Markus Sommerfeld PhD Researcher University of Victoria Department of Mechanical Engineering Institute for Integrated Energy Systems
3800 Finnerty Rd Victoria, BC V8W 2Y2 Canada msommerf@uvic.ca www.uvic.ca/research/centres/iesvic/
new leaf
M A N A G E M E N TParametric AWES Sizing Study Using Mesoscale Wind Profiles
Markus Sommerfeld1, Frédéric Bourgault2, Curran Crawford1 1University of Victoria,2New Leaf Management Ltd.
Airborne wind energy systems (AWES) aim to operate at altitudes above conventional wind turbines where wind speeds are generally higher and assumed to be more pre-dictable. However, these devices will still operate within the atmospheric boundary layer, the part of the atmo-sphere which is directly influenced by the contact with earth’s surface. Therefore, a wide range of atmospheric stability conditions develop as a result of synoptic and diurnal weather patterns. These lead to significant vari-ations in wind speeds and wind speed profile shapes [1]. This study presents results from a parametric sizing study of ground-generation AWES. Several representative kite and tether sizes are defined to explore the AWES de-sign space. We estimate average power, annual en-ergy production and dynamic tether loads based on 10-minute modeled wind speed profiles for an onshore lo-cation in Northern Germany [2]. Wind speed profiles from the mesoscale Weather Research and Forecasting Model (WRF) [3] are implemented in the open-source Airborne Wind Energy trajectory optimization toolbox (AWEbox) [4]. To reduce computational costs, the one-year wind speed profile data set is divided up into 20 clusters using a k-means clustering algorithm and representative pro-files are chosen from within each cluster. Results are com-pared to reference logarithmic and uniform wind inflows. The optimization problem is defined to maximize average power over one pumping cycle, subject to tether speed, length and tension constraints.
Estimated annual energy production of a 12m2wing area ground-generation AWES (blue) over mean wind speeds up to 500 m. Wind inflow is based on 10-min onshore WRF model. The background shows the WRF-simulated annual probability distribution of aver-age wind speeds up to 500 m.
References:
[1] Sommerfeld,M. et al: Improving mesoscale wind speed forecasts using LiDAR-based observation nudging for airborne wind energy systems. Wind Energy Science (2019)
[2] Sommerfeld,M. et al: LiDAR-based characterization of mid-altitude wind conditions for airborne wind energy system. Wind En-ergy (2019)
[3] Skamarock,C. et al.: A Description of the Advanced Research WRF Version 3.(2008)
[4] De Schutter,J. et al.: Python toolbox for modelling and op-timal control of multiple-kite systems for Airborne Wind Energy. https://github.com/awebox/awebox
