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Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network

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  • Liang, Yushi
  • Wu, Chunbing
  • Ji, Xiaodong
  • Zhang, Mulan
  • Li, Yiran
  • He, Jianjun
  • Qin, Zhiheng

Abstract

This study proposes a deep neural network-based wind energy assessment method, aiming to systematically evaluate the effects of the spatiotemporal variations in air density on wind energy assessment in China. On this basis, the spatiotemporal patterns of air density are modelled on a high-spatial-resolution scale (1000 m × 1000 m). The influence of the spatiotemporal distribution characteristics of air density on the wind energy production is quantified, and the spatiotemporal variability of the corresponding wind energy production is estimated. The results demonstrate that changes in air density during the year present typical periodic characteristics. The mean air density value in January is 1.079 kg/m3, the highest throughout the year. The difference in mean air density between cold and warm seasons in the study area shows a decreasing law of higher in the northeast and lower in the southwest. When the elevation is less than 3500 m, it reaches 5.06%. The observed spatiotemporal variability in annual energy production exhibits a distinct seasonal cycle, with the highest production appears in spring (2.968 GWh/yr). The total annual energy production in the cold season is 16.08 GWh/yr, whereas the annual energy production decreases by higher than 23.46% when it comes to the warm season.

Suggested Citation

  • Liang, Yushi & Wu, Chunbing & Ji, Xiaodong & Zhang, Mulan & Li, Yiran & He, Jianjun & Qin, Zhiheng, 2022. "Estimation of the influences of spatiotemporal variations in air density on wind energy assessment in China based on deep neural network," Energy, Elsevier, vol. 239(PC).
    • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024580
    DOI: 10.1016/j.energy.2021.122210
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    as
    1. Kruyt, Bert & Lehning, Michael & Kahl, Annelen, 2017. "Potential contributions of wind power to a stable and highly renewable Swiss power supply," Applied Energy, Elsevier, vol. 192(C), pages 1-11.
    2. Jung, Sungmoon & Arda Vanli, O. & Kwon, Soon-Duck, 2013. "Wind energy potential assessment considering the uncertainties due to limited data," Applied Energy, Elsevier, vol. 102(C), pages 1492-1503.
    3. Shahriari, Mehdi & Blumsack, Seth, 2017. "Scaling of wind energy variability over space and time," Applied Energy, Elsevier, vol. 195(C), pages 572-585.
    4. Nugent, Daniel & Sovacool, Benjamin K., 2014. "Assessing the lifecycle greenhouse gas emissions from solar PV and wind energy: A critical meta-survey," Energy Policy, Elsevier, vol. 65(C), pages 229-244.
    5. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
    6. Carta, José A. & Díaz, Santiago & Castañeda, Alberto, 2020. "A global sensitivity analysis method applied to wind farm power output estimation models," Applied Energy, Elsevier, vol. 280(C).
    7. Carreno-Madinabeitia, Sheila & Ibarra-Berastegi, Gabriel & Sáenz, Jon & Ulazia, Alain, 2021. "Long-term changes in offshore wind power density and wind turbine capacity factor in the Iberian Peninsula (1900–2010)," Energy, Elsevier, vol. 226(C).
    8. Jafarian, M. & Ranjbar, A.M., 2010. "Fuzzy modeling techniques and artificial neural networks to estimate annual energy output of a wind turbine," Renewable Energy, Elsevier, vol. 35(9), pages 2008-2014.
    9. Ucar, Aynur & Balo, Figen, 2009. "Evaluation of wind energy potential and electricity generation at six locations in Turkey," Applied Energy, Elsevier, vol. 86(10), pages 1864-1872, October.
    10. Liu, Fa & Sun, Fubao & Liu, Wenbin & Wang, Tingting & Wang, Hong & Wang, Xunming & Lim, Wee Ho, 2019. "On wind speed pattern and energy potential in China," Applied Energy, Elsevier, vol. 236(C), pages 867-876.
    11. Safari, Bonfils & Gasore, Jimmy, 2010. "A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda," Renewable Energy, Elsevier, vol. 35(12), pages 2874-2880.
    12. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    13. Zhang, Sufang & Li, Xingmei, 2012. "Large scale wind power integration in China: Analysis from a policy perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(2), pages 1110-1115.
    14. Li, Yi & Wu, Xiao-Peng & Li, Qiu-Sheng & Tee, Kong Fah, 2018. "Assessment of onshore wind energy potential under different geographical climate conditions in China," Energy, Elsevier, vol. 152(C), pages 498-511.
    15. Sun, Shengpeng & Liu, Fengliang & Xue, Song & Zeng, Ming & Zeng, Fanxiao, 2015. "Review on wind power development in China: Current situation and improvement strategies to realize future development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 589-599.
    16. Ulazia, Alain & Sáenz, Jon & Ibarra-Berastegi, Gabriel & González-Rojí, Santos J. & Carreno-Madinabeitia, Sheila, 2019. "Global estimations of wind energy potential considering seasonal air density changes," Energy, Elsevier, vol. 187(C).
    17. Ling, Yu & Cai, Xu, 2012. "Exploitation and utilization of the wind power and its perspective in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(4), pages 2111-2117.
    18. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
    19. Vanvyve, Emilie & Delle Monache, Luca & Monaghan, Andrew J. & Pinto, James O., 2015. "Wind resource estimates with an analog ensemble approach," Renewable Energy, Elsevier, vol. 74(C), pages 761-773.
    20. WenminQin, & Wang, Lunche & Gueymard, Christian A. & Bilal, Muhammad & Lin, Aiwen & Wei, Jing & Zhang, Ming & Yang, Xuefang, 2020. "Constructing a gridded direct normal irradiance dataset in China during 1981–2014," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    21. Jung, Christopher & Schindler, Dirk, 2019. "The role of air density in wind energy assessment – A case study from Germany," Energy, Elsevier, vol. 171(C), pages 385-392.
    22. Isabelle Tobin & Robert Vautard & Irena Balog & François-Marie Bréon & Sonia Jerez & Paolo Ruti & Françoise Thais & Mathieu Vrac & Pascal Yiou, 2015. "Assessing climate change impacts on European wind energy from ENSEMBLES high-resolution climate projections," Climatic Change, Springer, vol. 128(1), pages 99-112, January.
    23. 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).
    24. Ren, Guorui & Wan, Jie & Liu, Jinfu & Yu, Daren, 2019. "Characterization of wind resource in China from a new perspective," Energy, Elsevier, vol. 167(C), pages 994-1010.
    25. Gao, Yang & Ma, Shaoxiu & Wang, Tao, 2019. "The impact of climate change on wind power abundance and variability in China," Energy, Elsevier, vol. 189(C).
    26. Alain Ulazia & Gabriel Ibarra-Berastegi & Jon Sáenz & Sheila Carreno-Madinabeitia & Santos J. González-Rojí, 2019. "Seasonal Correction of Offshore Wind Energy Potential due to Air Density: Case of the Iberian Peninsula," Sustainability, MDPI, vol. 11(13), pages 1-22, July.
    27. Liu, Li-qun & Wang, Zhi-xin, 2009. "The development and application practice of wind-solar energy hybrid generation systems in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1504-1512, August.
    28. Eurek, Kelly & Sullivan, Patrick & Gleason, Michael & Hettinger, Dylan & Heimiller, Donna & Lopez, Anthony, 2017. "An improved global wind resource estimate for integrated assessment models," Energy Economics, Elsevier, vol. 64(C), pages 552-567.
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