the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Mesoscale modelling of North Sea wind resources with COSMO-CLM: model evaluation and impact assessment of future wind farm characteristics on cluster-scale wake losses
Marieke Dirksen
Ine L. Wijnant
Andrew Stepek
Ad Stoffelen
Naveed Akhtar
Jérôme Neirynck
Jonas Van de Walle
Johan Meyers
Nicole P. M. van Lipzig
Abstract. As many coastal regions experience a rapid increase in offshore wind farm installations, inter-farm distances become smaller with a tendency to install larger turbines at high capacity densities. It is however not clear how the wake losses in wind farm clusters depend on the characteristics and spacing of the individual wind farms. Here, we quantify this based on multiple COSMO-CLM simulations, each of which assumes a different, spatially invariant combination of the turbine type and capacity density in a projected, future wind farm layout in the North Sea. An evaluation of the modelled wind climate with mast and lidar data for the period 2008–2020 indicates that the frequency distributions of wind speed and wind direction at turbine hub height are skillfully modelled and the seasonal and inter-annual variations in wind speed are represented well. The wind farm simulations indicate that at a capacity density of 8.1 MW km-2 and for SW-winds, inter-farm wakes can reduce the capacity factor at the inflow edge of wind farms from 59 % to between 55 % and 40 % depending on the proximity, size and number of the upwind farms. However, the long-term impact of wake losses in and between wind farms is mitigated by adopting next-generation, 15 MW wind turbines instead of 5 MW turbines, as the annual energy generation over all wind farms in the simulation is increased by 24 % at the same capacity density. In contrast, the impact of wake losses is exacerbated with increasing capacity density, as the layout-integrated, annual capacity factor varies between 54.4 % and 44.3 % over the considered range of 3.5 to 10 MW km-2. Overall, wind farm characteristics and inter-farm distances play an essential role in cluster-scale wake losses, which should be taken into account in future wind farm planning.
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Ruben Borgers et al.
Status: final response (author comments only)
- RC1: 'Comment on wes-2023-33', David Schultz, 15 Apr 2023
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RC2: 'Comment on wes-2023-33', Andrea Hahmann, 23 May 2023
Review of WESC-2023-33
The manuscript presents an excellent contribution to assessing wind resources in the North Sea, which could be limited by extracting kinetic energy from the atmosphere by large wind farms offshore. The work is well embedded in the existing literature and brings enough novelty. The design of the study is robust, and results soundly support the conclusions, and I recommend the publication with two ‘medium’ and a few minor concerns, as follows:
Medium points:
- It will be nice to get an indication of the accuracy of the simulated model stability classes. Since you are using these frequencies to split the wake losses among stability classes, it would be good to know if they relate to reality. We know the SSTs are input to the model simulation, so could we verify the temperature above the sea using buoy data? For the NEWA project, we estimated that the stability classes could be different by as much as 10% when a different PBL was used in the simulations (see Figures 11-13 in NEWA D4.3 report https://backend.orbit.dtu.dk/ws/portalfiles/portal/180023688/2019_05_09_NEWA_D4_3_final.pdf).
- I am missing a discussion on validating the wake farm parameterisation used. It is challenging to validate these parameterisations in terms of far wakes due to the lack of wind farm data and the fact that current wind farms are not yet as large as the ones you simulate. Please indicate an uncertainty based on the literature. Volker et al. (2015), Fischereit et al. (2022) (DOI: doi:10.5194/gmd-8-3715-2015
and DOI: 10.5194/wes-7-1069-2022) and other publications show considerable differences between Fitch and EWP schemes with limited validation data, which are relatively close to the wind farm. How much would this uncertainty affect your CF reductions for the North Sea?
Minor points:
- Please follow the WES guidelines for units (e.g., m/s is not acceptable)
- L22: are -> is
- L28: “and gigawatt-scale wind farms emerge…” I find that maybe the verb tense is not right, future?
- L94. I would add, “However, wind farms have increasingly affected some of the masts used in the validation. “ This is the case for FINO1 and FINO3.
- Some of your symbols are sometimes italics and sometimes not. e.g. R in 104. Also, after the equations. All symbols should be in italics.
- Is there a direct relationship between the PSS and the EMD? I have a feeling it does. It could be good to mention and thus be able to compare your statistics with those of the NEWA simulations.
- L211: This is not an extrapolation, right? The values above and below the sensor height are known. BTW, this is analogous to a log interpolation of the wind speed between the levels. Too much text confuses people. Also, we want to move away from using power law relationships when data for interpolation is available. The way you write this method could give the wrong impression.
- L241. Please use conventional abbreviations (for example, from textbooks) for often-used quantities, e.g. Ri_B.
- Captions of Fig 8-10: Grey shadings represent wind farm locations.
- How are the turbines located in each grid cell? That should be mentioned.
- Extrapolation of each measurement has been shown to work poorly. See Badger et al. (2016) in JAMC, DOI:10.1175/JAMC-D-15-0197.1
- L258-259: Please be more explicit on what data is used to compute the static stability
- Figure 2. Please explicitly name the period used.
- Figure 7. The height at which the maps are computed and the time period details are also missing.
- L360-361. It is not clear what you mean by “data points”. Are these in space or in time? I guess in time, but maybe a time match will be best or a frequency.
- I like the transects in Fig. 8. Especially because they help assess the necessary spacing between wind farm clusters regarding recovery distances. But again, you are missing the height in Fig 8 caption. Please make sure that the captions are complete.
Citation: https://doi.org/10.5194/wes-2023-33-RC2
Ruben Borgers et al.
Data sets
Python scripts and data to create figures 3-10 of the manuscript Ruben Borgers https://doi.org/10.5281/zenodo.7767102
Ruben Borgers et al.
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