Abstract
Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air–surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude.
Highlights
The prospects of higher energy potential, more consistent strong winds and less turbulence in comparison to nearsurface winds has sparked interest in mid-altitude wind energy systems, with “mid-altitude” defined here as heights above 100 m and below 1500 m
Airborne wind energy systems are a novel class of renewable energy technology that harvest stronger winds at altitudes which are unreachable by current wind turbines, at potentially significantly reduced capital cost (Lunney et al, 2017; Fagiano and Milanese, 2012)
Both simulations overpredict horizontal wind speeds at low altitudes, which is a known problem of Weather Research and Forecasting (WRF) and could be attributed to the model not resolving subgrid-scale roughness elements properly or flaws in the planetary boundary layer model; this could lead to overly geostrophic winds over land (Mass and Ovens, 2011)
Summary
The prospects of higher energy potential, more consistent strong winds and less turbulence in comparison to nearsurface winds has sparked interest in mid-altitude wind energy systems, with “mid-altitude” defined here as heights above 100 m and below 1500 m,. The expensive and time-consuming nature of measurements motivates the usage of numerical weather prediction models, such as the mesoscale Weather Research and Forecasting (WRF) model, as adequate tools to assess the synoptic characteristics of the atmospheric boundary layer (ABL) (Al-Yahyai et al, 2010). These models typically have a spatial resolution that ranges from 1 km to tens of kilometers and a temporal resolution of the order of minutes.
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