Thursday, May 21, 2026
No menu items!
HomeNatureAdvancing solar and wind penetration in China through energy complementarity

Advancing solar and wind penetration in China through energy complementarity

  • Grams, C. M., Beerli, R., Pfenninger, S., Staffell, I. & Wernli, H. Balancing Europe’s wind-power output through spatial deployment informed by weather regimes. Nat. Clim. Change 7, 557–562 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Lu, X. et al. Challenges faced by China compared with the US in developing wind power. Nat. Energy 1, 16061 (2016).

    Article 
    ADS 

    Google Scholar
     

  • Heide, D. et al. Seasonal optimal mix of wind and solar power in a future, highly renewable Europe. Renew. Energy 35, 2483–2489 (2010).

    Article 

    Google Scholar
     

  • Heide, D., Greiner, M., von Bremen, L. & Hoffmann, C. Reduced storage and balancing needs in a fully renewable European power system with excess wind and solar power generation. Renew. Energy 36, 2515–2523 (2011).

    Article 

    Google Scholar
     

  • Xiao, M., Wetzel, M., Pregger, T., Simon, S. & Scholz, Y. Modeling the supply of renewable electricity to metropolitan regions in China. Energies 13, 3042 (2020).

    Article 

    Google Scholar
     

  • Pedruzzi, R. et al. Review of mapping analysis and complementarity between solar and wind energy sources. Energy 283, 129045 (2023).

    Article 

    Google Scholar
     

  • Engeland, K. et al. Space-time variability of climate variables and intermittent renewable electricity production – a review. Renew. Sustain. Energy Rev. 79, 600–617 (2017).

    Article 

    Google Scholar
     

  • Jurasz, J., Canales, F., Kies, A., Guezgouz, M. & Beluco, A. A review on the complementarity of renewable energy sources: concept, metrics, application and future research directions. Sol. Energy 195, 703–724 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Schindler, D., Behr, H. D. & Jung, C. On the spatiotemporal variability and potential of complementarity of wind and solar resources. Energy Convers. Manag. 218, 113016 (2020).

    Article 

    Google Scholar
     

  • Kapica, J., Canales, F. A. & Jurasz, J. Global atlas of solar and wind resources temporal complementarity. Energy Convers. Manag. 246, 114692 (2021).

    Article 

    Google Scholar
     

  • Hajou, A., El Mghouchi, Y. & Chaoui, M. A new solar-wind complementarity index: an application to the climate of Morocco. Renew. Energy 227, 120490 (2024).

    Article 

    Google Scholar
     

  • Gao, Y. et al. The wind-solar hybrid energy could serve as a stable power source at multiple time scale in China mainland. Energy 305, 132294 (2024).

    Article 

    Google Scholar
     

  • Prasad, A. A., Taylor, R. A. & Kay, M. Assessment of solar and wind resource synergy in Australia. Appl. Energy 190, 354–367 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Hu, J. et al. Reducing energy storage demand by spatial-temporal coordination of multienergy systems. Appl. Energy 329, 120277 (2023).

    Article 
    CAS 

    Google Scholar
     

  • Li, P., Lian, J., Ma, C. & Zhang, J. Complementarity and development potential assessment of offshore wind and solar resources in China seas. Energy Convers. Manag. 296, 117705 (2023).

    Article 

    Google Scholar
     

  • de Souza Nascimento, M. M. et al. Offshore wind and solar complementarity in Brazil: a theoretical and technical potential assessment. Energy Convers. Manag. 270, 116194 (2022).

    Article 

    Google Scholar
     

  • Kruitwagen, L. et al. A global inventory of photovoltaic solar energy generating units. Nature 598, 604–610 (2021).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhang, X., Xu, M., Wang, S., Huang, Y. & Xie, Z. Mapping photovoltaic power plants in China using Landsat, random forest, and Google Earth Engine. Earth Syst. Sci. Data 14, 3743–3755 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Chen, Y., Zhou, J., Ge, Y. & Dong, J. Uncovering the rapid expansion of photovoltaic power plants in China from 2010 to 2022 using satellite data and deep learning. Remote Sens. Environ. 305, 114100 (2024).

    Article 

    Google Scholar
     

  • Xia, Z. et al. Mapping the rapid development of photovoltaic power stations in northwestern China using remote sensing. Energy Rep. 8, 4117–4127 (2022).

    Article 

    Google Scholar
     

  • Jiang, H. et al. Multi-resolution dataset for photovoltaic panel segmentation from satellite and aerial imagery. Earth Syst. Sci. Data Discuss. 2021, 1–17 (2021).

    CAS 

    Google Scholar
     

  • Yu, J., Wang, Z., Majumdar, A. & Rajagopal, R. Deepsolar: a machine learning framework to efficiently construct a solar deployment database in the United States. Joule 2, 2605–2617 (2018).

    Article 

    Google Scholar
     

  • Ortiz, A. et al. An artificial intelligence dataset for solar energy locations in India. Sci. Data 9, 497 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Malof, J. M., Bradbury, K., Collins, L. M. & Newell, R. G. Automatic detection of solar photovoltaic arrays in high resolution aerial imagery. Appl. Energy 183, 229–240 (2016).

    Article 
    ADS 

    Google Scholar
     

  • Zhang, Z. et al. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nat. Commun. 14, 2347 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Mallapaty, S. et al. How China could be carbon neutral by mid-century. Nature 586, 482–483 (2020).

    Article 
    ADS 
    CAS 
    PubMed 

    Google Scholar
     

  • Wang, J. et al. Inherent spatiotemporal uncertainty of renewable power in China. Nat. Commun. 14, 5379 (2023).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • National Energy Administration, China. National Energy Administration releases 2024 national power industry statistics. National Energy Administration https://www.nea.gov.cn/20250121/097bfd7c1cd3498897639857d86d5dac/c.html (2025).

  • Yin, J., Molini, A. & Porporato, A. Impacts of solar intermittency on future photovoltaic reliability. Nat. Commun. 11, 4781 (2020).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Howe, C. As China’s renewable capacity soars, utilisation lags, data show. Reuters https://www.reuters.com/sustainability/climate-energy/chinas-renewable-capacity-soars-utilisation-lags-data-show-2025-08-05/ (2025).

  • Ren, G., Wan, J., Liu, J. & Yu, D. Spatial and temporal assessments of complementarity for renewable energy resources in China. Energy 177, 262–275 (2019).

    Article 

    Google Scholar
     

  • Jani, H. K., Kachhwaha, S. S., Nagababu, G. & Das, A. Temporal and spatial simultaneity assessment of wind-solar energy resources in India by statistical analysis and machine learning clustering approach. Energy 248, 123586 (2022).

    Article 

    Google Scholar
     

  • Santos-Alamillos, F., Pozo-Vázquez, D., Ruiz-Arias, J. A., Von Bremen, L. & Tovar-Pescador, J. Combining wind farms with concentrating solar plants to provide stable renewable power. Renew. Energy 76, 539–550 (2015).

    Article 

    Google Scholar
     

  • Xu, L., Wang, Z. & Liu, Y. The spatial and temporal variation features of wind-sun complementarity in China. Energy Convers. Manag. 154, 138–148 (2017).

    Article 
    ADS 

    Google Scholar
     

  • Jerez, S. et al. An action-oriented approach to make the most of the wind and solar power complementarity. Earth’s Future 11, e2022EF003332 (2023).

    Article 
    ADS 

    Google Scholar
     

  • Guo, Y. et al. Variation-based complementarity assessment between wind and solar resources in China. Energy Convers. Manag. 278, 116726 (2023).

    Article 

    Google Scholar
     

  • Johlas, H., Witherby, S. & Doyle, J. R. Storage requirements for high grid penetration of wind and solar power for the MISO region of North America: a case study. Renew. Energy 146, 1315–1324 (2020).

    Article 

    Google Scholar
     

  • Tong, D. et al. Geophysical constraints on the reliability of solar and wind power worldwide. Nat. Commun. 12, 6146 (2021).

    Article 
    ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Liu, L. et al. Optimizing wind/solar combinations at finer scales to mitigate renewable energy variability in China. Renew. Sustain. Energy Rev. 132, 110151 (2020).

    Article 

    Google Scholar
     

  • Cao, K., Zhou, C., Church, R., Li, X. & Li, W. Revisiting spatial optimization in the era of geospatial big data and GeoAI. Int. J. Appl. Earth Obs. Geoinf. 129, 103832 (2024).


    Google Scholar
     

  • Li, C. & Song, L. Regional differences and spatial convergence of green development in China. Sustainability 14, 8511 (2022).

    Article 
    ADS 

    Google Scholar
     

  • Fang, W. et al. Assessment of wind and solar power potential and their temporal complementarity in China’s northwestern provinces: insights from ERA5 reanalysis. Energies 16, 7109 (2023).

    Article 

    Google Scholar
     

  • Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).

    Article 
    ADS 

    Google Scholar
     

  • Sun, K., Xiao, B., Liu, D. & Wang, J. Deep high-resolution representation learning for human pose estimation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 5693–5703 (IEEE, 2019).

  • Wang, J. et al. Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43, 3349–3364 (2021).

    Article 
    ADS 
    PubMed 

    Google Scholar
     

  • Zhang, H., Wang, Y., Dayoub, F. & Sunderhauf, N. VarifocalNet: an IoU-aware dense object detector. In Proc. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 8514–8523 (IEEE, 2021).

  • Perez, R., Ineichen, P., Seals, R., Michalsky, J. & Stewart, R. Modeling daylight availability and irradiance components from direct and global irradiance. Sol. Energy 44, 271–289 (1990).

    Article 
    ADS 

    Google Scholar
     

  • Reda, I. & Andreas, A. Solar position algorithm for solar radiation applications. Sol. Energy 76, 577–589 (2004).

    Article 
    ADS 

    Google Scholar
     

  • Holmgren, W. F., Hansen, C. W. & Mikofski, M. A. pvlib python: a Python package for modeling solar energy systems. J. Open Source Softw. 3, 884 (2018).

    Article 
    ADS 

    Google Scholar
     

  • Chen, S. et al. The potential of photovoltaics to power the belt and road initiative. Joule 3, 1895–1912 (2019).

    Article 

    Google Scholar
     

  • Hu, W., Scholz, Y., Yeligeti, M., Bremen, L. & Deng, Y. Downscaling ERA5 wind speed data: a machine learning approach considering topographic influences. Environ. Res. Lett. 18, 094007 (2023).

    Article 

    Google Scholar
     

  • Gruber, K., Regner, P., Wehrle, S., Zeyringer, M. & Schmidt, J. Towards global validation of wind power simulations: a multi-country assessment of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the Global Wind Atlas. Energy 238, 121520 (2022).

    Article 

    Google Scholar
     

  • Goldwind. Goldwind sustainability report. Goldwind https://web.archive.org/web/20190819172242/http://goldwindglobal.com/images/about/duty/report/2017.pdf (2017).

  • Feng, J., Feng, L., Wang, J. & King, C. W. Evaluation of the onshore wind energy potential in mainland China—based on GIS modeling and EROI analysis. Resour. Conserv. Recycl. 152, 104484 (2020).

    Article 

    Google Scholar
     

  • Rinne, E., Holttinen, H., Kiviluoma, J. & Rissanen, S. Effects of turbine technology and land use on wind power resource potential. Nat. Energy 3, 494–500 (2018).

    Article 
    ADS 

    Google Scholar
     

  • National Energy Administration, China. Typical load profiles of provincial power grid. National Energy Administration https://www.ndrc.gov.cn/xwdt/tzgg/202012/P020201202546044875868.pdf (2020).

  • Jiang, H. et al. High-resolution analysis of rooftop photovoltaic potential based on hourly generation simulations and load profiles. Appl. Energy 348, 121553 (2023).

    Article 

    Google Scholar
     

  • Edmonds, J. Paths, trees, and flowers. Can. J. Math. 17, 449–467 (1965).

    Article 
    MathSciNet 

    Google Scholar
     

  • Hu, Y. & Liu, Y. China Solar–wind complementarity analysis dataset and code. Zenodo https://doi.org/10.5281/zenodo.19079543 (2026).

  • RELATED ARTICLES

    Most Popular

    Recent Comments