Understanding Cluster Wake-Induced Energy Losses off the U.S. East Coast

Xia, Geng; Optis, Mike; Deskos, Georgios; Sinner, Michael; Mulas Hernando, Daniel; Lundquist, Julie Kay; Kumler, Andrew; Sanchez Gomez, Miguel; Fleming, Paul; Musial, Walter

doi:https://doi.org/10.5194/wes-2025-154

Preprints

https://doi.org/10.5194/wes-2025-154

Preprints

21 Aug 2025

| 21 Aug 2025

Status: this preprint is currently under review for the journal WES.

Understanding Cluster Wake-Induced Energy Losses off the U.S. East Coast

Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial

Abstract. This study seeks to advance our understanding of energy losses caused by wind farm cluster wakes off the U.S. East Coast by utilizing advanced numerical models in conjunction with real-world, available data on existing and planned offshore wind sites. To this end, we have run simulations of existing and planned U.S. offshore wind lease areas using a typical-meteorological-year approach with a GPU-based Weather Research and Forecasting (WRF) model, where lease area layouts are generated based on most up-to-date project capacity information for each individual lease areas. To evaluate wake losses, we use an energy-loss-based definition of "wake shadow", as opposed to the traditional wind speed deficit assessment. A key insight from this study is that large wind speed deficits do not necessarily translate into significant energy losses. In addition, our results indicate that the conventional wind speed deficit method may underestimate the size of the wake area by up to 30 % compared to the proposed energy loss approach. These findings highlight the need to consider both wind speed deficits and energy losses when evaluating the wake effects of offshore wind farms and assessing future offshore wind development.

Received: 14 Aug 2025 – Discussion started: 21 Aug 2025

Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science. Furthermore, Mike Optis is the founder and president of Veer Renewables, a for-profit consulting company that uses a wind modeling product, WakeMap, which is based on a similar numerical weather prediction modeling framework as the mesoscale simulations described in this paper.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.

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Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial

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RC1: 'Comment on wes-2025-154', Anonymous Referee #1, 21 Aug 2025 reply

General Comments

Overall, this manuscript is well-written and supported by an appropriate number of references, which demonstrates the authors’ thorough engagement with the relevant literature. The use of a GPU-based WRF model is an excellent choice, and the configuration decisions—such as setting the TKE factor to 1 and employing a 1 km mesh resolution at the wind farm—are well-justified and technically sound.

I particularly appreciate the authors’ decision to focus on power loss rather than velocity deficit maps, as this approach provides a more meaningful metric for assessing wind farm performance. The AEP loss map presented in Figure 7 is especially informative and visually compelling.

The analysis showing that large velocity deficits at wind speeds above the turbine’s rated speed (11 m/s) have a limited impact on overall wind farm production is interesting. While this may not represent a groundbreaking finding, it is still valuable to publish, given the historical reliance on velocity deficit maps in WRF parameterization studies. This contribution helps clarify the limitations of traditional approaches and reinforces the importance of using power-based metrics.

Major Revisions

1- One of the most relevant findings in the manuscript relates to stability differences between the surface and the rotor area. This is an important aspect of wake modeling, and I encourage the authors to expand this analysis. Specifically, could you include a comparison with 1/L at the surface, which is a standard WRF output and widely used in the literature? Additionally, it would be helpful to investigate whether there is a larger mismatch between Ri at the surface and at rotor height near the shoreline. Please also clarify in the text how stability classes were defined using Ri values, as this is currently only indicated in Figure 4 and Table 3.

2- The configuration of WRF and the chosen parameterization is appropriate and well-executed. However, the study currently relies on a single model and configuration, which limits its robustness and generalizability. To strengthen the work, I recommend including a comparison with another widely used industry model, such as the Turbopark wake model (for at least one of the wind farms) or the Volker parameterization. This would provide additional context and enhance the credibility of the conclusions.

3- Regarding the contour maps presented in Figure 8, which are among the most impactful results for decision-making on wind farm siting: Is it necessary to simulate a full year to generate these maps? A sensitivity analysis using a shorter simulation period would be highly valuable. Furthermore, if a new wind farm were to be added, would it be necessary to rerun the entire cluster simulation, or could the new farm’s results be combined with the existing data? Addressing these questions would significantly improve the practical applicability of the study.

Minor Revisions

1- The improved parameterization proposed by Vollmer et al. (2024) could potentially influence the conclusions of this work. Would it be feasible to conduct a limited test to confirm that the main findings remain consistent under this updated approach?

2- Additionally, please elaborate on the rationale for selecting the three variables used in the TMY methodology. Would including additional variables such as TKE or stability indicators (Ri or 1/L) improve the representativeness of the dataset? Including a standard wind rose plot for the TMY dataset would also enhance the clarity of the analysis.

3- It would be useful to verify whether a single turbine in the domain reproduces the input power curve when using the parameterization, particularly under stable conditions where low-level jets affect the rotor area. This would help clarify whether some of the observed losses are due to wake effects or to the parameterization’s response to vertical wind profiles.

4- In Figure 6, consider presenting energy loss aggregated at the wind farm level, in addition to the 1 km grid cell resolution. Furthermore, could you explain the energy loss observed far from the wind farms in this figure? Is this related to the well-known WRF parameterization numerical errors in stormy conditions?

5- Finally, for context, are velocity deficit and power loss maps actually used by government agencies for planning purposes? If so, please provide a reference where this application is documented, as this would strengthen the practical relevance of the study.

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Citation: https://doi.org/10.5194/wes-2025-154-RC1

Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial

Data sets

Understanding Cluster Wake-Induced Energy Losses off the U.S. East Coast Geng Xia https://doi.org/10.5281/zenodo.15078171

Geng Xia, Mike Optis, Georgios Deskos, Michael Sinner, Daniel Mulas Hernando, Julie Kay Lundquist, Andrew Kumler, Miguel Sanchez Gomez, Paul Fleming, and Walter Musial

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Latest update: 21 Aug 2025

Short summary

This study examines energy losses from cluster wakes in offshore wind farms along the U.S. East Coast. Simulations based on real lease projects show that large wind speed deficits do not always cause equally large energy losses. The energy loss method revealed wake areas up to 30 % larger than traditional estimates, underscoring the need to consider both wind speed deficit and energy loss in planning offshore wind development.