Characteristics of Wind Profiles for Airborne Wind Energy Systems

Hao He, Xiaojing Niu (), Xiaoyu Li, Yanfeng Cai, Leming Li, Xinwei Ye and Junhao Wang
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Hao He: Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Xiaojing Niu: Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Xiaoyu Li: China Power Engineering Consulting Group Co., Ltd., Beijing 100120, China
Yanfeng Cai: China Energy Engineering Group Guangdong Electric Power Design Institute Co., Ltd., Guangzhou 510663, China
Leming Li: Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Xinwei Ye: Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
Junhao Wang: Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China

Energies, 2025, vol. 18, issue 9, 1-16

Abstract: An airborne wind energy system (AWES) harvests wind at a higher altitude above conventional wind turbines using tethered flying devices. For the design and development of an AWES, we need to know the representative wind speed profile, and its temporal variation is also quite important for the optimization of operation control. This study investigates wind speed profiles up to 3000 m, utilizing ERA5 data spanning from 2000 to 2022 and measured data from a laser wind radar. The long-term averaged wind profile is statistically analyzed, as well as wind profiles with different cumulative probabilities, which are generally consistent with the logarithmic law. Statistical results show that the frequency of negative shear is more than 85% in instantaneous wind profiles, with a greater likelihood at altitudes between 500 m and 1500 m. Fluctuations in wind speed and direction based on 10 min averaged wind speed data have also been provided, which are described by a normal distribution. The wind speed fluctuations primarily concentrate within 2 m/s, with a standard deviation of approximately 0.45 m/s. The wind direction fluctuations are severe at the ground layer and show a rapid decay trend with increasing altitude and averaged wind speed. These results can support the design and control optimization of the AWES.

Keywords: airborne wind energy; wind energy resources; wind profile; LiDAR; reanalysis data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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