Preprints
https://doi.org/10.5194/wes-2021-164
https://doi.org/10.5194/wes-2021-164
 
14 Feb 2022
14 Feb 2022
Status: a revised version of this preprint was accepted for the journal WES and is expected to appear here in due course.

Evaluation of Obstacle Modelling Approaches for Resource Assessment and Small Wind Turbine Siting: Case Study in the Northern Netherlands

Caleb Phillips1, Lindsay Sheridan2, Patrick Conry3, Dimitrios K. Fytanidis4, Dimitry Duplyakin1, Sagi Zisman1, Nicolas Duboc3, Matt Nelson3, Rao Kotamarthi4, Rod Linn3, Marc Broersma5, Timo Spijkerboer5, and Heidi Tinnesand1 Caleb Phillips et al.
  • 1National Renewable Energy Laboratory, Golden, CO, USA
  • 2Pacific Northwest National Laboratory, Richland, WA, USA
  • 3Los Alamos National Laboratory, Los Alamos, NM, USA
  • 4Argonne National Laboratory, Argonne, IL, USA
  • 5EAZ Wind, Rijswijk, Netherlands

Abstract. Growth in adoption of distributed wind turbines for energy generation is significantly impacted by challenges associated with siting and accurate estimation of the wind resource. Small turbines, at hub heights of 40 m or less, are greatly impacted by terrestrial obstacles such as built structures and vegetation that can cause complex wake effects. While some progress in high-fidelity complex fluid dynamics (CFD) models has increased the potential accuracy for modelling the impacts of obstacles on turbulent wind flow, these models are too computationally expensive for practical siting and resource assessment applications. To understand the efficacy of available models in situ, this study evaluates classical and commonly used methods alongside new state-of-the-art lower-order models derived from CFD simulations and machine learning approaches. The evaluation is conducted using a subset of an extensive original dataset of measurements from more than 300 operational wind turbines in the northern Netherlands. We find that data driven methods (e.g., machine learning and statistical modelling) are most effective at predicting production at real sites with average error in annual energy production of 2.5 %. When sufficient data may not be available de novo to support these data-driven approaches, models derived from high fidelity simulations show promise and reliably outperform classical methods. On average these models have 6.3–11.5 % error compared to 26 % for classical methods and 27 % baseline error for reanalysis data without obstacle correction. While more performant on average, these methods are also sensitive to the quality of obstacle descriptions and reanalysis inputs.

Caleb Phillips et al.

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-164', Anonymous Referee #1, 14 Mar 2022
  • RC2: 'Comment on wes-2021-164', Anonymous Referee #2, 16 Mar 2022
  • RC3: 'Comment on wes-2021-164', Anonymous Referee #3, 24 Mar 2022
  • EC1: 'Comment on wes-2021-164', Alessandro Bianchini, 29 Mar 2022
  • AC1: 'Comment on wes-2021-164', Caleb Phillips, 05 Apr 2022
  • AC2: 'Comment on wes-2021-164', Caleb Phillips, 12 Apr 2022

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2021-164', Anonymous Referee #1, 14 Mar 2022
  • RC2: 'Comment on wes-2021-164', Anonymous Referee #2, 16 Mar 2022
  • RC3: 'Comment on wes-2021-164', Anonymous Referee #3, 24 Mar 2022
  • EC1: 'Comment on wes-2021-164', Alessandro Bianchini, 29 Mar 2022
  • AC1: 'Comment on wes-2021-164', Caleb Phillips, 05 Apr 2022
  • AC2: 'Comment on wes-2021-164', Caleb Phillips, 12 Apr 2022

Caleb Phillips et al.

Caleb Phillips et al.

Viewed

Total article views: 479 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
327 137 15 479 2 1
  • HTML: 327
  • PDF: 137
  • XML: 15
  • Total: 479
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 14 Feb 2022)
Cumulative views and downloads (calculated since 14 Feb 2022)

Viewed (geographical distribution)

Total article views: 463 (including HTML, PDF, and XML) Thereof 463 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 May 2022
Download
Short summary
Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classical and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data driven methods are most effective at predicting production at real sites and new models reliably outperform classical methods.