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The energy security risk assessment of inefficient wind and solar resources under carbon neutrality in China

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  • Sun, Jingbo
  • Wang, Yang
  • He, Yuan
  • Cui, Wenrui
  • Chao, Qingchen
  • Shan, Baoguo
  • Wang, Zheng
  • Yang, Xiaofan

Abstract

To achieve the national objective of “carbon peak and carbon neutrality,” it is imperative to significantly enhance the utilization of renewable energy sources, such as wind and solar power, in the transformation of China's energy infrastructure. However, the intermittent, volatile, and unstable nature of wind and solar power generation systems is often subject to the influence of local weather conditions. Furthermore, recent years have witnessed a growing frequency and intensity of extreme weather events, posing considerable constraints on the effective exploitation of wind and solar resources. Consequently, conducting a comprehensive climate risk analysis pertaining to wind and solar resources is of paramount importance in expediting their adoption while mitigating potential energy security risks. In this study, we conducted an extensive examination of inefficient wind and solar resources across China, encompassing the intensity, spatial distribution, and duration of such events. Leveraging high-resolution wind and solar resource datasets spanning from 2000 to 2021, we employed intensity-area-duration analysis to elucidate the spatial and temporal characteristics of these inefficiencies nationwide. Moreover, we conducted a diagnostic assessment of key factors, including scope, duration, and frequency. Additionally, we proposed a comprehensive index system to evaluate the viability of wind and solar resource development. Our findings revealed that climate risks associated with wind and solar resources were predominantly concentrated in autumn and winter. Regions such as Xinjiang, Sichuan, and the middle reaches of the Yangtze River exhibited a higher frequency of inefficient wind and solar events. Notably, the North China Plain and the middle reaches of the Yangtze River were primarily affected by haze and plum rain weather, respectively, leading to these inefficiencies. Conversely, the northwest and northeast regions demonstrated a superior suitability for wind and solar resource development. This study provides valuable scientific insights for guiding the selection of optimal locations for wind and solar power plants, as well as the development of regional renewable energy infrastructure in China.

Suggested Citation

  • Sun, Jingbo & Wang, Yang & He, Yuan & Cui, Wenrui & Chao, Qingchen & Shan, Baoguo & Wang, Zheng & Yang, Xiaofan, 2024. "The energy security risk assessment of inefficient wind and solar resources under carbon neutrality in China," Applied Energy, Elsevier, vol. 360(C).
  • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924002721
    DOI: 10.1016/j.apenergy.2024.122889
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    References listed on IDEAS

    as
    1. 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.
    2. Lima, Francisco J.L. & Martins, Fernando R. & Pereira, Enio B. & Lorenz, Elke & Heinemann, Detlev, 2016. "Forecast for surface solar irradiance at the Brazilian Northeastern region using NWP model and artificial neural networks," Renewable Energy, Elsevier, vol. 87(P1), pages 807-818.
    3. Li, Mingquan & Virguez, Edgar & Shan, Rui & Tian, Jialin & Gao, Shuo & Patiño-Echeverri, Dalia, 2022. "High-resolution data shows China’s wind and solar energy resources are enough to support a 2050 decarbonized electricity system," Applied Energy, Elsevier, vol. 306(PA).
    4. Ti, Zilong & Deng, Xiao Wei & Yang, Hongxing, 2020. "Wake modeling of wind turbines using machine learning," Applied Energy, Elsevier, vol. 257(C).
    5. Jani, Hardik K. & Kachhwaha, Surendra Singh & Nagababu, Garlapati & Das, Alok, 2022. "Temporal and spatial simultaneity assessment of wind-solar energy resources in India by statistical analysis and machine learning clustering approach," Energy, Elsevier, vol. 248(C).
    6. Neupane, Deependra & Kafle, Sagar & Karki, Kaji Ram & Kim, Dae Hyun & Pradhan, Prajal, 2022. "Solar and wind energy potential assessment at provincial level in Nepal: Geospatial and economic analysis," Renewable Energy, Elsevier, vol. 181(C), pages 278-291.
    7. Jebli, Imane & Belouadha, Fatima-Zahra & Kabbaj, Mohammed Issam & Tilioua, Amine, 2021. "Prediction of solar energy guided by pearson correlation using machine learning," Energy, Elsevier, vol. 224(C).
    8. Laith Abualigah & Raed Abu Zitar & Khaled H. Almotairi & Ahmad MohdAziz Hussein & Mohamed Abd Elaziz & Mohammad Reza Nikoo & Amir H. Gandomi, 2022. "Wind, Solar, and Photovoltaic Renewable Energy Systems with and without Energy Storage Optimization: A Survey of Advanced Machine Learning and Deep Learning Techniques," Energies, MDPI, vol. 15(2), pages 1-26, January.
    9. Colin Raymond & Radley M. Horton & Jakob Zscheischler & Olivia Martius & Amir AghaKouchak & Jennifer Balch & Steven G. Bowen & Suzana J. Camargo & Jeremy Hess & Kai Kornhuber & Michael Oppenheimer & A, 2020. "Understanding and managing connected extreme events," Nature Climate Change, Nature, vol. 10(7), pages 611-621, July.
    10. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
    11. Mayis G. Gulaliyev & Elchin R. Mustafayev & Gulsura Y. Mehdiyeva, 2020. "Assessment of Solar Energy Potential and Its Ecological-Economic Efficiency: Azerbaijan Case," Sustainability, MDPI, vol. 12(3), pages 1-11, February.
    12. Dongdong Song & Haitian Pei & Yuewen Liu & Haiyong Wei & Shengfu Yang & Shougeng Hu, 2022. "Review on Legislative System of Photovoltaic Industry Development in China," Energies, MDPI, vol. 15(1), pages 1-15, January.
    13. Wu, Yunna & Ke, Yiming & Zhang, Ting & Liu, Fangtong & Wang, Jing, 2018. "Performance efficiency assessment of photovoltaic poverty alleviation projects in China: A three-phase data envelopment analysis model," Energy, Elsevier, vol. 159(C), pages 599-610.
    14. Wu, Jie & Wang, Zhi-Xin & Wang, Guo-Qiang, 2014. "The key technologies and development of offshore wind farm in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 453-462.
    15. Bingchun Liu & Shijie Zhao & Xiaogang Yu & Lei Zhang & Qingshan Wang, 2020. "A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model," Energies, MDPI, vol. 13(18), pages 1-17, September.
    16. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
    17. Zhang, Shijie & Wei, Jing & Chen, Xi & Zhao, Yuhao, 2020. "China in global wind power development: Role, status and impact," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    18. Lin, Zi & Liu, Xiaolei, 2020. "Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network," Energy, Elsevier, vol. 201(C).
    19. Allouhi, A. & Zamzoum, O. & Islam, M.R. & Saidur, R. & Kousksou, T. & Jamil, A. & Derouich, A., 2017. "Evaluation of wind energy potential in Morocco's coastal regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 311-324.
    20. Zhang, Yu & Li, Yanting & Zhang, Guangyao, 2020. "Short-term wind power forecasting approach based on Seq2Seq model using NWP data," Energy, Elsevier, vol. 213(C).
    21. Li, Chong & Zhou, Dequn & Zheng, Yuan, 2018. "Techno-economic comparative study of grid-connected PV power systems in five climate zones, China," Energy, Elsevier, vol. 165(PB), pages 1352-1369.
    22. 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.
    23. Seleshi G. Yalew & Michelle T. H. van Vliet & David E. H. J. Gernaat & Fulco Ludwig & Ariel Miara & Chan Park & Edward Byers & Enrica De Cian & Franziska Piontek & Gokul Iyer & Ioanna Mouratiadou & Ja, 2020. "Impacts of climate change on energy systems in global and regional scenarios," Nature Energy, Nature, vol. 5(10), pages 794-802, October.
    24. Mei, H. & Li, Y.P. & Suo, C. & Ma, Y. & Lv, J., 2020. "Analyzing the impact of climate change on energy-economy-carbon nexus system in China," Applied Energy, Elsevier, vol. 262(C).
    25. Zheng, Hanbo & Huang, Wufeng & Zhao, Junhui & Liu, Jiefeng & Zhang, Yiyi & Shi, Zhen & Zhang, Chaohai, 2022. "A novel falling model for wind speed probability distribution of wind farms," Renewable Energy, Elsevier, vol. 184(C), pages 91-99.
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