the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Beyond wind-sector management – Optimal wind farm operational planning for balancing fatigue damage and extending lifetime
Abstract. We present an optimization framework for long‑term wind farm operation that balances fatigue loads and energy yield to extend turbine and farm lifetime. The approach plans per‑turbine derating setpoints for discrete wind conditions using an engineering wake model and surrogate models for power and damage. We formulate the problem as a nonlinear program and solve it either for the full farm in a single run or iteratively per turbine. The method is demonstrated on a 9‑turbine farm under onshore and offshore turbulence conditions and across multiple failure modes. For tower bottom bending, modest lifetime extensions of 2–5 years increase net present value (NPV) between (5 % and more than 100 %) with small losses of annual energy production (AEP) of 1–4 % onshore and less than 0.2 % offshore. The per‑turbine optimization achieves similar results as the full farm approach in most cases. Under offshore turbulence, upstream derating more effectively reduces wake‑induced turbulence and the coordinated farm optimization yields additional benefits. When blade edgewise loads are included, lifetime gains require stronger derating due to rotational‑speed sensitivity and reduce economic benefits. The framework produces implementable lookup tables that can be integrated into wind farm planning and control. While results rely on steady wake models and design‑style damage estimation and thus represent first‑order comparisons, they show that targeted derating can redistribute damage and extend lifetime with limited impact on energy.
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RC1: 'Comment on wes-2025-284', Anonymous Referee #1, 11 Mar 2026
This paper presents an optimization approach that considers derating to extend turbine operational life and increase overall energy production over the farm's lifespan. The algorithm uses an engineering wake model with a surrogate model for damage. Results are presented for a small wind farm based on binned steady state conditions derived from ERA5 data, both on- and offshore. The selected operation plans were shown in simulation to improve net present value with moderate reductions in annual energy production, showing the promise of the approach.## Comments1. Please discuss the creation and validation of the surrogate models. How well do they represent real world damage observations? What is the current state-of-the-art w.r.t. component failure predictions?2. How feasible or valuable would it be to include power price in the optimizations, e.g., for farms with strong diurnal variations in power demand?3. Even if the code is not useful for general purposes, it should be shared anyway, as it fully describes the computational methods, whereas prose alone is insufficient. There is no need to make it user friendly or general purpose. If it is code that should be kept secret for commercial reasons, that is an acceptable reason to state as well.## Minor comments1. Line 7: The range 1--4 uses a hyphen instead of an en-dash.2. The hyphen in the title should probably be a colon.3. Line 84: "he framework" should be "The framework".4. Line 89: "tower bm" should be "tower BM".5. Figure 2: Italicization for sub- and superscripts does not match the text.6. Table 3: The italicization here for subscripts does not match the text.7. Figure 3a: The farm layout appears to be measured in meters, not rotor diameters.8. Figure 4: If longer lifetime is preferable, perhaps green or blue would be a better color than red for the color bar.9. Line 476: Typo in the word "specifically".Citation: https://doi.org/
10.5194/wes-2025-284-RC1 -
RC2: 'Comment on wes-2025-284', Anonymous Referee #2, 27 Mar 2026
Overview
This manuscript describes an approach for strategically using derating of individual turbines to achieve lifetime extension and NPV uplift for wind farms. The approach herein uses a time-monolithic nonlinear program-based optimization over the sector average-driven estimates of the AEP performance and lifetime load accumulation using both global and local (in the turbine/farm sense) strategies. The finding is that NPV increases are available by extending the operational lifetime of turbines while decreasing AEP in all present studies except for when limiting blade edgewise loads. Overall this paper is a strong contribution to the literature, and begins to elucidate the relationship between derating strategies and overall economic performance, though many outstanding questions remain, as the authors note, about how to act on the findings.
Major comments
- Overall, I would consider simplifying the notation a little bit and adding some more straightforward and immediate clarification for the definitions- it's taking me a few passes through a few sections to fully understand the material as it's presently presented.
- A key aspect I would like to see foregrounded a little more is what to do with the results here in terms of operational strategy.
- If I were operating a farm in real time, I'm not necessarily compelled to work in this time-monolithic way, but rather projecting out the cost/value of a) what already happened to my farm/what I can detect about it and b) what I see, say 1 decade into operation, as the future circumstances: I might be in a totally different interest rate regime, or cost regime, or whatever, and I'm going to think of fatigue optimal choices w.r.t. this and my projections of the next 10-, 15-, 20-year period.
- There are likely future moments where repowering is the optimal choice given the trajectory I've taken to a given point, and futures in which it absolutely does not make sense to repower at all- and the perspective from which that question is relevant is not a time-monolithic one at all.
- I would like to see a little more explication of the modeling approaches for 1) the surrogate modeling as well as 2) the development of the per-turbine optimization approach.
- The latter is likely to include some impact from ordering to capture non-linear effects, such as in Algo. 1, L4, where optimal choices will propogate to neighboring turbines.
Minor comments
- L17: "For energy, the objective is clear: ..."
- I'm not sure the objective is totally clear especially in the context of a fatigue life study
- rate of return, capacity factor/capacity utilization factor, PPA and debt service periods vs. post-contract pure-profit operations, etc. all should factor into what this objective actually might be
- highly variable depending on actor, regulatory climate, etc.
- L17, "For loads..." and subsequent L18 "In contrast, ..." lines are duplicative; suggest rewording
- L116: XambXamb isn't defined well enough?
- lead with Table 1?
- local conditions (xx): is this upstream of the turbine w/ wake effects from upstream turbines but no induction effects? some clarification, please!
- L126: what are the surrogate models? are there details in here somewhere?
- L288, typo: your "NWP" I think should be "NPV"
- Fig. 17: caption is unclear, appears to be incorrect or at least ambiguous
- on vizualizations with "turbine index", since you have a 9-turbine farm, perhaps use "N", "NE", "E", ..., "NW", and "C" (for center) rather than indices, to help the reader interpret which row/column is impacted by derating choices
Citation: https://doi.org/10.5194/wes-2025-284-RC2
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