Preprints
https://doi.org/10.5194/wes-2022-112
https://doi.org/10.5194/wes-2022-112
 
09 Dec 2022
09 Dec 2022
Status: this preprint is currently under review for the journal WES.

A new RANS-based wind farm parametrization and inflow model for wind farm cluster modeling

Maarten Paul van der Laan1, Oscar García-Santiago1, Mark Kelly1, Alexander Meyer Forsting1, Camille Dubreuil-Boisclair2, Knut Sponheim Seim2, Marc Imberger1, Alfredo Peña1, Niels Nørmark Sørensen1, and Pierre-Elouan Réthoré1 Maarten Paul van der Laan et al.
  • 1Technical University of Denmark, DTU Wind Energy, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
  • 2Equinor ASA, Oslo, Norway

Abstract. Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Maarten Paul van der Laan et al.

Status: open (until 05 Feb 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2022-112', Gonzalo Pablo Navarro Diaz, 20 Dec 2022 reply
  • RC2: 'Comment on wes-2022-112', Anonymous Referee #2, 26 Jan 2023 reply

Maarten Paul van der Laan et al.

Maarten Paul van der Laan et al.

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Short summary
Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher fidelity numerical models and validated with measurements of an existing wind farm cluster.