Articles | Volume 9, issue 3
https://doi.org/10.5194/wes-9-555-2024
© Author(s) 2024. 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-9-555-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Mar 2024
Research article |
| 14 Mar 2024
| 14 Mar 2024
Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production
David Rosencrans
CORRESPONDING AUTHOR
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80303, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
Julie K. Lundquist
Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80303, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
Renewable and Sustainable Energy Institute, Boulder, CO 80303, USA
Mike Optis
National Renewable Energy Laboratory, Golden, CO 80401, USA
Veer Renewables, Courtenay, V9N 9B4, Canada
Alex Rybchuk
National Renewable Energy Laboratory, Golden, CO 80401, USA
Nicola Bodini
National Renewable Energy Laboratory, Golden, CO 80401, USA
Michael Rossol
National Renewable Energy Laboratory, Golden, CO 80401, USA
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Cited
14 citations as recorded by crossref.
- The effects of wind farm wakes on freezing sea spray in the mid-Atlantic offshore wind energy areas D. Rosencrans et al. 10.5194/wes-10-59-2025
- Can mesoscale models capture the effect from cluster wakes offshore? M. Gomez et al. 10.1088/1742-6596/2767/6/062013
- The impact of future UK offshore wind farm distribution and climate change on generation performance and variability J. Giddings et al. 10.1088/1748-9326/ad489b
- Large offshore wind farms have minimal direct impacts on air quality M. Golbazi & C. Archer 10.1088/1748-9326/ad8f47
- Benchmarking Engineering Wake Models for Assessing Wind Farm Wakes Interaction R. Rodrigues & A. Moura 10.1088/1742-6596/3016/1/012045
- Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning Q. Wang et al. 10.1016/j.apenergy.2024.124812
- Simulations suggest offshore wind farms modify low-level jets D. Quint et al. 10.5194/wes-10-117-2025
- Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective Q. Wang et al. 10.1016/j.enconman.2024.119275
- The 2023 National Offshore Wind data set (NOW-23) N. Bodini et al. 10.5194/essd-16-1965-2024
- The future of offshore wind power production: Wake and climate impacts S. Warder & M. Piggott 10.1016/j.apenergy.2024.124956
- Development and validation of a hybrid data-driven model-based wake steering controller and its application at a utility-scale wind plant P. Bachant et al. 10.5194/wes-9-2235-2024
- Benefits and Challenges of California Offshore Wind Electricity: An Updated Assessment A. Rose et al. 10.3390/en18010118
- Exploring the impact of surface waves on wakes using LES in the WRF model O. García-Santiago & A. Peña 10.1088/1742-6596/3016/1/012037
- Identifying Wake Patterns in Weather Regimes over the Southern Bight of the North Sea using clustering techniques A. Palatos-Plexidas et al. 10.1088/1742-6596/3016/1/012046
14 citations as recorded by crossref.
- The effects of wind farm wakes on freezing sea spray in the mid-Atlantic offshore wind energy areas D. Rosencrans et al. 10.5194/wes-10-59-2025
- Can mesoscale models capture the effect from cluster wakes offshore? M. Gomez et al. 10.1088/1742-6596/2767/6/062013
- The impact of future UK offshore wind farm distribution and climate change on generation performance and variability J. Giddings et al. 10.1088/1748-9326/ad489b
- Large offshore wind farms have minimal direct impacts on air quality M. Golbazi & C. Archer 10.1088/1748-9326/ad8f47
- Benchmarking Engineering Wake Models for Assessing Wind Farm Wakes Interaction R. Rodrigues & A. Moura 10.1088/1742-6596/3016/1/012045
- Hierarchical dynamic wake modeling of wind turbine based on physics-informed generative deep learning Q. Wang et al. 10.1016/j.apenergy.2024.124812
- Simulations suggest offshore wind farms modify low-level jets D. Quint et al. 10.5194/wes-10-117-2025
- Towards machine learning applications for structural load and power assessment of wind turbine: An engineering perspective Q. Wang et al. 10.1016/j.enconman.2024.119275
- The 2023 National Offshore Wind data set (NOW-23) N. Bodini et al. 10.5194/essd-16-1965-2024
- The future of offshore wind power production: Wake and climate impacts S. Warder & M. Piggott 10.1016/j.apenergy.2024.124956
- Development and validation of a hybrid data-driven model-based wake steering controller and its application at a utility-scale wind plant P. Bachant et al. 10.5194/wes-9-2235-2024
- Benefits and Challenges of California Offshore Wind Electricity: An Updated Assessment A. Rose et al. 10.3390/en18010118
- Exploring the impact of surface waves on wakes using LES in the WRF model O. García-Santiago & A. Peña 10.1088/1742-6596/3016/1/012037
- Identifying Wake Patterns in Weather Regimes over the Southern Bight of the North Sea using clustering techniques A. Palatos-Plexidas et al. 10.1088/1742-6596/3016/1/012046
Latest update: 05 Jun 2025
Short summary
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants in the US mid-Atlantic, we assess the impacts of wind turbine wakes. While wakes are the strongest and longest during summertime stably stratified conditions, when New England grid demand peaks, they are predictable and thus manageable. Over a year, wakes reduce power output by over 35 %. Wakes in a wind plant contribute the most to that reduction, while wakes between wind plants play a secondary role.
The US offshore wind industry is developing rapidly. Using yearlong simulations of wind plants...
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