Preprints
https://doi.org/10.5194/wes-2021-141
https://doi.org/10.5194/wes-2021-141

  23 Nov 2021

23 Nov 2021

Review status: this preprint is currently under review for the journal WES.

Large-eddy simulation of airborne wind energy farms

Thomas Haas1, Jochem De Schutter2, Moritz Diehl2,3, and Johan Meyers1 Thomas Haas et al.
  • 1KU Leuven, Department of Mechanical Engineering, Celestijnenlaan 300, BE-3001 Leuven, Belgium
  • 2University of Freiburg, Department of Microsystems Engineering, Georges-Köhler-Allee 102, DE-79110 Freiburg, Germany
  • 3University of Freiburg, Department of Mathematics, Georges-Köhler-Allee 102, DE-79110 Freiburg, Germany

Abstract. The future utility-scale deployment of airborne wind energy technologies requires the development of large-scale multi-megawatt systems. This study aims at quantifying the interaction between the atmospheric boundary layer (ABL) and large-scale airborne wind energy systems operating in a farm. To that end, we present a virtual flight simulator combining large-eddy simulations to simulate turbulent flow conditions and optimal control techniques for flight-path generation and tracking. The two-way coupling between flow and system dynamics is achieved by implementing an actuator sector method that we pair to a model predictive controller. In this study, we consider ground-based power generation pumping-mode AWE systems (lift-mode AWES) and on-board power generation AWE systems (drag-mode AWES). For the lift-mode AWES, we additionally investigate different reel-out strategies to reduce the interaction between the tethered wing and its own wake. Further, we investigate AWE parks consisting of 25 systems organized in 5 rows of 5 systems. For both lift- and drag-mode archetypes, we consider a moderate park layout with a power density of 10 MW km−2 achieved at a rated wind speed of 12 m s−1. For the drag-mode AWES, an additional park with denser layout and power density of 28 MW km−2 is also considered. The model predictive controller achieves very satisfactory flight-path tracking despite the AWE systems operating in fully waked, turbulent flow conditions. Furthermore, we observe significant wake effects for the utility-scale AWE systems considered in the study. Wake-induced performance losses increase gradually through the downstream rows of systems and reach in the last row of the parks up to 17 % for the lift-mode AWE park and up to 25 % and 45 % for the moderate and dense drag-mode AWE parks, respectively. For an operation period of 60 minutes at a below-rated reference wind speed of 10 m s−1, the lift-mode AWE park generates about 84.4 MW of power, corresponding to 82.5 % of the power yield expected when AWE systems operate ideally and interaction with the ABL is negligible. For the drag-mode AWE parks, the moderate and dense layouts generate about 86.0 MW and 72.9 MW of power, respectively, corresponding to 89.2 % and 75.6 % of the ideal power yield.

Thomas Haas et al.

Status: open (until 04 Jan 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Thomas Haas et al.

Data sets

Large-eddy simulation of airborne wind energy farms: AWES virtual flight data Thomas Haas and Johan Meyers https://doi.org/10.5281/zenodo.5705563

Thomas Haas et al.

Viewed

Total article views: 147 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
101 44 2 147 1 1
  • HTML: 101
  • PDF: 44
  • XML: 2
  • Total: 147
  • BibTeX: 1
  • EndNote: 1
Views and downloads (calculated since 23 Nov 2021)
Cumulative views and downloads (calculated since 23 Nov 2021)

Viewed (geographical distribution)

Total article views: 147 (including HTML, PDF, and XML) Thereof 147 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 26 Nov 2021
Download
Short summary
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight simulator. The virtual flight simulator combines numerical techniques from flow simulation and kite control. Using advanced control algorithms, the systems can operate efficiently in the park despite turbulent flow conditions. For the three configurations considered in the study, we observe significant wake effects reducing the power yield of the parks.