variability of solar radiation in Rajasthan using SRRA data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 798-806.
    15. Narvaez, Gabriel & Giraldo, Luis Felipe & Bressan, Michael & Pantoja, Andres, 2021. "Machine learning for site-adaptation and solar radiation forecasting," Renewable Energy, Elsevier, vol. 167(C), pages 333-342.
    16. Zhang, Jianyuan & Zhao, Li & Deng, Shuai & Xu, Weicong & Zhang, Ying, 2017. "A critical review of the models used to estimate solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 314-329.
    17. Bódis, Katalin & Kougias, Ioannis & Jäger-Waldau, Arnulf & Taylor, Nigel & Szabó, Sándor, 2019. "A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    18. Joel R. Norris & Robert J. Allen & Amato T. Evan & Mark D. Zelinka & Christopher W. O’Dell & Stephen A. Klein, 2016. "Evidence for climate change in the satellite cloud record," Nature, Nature, vol. 536(7614), pages 72-75, August.
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