| 28 Nov 2025
Estimating annual energy production of wake mixing control strategies including comparisons to wake steering
Abstract. This study presents an estimation of the annual energy production (AEP) associated with active wake mixing (AWM) control strategies in a wind farm. To achieved this, we first conduct a series of high-fidelity large eddy simulations (LES) of a wind farm for various turbine layouts and control parameters. These simulations extend previous findings from two-turbine studies to a larger array of wind turbines, demonstrating the effectiveness of AWM in enhancing power generation, particularly in geometrically aligned wind farms situated in stable atmospheric boundary layers. The results indicate that while the conventional pulse method leads to the best performance for second-row turbines, the helix method leads to greater improvements in power generation for third-row turbines. Second, a framework for estimating the AEP associated with AWM strategies is developed within the FLOw Redirection and Induction in Steady-state (FLORIS) toolkit, using a new empirical Gaussian wake model. The FLORIS parameters are calibrated to the LES data and an optimization routine is established for determining the optimal use of AWM in a wind farm for maximizing AEP. AEP estimates are provided using Weibull data from the New York Bight for multiple turbine layouts and blade pitching amplitudes. Third, the AEP gains from wake mixing are compared to those from wake steering using the yaw optimization routines in FLORIS. The power performance is similar for both control methods, generally leading to power gains of 1 % to 3 % for the wind conditions where active wake mixing or steering is used, which translates to AEP gains that are mostly less than 1 % for the wind farm and control parameters considered in this study.
The manuscript "Estimating annual energy production of wake mixing control strategies including comparisons to wake steering" by Yalla et al. presents a methodology for estimating AEP gains under the application of wake control methods. This methodology is applied to a 3x3 wind farm, where it observes AEP gains of 0.1-0.8%. This is an important contribution to the understanding of wake control strategies and a logical next step in the development of such technologies. Most of the results are presented clearly, and the text is easy to follow. Therefore, the paper should be published in the Journal after considering the following remarks and concerns.Â
Major comments:
My major concerns revolve around the topic of uncertainty, which, given that the observed AEP gains range in very low values, could ultimately decide between positive and negative AEP gains. I understand that a direct determination of the uncertainty is probably not possible. Therefore, the following aspects should be addressed to at least understand and minimize the uncertainty of the presented results, where possible:Â
  1. The entire study is based on one precursor representing a stable ABL in which AWM is assumed to be particularly effective. However, it appears very unlikely that these stable conditions are representative of the flow conditions throughout the entire year, as they are required for the AEP estimation. Consequently, it must be assumed that in a significant part of the year, AWM actually performs worse than assumed in the calculation, hence the AEP gains would be lower. Related to that, the following questions:
    1.1 Does the measurement data from the NY Bight include the atmospheric stability? Can it be justified to assume stable stratification throughout the entire year?Â
    1.2 How would the results change when considering also unstable conditions?
  2. Figure 11 validates the tuned FLORIS model with the LES data. Unfortunately, the FLORIS profiles don't match the LES profiles satisfactorily in all cases. For instance, in the second row, at 4D, the wake deficit is almost double as high in the LES as in FLORIS. This might be the reason for the row-averaged power overprediction of FLORIS visible in Fig. 10. This is concerning because the AEP estimates are expected to be very sensitive to these wake profiles, which raises the following questions:
    2.1. How do the wake profiles compare in cases with different wind directions?Â
    2.2. How does the standard FLORIS model used for the Baseline control cases compare with the LES? If the match is not better, can the authors explain the source of this mismatch? Maybe from using a Gaussian mean flow model that doesn't account for thermal stratification in a stable ABL?Â
    2.3. The model is trained and validated with the same LES data. Maybe additional LES validation case(s) with unseen wind speeds and wind direction could validate the choice of parameters and estimate the uncertainty coming from the wake model.
  3. In lines 332/333, recent studies of synchronizing the helix over multiple rows are used to justify that waked turbines can also apply AWM. However, the cited study from van Vondelen also shows that unfavourable alignment can also reduce the power gains. Therefore, I think a mean flow model would require further adaptation when arguing with synchronization. The question is, therefore, whether the optimizer decides to apply AWM also to mid farm turbines? If yes, it must be assumed that the results are off and consequently increase the uncertainty of the AEP estimates.
=> Considering these points, I would welcome clarification in the title that this is an AEP estimation strategy, but not a generally valid AEP estimate.
Further comments:
  4. Line 112 states that the LES domain is 10 km high. Is that really the case, or is a 0 too much? If yes, why? And if yes, what's the inversion layer height and the lapse rate above the free atmosphere? Vertical profiles would help.Â
  5. Line 113/General: Kasper 2025 also uses a 1km high ABL, where a 3x3 wind farm might be representative for the momentum entrainment mechanisms in a larger wind farm. However, it has been shown that more shallow ABLs fundamentally change these mechanisms, leading to global blockage and atmospheric gravity waves. Do the results shown in the manuscript allow for conclusions on a large-scale wind farm subject to deep array effects?
  6. Line 60/236: What is the full citation of Frederik et al.(2024)? I was not able to find that publication, but it is where the FLORIS part of the work is based on.
  7. Lines 98-100: It is assumed that relative power gains translate from one wind direction to others. What is that assumption based on? Can it be validated?
  8. Table 3/Lines176ff.: Helix method is only investigated in one configuration, in which it actually outperforms the Pulse. Why is it then not considered in the other configurations to investigate if it might even lead to larger AEP gains?Â
  9. Fig. 6,7,8, Lines 206ff: I agree there is not a lot of interaction between the turbines visible. At these small wind farms in the given ABL conditions also deep-array effects can probably be neglected. Could the contributions of this paper also be drawn from three turbines with different streamwise and lateral alignments to reduce computational costs?Â
  10. Fig. 11 shows many lines per figure that only differ in terms of very similar colors. Could the FLORIS and LES lines of the same case be plotted in the same color, but with different line styles? If that doesn't help the readability, maybe consider distributing the lines over multiple plots or plotting less downstream distances
  11. The manuscript would profit from a more direct comparison between the Wake Steering and AWM results, especially because it is explicitly mentioned in the title. That comparison could focus on realistic scenarios. For instance, it seems unreasonable that any operator will ever apply AWM with 6 ° pitch amplitude, and also turbine spacings of 3D seem rather short. Could the same methodology of Fig. 14 maybe be applied to wake steering and then shown in the same plot as AWM to get a direct comparison?
  12. Can wake steering and AWM be combined in the same AEP optimizing routine? Then the AEP would profit from AWM in fully waked conditions and from wake steering in partial wake overlaps. Intuitively, that should further increase the AEP gains.
Minor comments:
  -Lines 38-39: The following might be relevant for the underlying physics of wake mixing:
    -van der Hoek et al.: "Maximizing wind farm power output with the helix approach: Experimental validation and wake analysis using tomographic particle image velocimetry" (https://doi.org/10.1002/we.2896)
    -Coquelet et al.: "Dynamic individual pitch control for wake mitigation: Why does the helix handedness in the wake matter?" (DOI 10.1088/1742-6596/2767/9/092084)
    -Gutknecht et al.: "The impact of coherent large-scale vortices generated by helix active wake control on the recovery process of wind turbine wakes" (https://doi.org/10.1063/5.0278687)
  -Table 3: I would suggest moving the Pulse with St=0.15 from the bottom of the table up to the cases with the same pitch and wind farm orientation cases to facilitate the comparison of the Power gains with the cases in the same conditions.
  -Fig. 5: I recommend plotting different pitch amplitudes in different line styles, to highlight that the lines do not directly compare with each other. This should also be emphasised in the text.
  -Line 231: Citation refers to NREL, but the name was recently changed to NLR. Should that be considered here? It might be worth contacting the Editorial board of the Journal for that.Â
  -Line 272: "All of the LES cases were used" sounds like also the helix and pulse 0.15 case were included in the tuning; however, I assume they were not. This might need clarification in the text.
  -Line 291: "...does not does..."I guess that's a typo
  -Lines 305-315: Description of assumptions feels a bit hard to follow due to quite convoluted sentences. Could these assumptions be outlined in a clearer way?
  -How about the loads? (Sorry, I couldn't resist asking that ;-) )