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https://doi.org/10.5194/wes-2025-103
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-2025-103
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
26 Jun 2025
| 26 Jun 2025
Status: this preprint is currently under review for the journal WES.
A numerical framework for optimal trade-offs between land use and LCOE using efficient, blockage-aware multi-fidelity methods
Abstract. This paper introduces a novel approach to efficiently estimate the annual energy production (AEP) of a wind farm. The numerical predictions are generated thanks to a multi-fidelity model that combines a classical low-fidelity wake engineering solver with a mid-fidelity computational fluid dynamic solver. The novel setup is not only faster than conventional approaches but is also capable of estimating the AEP of tightly spaced wind farms. Using this approach, we explore the trade-off between land use and levelized cost of energy (LCOE) for a wind farm made of 25 turbines. The results of this study, which ignore impact the layout sensitivity of fatigue loads and their incumbent effect on costs, quantify the penalty on LCOE performance that can be paid to restrict the land use of a wind farm. The results also exhibit the novel capabilities of our approach for multi-fidelity wind farm design to avoid false optima according to incomplete representations of the relevant physical phenomena.
How to cite. Frontin, C., Allen, J., Bay, C. J., Thomas, J., Young, E., and Bortolotti, P.: A numerical framework for optimal trade-offs between land use and LCOE using efficient, blockage-aware multi-fidelity methods, Wind Energ. Sci. Discuss. [preprint], https://doi.org/10.5194/wes-2025-103, in review, 2025.
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RC1: 'Comment on wes-2025-103', Anonymous Referee #1, 26 Aug 2025
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This manuscript present a multi-fidelity model for aerodynamic analysis of wind farms, with an application to wind farm design with varying requirement for land use. Several interesting ideas are presented. The multi-fidelity method is promising to increase the fidelity of aerodynamic models in wind farm design while keeping the computational costs reasonable. In addition, the study on the impact of land-use limit on the performance metrics of the wind farm is valuable for the community. As such, the topic of this work aligns well with the scope of WES. However, and in the reviewer's opinion, this manuscript cannot be published in its current form due to 2 major weaknesses. ÂÂ
First, the scope is poorly defined. Neither the research question nor the scientific challenge addressed in the work are clearly defined. There are two topics addressed in this work: land-use and multi-fidelity models for wind farm designs, but not enough depth in either one. If the focus of the work is on land-use, one would expect an in-depth review of the drivers of wind farm design and an explanation of how wind farm boundaries are decided. If the focus of the work is on the multi-fidelity models, a comparison of state-of-the-art models with varying fidelity would be expected. The lack of focus in the presented manuscript dilutes its message, and makes it difficult to see its relevance and novelty for the research community.
Second, the presentation of the work is poor and impedes the understanding of the work. There is a lack of structure in the text, with several instance of unnecessary repetition. The language used is verbose and unclear. In particular, the use of emphatic terms ("key issue", "clearly demonstrate", etc.) inflates the impact and importance of the results.ÂÂSpecific comments and suggestions for improvement are listed below.ÂMethods and Assumption- Line 17: "A major challenge to achieving this goal is that social and environmental factors are usually harder to pose as straightforward optimization targets. In this work, we take a first step in this direction by studying the trade-off between the levelized cost of energy (LCOE) and land use": This statement is not coherent with work presented in the manuscript. The implementation of a constraint for land use is not difficult, as demonstrated in Eq. (6) with a simple multiplication.
- Line 21: "Land-use [...] dictates the likelihood that a wind farm creates and unwanted negative impact" and line 28 "we assume that land use is correlated to the likelihood that a farm intersects with migratory pathways" can the authors provide a reference to support these statements? Land-use does not necessarily lead to negative effects, consider for example the financial compensation received by land owners/farmers.
- What is the reasoning behind using a LCOE minimization problem and not an AEP maximization problem? Do the balance of station costs change with a change of wake model fidelity? One would expect that the variation of LCOE shown in Figures 9 and 10 can be explained almost fully by the difference in AEP, with the cost component having a negligible effects on the results.
- Line 317: "The results in Fig. 9 demonstrates the necessity of accounting for effects that are not captured by low-fidelity methods" and line 342: "Taken together, these results strongly underscore the importance of incorporating higher-fidelity analyses in optimization". These statements are not backed up by the presented results. What is shown is that models with different fidelity give different results and different performance metrics. Whether or not it is necessary or not to use high fidelity models in design optimization can only be answered if there was a large difference in the optimum (here, the optimal values for L1, L2, theta and phi). It is well known in MDAO applied to wind energy systems that simplified models are used in optimization framework. The performance metrics are then calculated precisely with high-fidelity models.
- The use of a grid-based wind farm parametrization limits the impact of the work for wind farm design problems. The results presented do not allow to disentangle the effect of the parametrization and the effect of the high-fidelity model. A discussion on how to generalize the results for other wind farm layout parametrizations would be very interesting.
- Line 362: "The framework efficiently combines CFD-based and engineering model estimates of the AEP." and line 363 "2 order of magnitude reduction in the computational effort": the efficiency or computational effort of the proposed approach have not been documented in the manuscript (or it is unclear where this analysis is)
Presentation- The manuscript uses the name of several NREL tools (WindSE, FLORIS) to refer to models with different fidelity. For readers not familiar with these tools, this makes it more difficult to understand the work. In general, the reviewer recommends to refer to the model implemented in the tools (engineering wake model, RANS, etc.) rather than the tools, to make the work relevant for a wider audience.
- The XDSM matrix presented in Figure 1 is very relevant in the manuscript, but difficult to read, due to the exclusive use of symbols instead of text.
- Equation (5) is introduced, but it seems that all presented results are the solution of Eq. (6).
- Some symbols used in the equations are not introduced. See for example the symbols A and A_limit in Eq. (6).
- In section 4.1., there is no description nor analysis of the data in Figure 9. The impact of the data follows immediately the introduction of the figure reporting what data is represented. As a result, it is more difficult to understand and follow the reasoning.
- In section 3.2, it would be relevant to put the results in perspective with the literature. Furthermore, a comparison between the multi-fidelity, the low-fidelity and high-fidelity models in terms of accuracy and computational effort is missing to really showcase the relevance of the work.
ÂLanguage Â- There are repetitions in the manuscript. For example with the blockage effect at lines 44 and 56, and with the multi-fidelity gaussian process at lines 284 and 285.
- The following sentences are unclear to the reviewer:
- Line 286: "The standard deviation surfaces demonstrate the regions of relative trust for each Gaussian process surface, which are leveraged by the multi-fidelity fitting process."Â
- Line 290: "At the lowest level, the multi-fidelity response and the trivial, power curve Gaussian process response are identical and given by the trivial response in Fig. 6."Â
- Line 302 "At this point, it has been demonstrated that we can approximate blockage-aware AEP using the multi-fidelity solver to capture key features from the high-fidelity solver at significantly reduced cost."
- Â Line 329: "Figure 10 shows the resulting Pareto front that emerges from the optimizer termination points.": the term "optimization termination points" is not standard in the field and a verbose. The terms "optimum" or "solution of the optimization problems" are generally preferred.
- Line 324: "These LCOE sub-optimalities": using the term "sub-optimality" is not misleading here, because it suggests that the optimization problem was not solved. The solution of the optimization problem is optimal with regards to the implemented model.
Citation: https://doi.org/10.5194/wes-2025-103-RC1
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National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
Jeff Allen
National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
Christopher J. Bay
National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
Jared Thomas
National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
Ethan Young
National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
Pietro Bortolotti
National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, CO 80401, USA
Short summary
Wind farms produce energy and to do so have to occupy a non-trivial amount of space. Understanding how much energy a proposed wind farm will make (and at what cost) is technically challenging, especially when turbines are packed closely together. Plus, there's a key tradeoff in how much space a farm occupies and how cheap the energy it can produce might be: less space means more costly energy. This work shows an novel way to run computational simulations efficiently to understand that tradeoff.
Wind farms produce energy and to do so have to occupy a non-trivial amount of space....
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