A “ceiling” on offshore wind sent Dutch politicians into a panic, but scientists cannot agree on how it was built
A scientific paper claiming to identify a hard upper limit for offshore wind energy output landed in the Dutch parliament with considerable force. Policymakers were told that the Netherlands’ national wind targets assumed capacity factors nearly 50% above what physics would allow — alarming enough to prompt a formal parliamentary hearing.
The paper was built on a deliberately simple analytical model, designed to be usable by non-specialists. A new scientific comment raises a pointed question: what if the ceiling that sent politicians scrambling was never as solid as it appeared?
A model meant to simplify a complex problem
When wind farms multiply across a limited stretch of sea, they start stealing from each other. Turbines downstream operate in the wake of those upstream, receiving slower, more turbulent air — and producing less energy. Quantifying those losses across an entire national fleet is genuinely difficult.
A theoretical upper limit for offshore wind energy extraction
Sophisticated numerical models can do it, but their computational demands put them out of reach for most policymakers. Simão Ferreira et al. stepped into that gap with a deliberately simple analytical model — one operable in a basic spreadsheet. Drawing on established wind energy theory from Frandsen (1992) and Sørensen and Larsen (2021), the model estimates a capacity-factor ceiling for offshore wind farms, validated against 72 real offshore wind farms in European waters.
Why calling it a ‘theoretical limit’ matters
Not all limits are equal. The Betz limit — the maximum fraction of kinetic energy a turbine can extract from the wind — is a true theoretical limit, following directly from conservation of momentum and holding unconditionally regardless of turbine design or location.
The limit proposed by Simão Ferreira et al. is something different. According to the published comment by van der Laan et al., what the paper actually calculates is a normalized gross annual energy production estimate, multiplied by a loss factor. That’s a model-derived bound, not a physics-based ceiling. The paper’s own figures label this quantity as a “theoretical limit” — a framing the critics call misleading. Policymakers reading that label would reasonably assume the constraint is as ironclad as the Betz limit. It isn’t.
The correction that cannot be reproduced
The model’s most consequential weakness sits inside what its authors call the “finite wind farm correction.” Real wind farms aren’t infinite, uniform grids — they have irregular shapes, varying turbine spacing, and neighboring farms that cast wakes of their own.
Simão Ferreira et al. determined this correction by manually counting “freestream turbines” — those operating in undisturbed air — for each of the 72 farms, factoring in layout, wind direction, and neighboring installations. No documented scientific method accompanied these counts. When independent researchers tried to automate the same procedure, results diverged substantially, with differences exceeding 30% for 20 wind farms. When the original authors attempted to reproduce their manual results using an updated automated version during peer review, they couldn’t.
How sensitive is the model — and what does that mean for validation?
The finite wind farm correction isn’t a minor tweak — it’s the dominant parameter. Small changes in how freestream turbines are counted shift the model’s outputs dramatically. When different correction methods are applied to the same 72 wind farms, the model’s explanatory power drops sharply: the original manual method produced an R² of 0.87, while alternative automated approaches yielded significantly lower values.
The validation carries a second problem that’s easy to overlook. Measured capacity factors bundle wake losses together with unrelated losses — grid faults, curtailment, turbine downtime. An assumed additional loss factor of 0.9 is applied to bridge that gap, but the comment notes no compelling empirical justification for that specific value.
Real policy consequences: the Dutch case
Simão Ferreira et al. concluded that Dutch national offshore wind targets imply capacity factors exceeding their modeled limit by 49% — a claim sufficient to trigger a parliamentary hearing.
The critics examine the same Dutch policy references and find a more ambiguous picture. Those documents cite a range of planned capacity densities and expected full-load hours, corresponding to capacity factors between roughly 0.42 and 0.58. The lower end of that range already falls within what the 72 measured wind farms achieve. A planned 10,000 MW installation also wouldn’t be built as one enormous uniform farm; a realistic scenario involves separate 1,000–2,000 MW farms installed over time, with spacing between them.
What the model can and cannot do
None of this means the analytical framework is worthless. It offers genuine value as a screening tool, providing transparent upper and lower bounds on wind farm capacity factors and flagging scenarios where energy density assumptions are becoming physically implausible.
Its appropriate domain is rough estimation for large, relatively uniform wind farm scenarios. For finite, irregularly shaped farms in complex cluster arrangements, higher-fidelity models remain necessary. Simplified models are designed to democratize access to technical knowledge, handing policymakers a usable tool rather than a computational black box. But when a model’s assumptions and limitations aren’t made explicit, its conclusions can carry more authority than the underlying evidence warrants. A spreadsheet that looks like physics can move parliaments.
You can learn more about this discovery here: van der Laan, M. P. and Watson, S.: Comment on “A theoretical upper limit for offshore wind energy extraction” by Simão Ferreira et al. (2026), Wind Energ. Sci., 11, 3763–3774, https://doi.org/10.5194/wes-11-3763-2026, 2026.
Daniel García is an Editor-in-Chief with strong expertise in structural work and engineering principles. He combines this technical foundation with deep knowledge of energy, spatial design, and emerging technologies, bringing a forward-thinking and analytical approach to editorial leadership.
