Articles | Volume 9, issue 1
https://doi.org/10.5194/wes-9-263-2024
© 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-9-263-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
01 Feb 2024
Research article |
| 01 Feb 2024
| 01 Feb 2024
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
Rebecca Foody
Department of Earth and Atmospheric Science, Cornell University, Ithaca, NY 14850, USA
Jacob Coburn
Department of Earth and Atmospheric Science, Cornell University, Ithaca, NY 14850, USA
Jeanie A. Aird
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
Rebecca J. Barthelmie
Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14850, USA
Department of Earth and Atmospheric Science, Cornell University, Ithaca, NY 14850, USA
Related authors
No articles found.
Tristan Shepherd, Frederick Letson, Rebecca J. Barthelmie, and Sara C. Pryor
Nat. Hazards Earth Syst. Sci., 24, 4473–4505, https://doi.org/10.5194/nhess-24-4473-2024, https://doi.org/10.5194/nhess-24-4473-2024, 2024
Short summary
Short summary
A historic derecho in the USA is presented. The 29 June 2012 derecho caused more than 20 deaths and millions of US dollars of damage. We use a regional climate model to understand how model fidelity changes under different initial conditions. We find changes drive different convective conditions, resulting in large variation in the simulated hazards. The variation using different reanalysis data shows that framing these results in the context of contemporary and future climate is a challenge.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, and Sara C. Pryor
Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, https://doi.org/10.5194/wes-6-1015-2021, 2021
Short summary
Short summary
Low-level jets (LLJs) are pronounced maxima in wind speed profiles affecting wind turbine performance and longevity. We present a climatology of LLJs over Iowa using output from the Weather Research and Forecasting (WRF) model and determine the rotor plane conditions when they occur. LLJ characteristics are highly sensitive to the identification criteria applied, and different (unique) LLJs are extracted with each criterion. LLJ characteristics also vary with different model output resolution.
Frederick W. Letson, Rebecca J. Barthelmie, Kevin I. Hodges, and Sara C. Pryor
Nat. Hazards Earth Syst. Sci., 21, 2001–2020, https://doi.org/10.5194/nhess-21-2001-2021, https://doi.org/10.5194/nhess-21-2001-2021, 2021
Short summary
Short summary
Windstorms during the last 40 years in the US Northeast are identified and characterized using the spatial extent of extreme wind speeds at 100 m height from the ERA5 reanalysis. During all of the top 10 windstorms, wind speeds exceeding the local 99.9th percentile cover at least one-third of the land area in this high-population-density region. These 10 storms followed frequently observed cyclone tracks but have intensities 5–10 times the mean values for cyclones affecting this region.
Frederick Letson, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 5, 331–347, https://doi.org/10.5194/wes-5-331-2020, https://doi.org/10.5194/wes-5-331-2020, 2020
Short summary
Short summary
Wind turbine blade leading edge erosion (LEE) is potentially a significant source of energy loss and expense for wind farm operators. This study presents a novel approach to characterizing LEE potential from precipitation across the contiguous USA based on publicly available National Weather Service dual-polarization RADAR data. The approach is described in detail and illustrated using six locations distributed across parts of the USA that have substantial wind turbine deployments.
Rebecca J. Barthelmie and Sara C. Pryor
Atmos. Meas. Tech., 12, 3463–3484, https://doi.org/10.5194/amt-12-3463-2019, https://doi.org/10.5194/amt-12-3463-2019, 2019
Short summary
Short summary
Wakes are volumes of air with low wind speed that form downwind of wind turbines. Their properties and behaviour determine optimal turbine spacing in wind farms. We use scanning Doppler lidar to accurately and precisely measure wake characteristics at a complex terrain site in Portugal. We develop and apply an automatic processing algorithm to detect wakes and quantify their characteristics. In higher wind speeds, the wake centres are lower. Wake centres are also lower in convective conditions.
Frederick Letson, Rebecca J. Barthelmie, Weifei Hu, and Sara C. Pryor
Atmos. Chem. Phys., 19, 3797–3819, https://doi.org/10.5194/acp-19-3797-2019, https://doi.org/10.5194/acp-19-3797-2019, 2019
Short summary
Short summary
Wind gusts are a key driver of aerodynamic loading, and common approximations used to describe wind gust behavior may not be appropriate in complex terrain at heights relevant to wind turbines and other structures. High-resolution observations from sonic anemometers and vertically pointing Doppler lidars collected in the Perdigão experiment are analyzed to provide a foundation for improved wind gust characterization in complex terrain.
Sara C. Pryor, Tristan J. Shepherd, and Rebecca J. Barthelmie
Wind Energ. Sci., 3, 651–665, https://doi.org/10.5194/wes-3-651-2018, https://doi.org/10.5194/wes-3-651-2018, 2018
Short summary
Short summary
The interannual variability (IAV) of annual energy production (AEP) from wind turbines due to IAV in wind speeds from proposed wind farms plays a key role in dictating project financing but is only poorly constrained. This study provides improved quantification of IAV over eastern N. America using purpose-performed long-term numerical simulations. It may be appropriate to reduce the IAV applied to preconstruction AEP estimates, which would decrease the cost of capital for wind farm developments.
Paula Doubrawa, Alex Montornès, Rebecca J. Barthelmie, Sara C. Pryor, and Pau Casso
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2017-61, https://doi.org/10.5194/wes-2017-61, 2018
Preprint withdrawn
Short summary
Short summary
We perform time-resolved, high-resolution simulations of the atmospheric boundary layer with a numerical weather prediction model. The downscaling is done within the model by defining nested domains, and we investigate different ways of treating turbulence modeling at intermediate spatial scales in which traditional turbulence parameterizations are inadequate. We focus on quantities of interest to wind energy and compare the simulations with measurements collected at a complex-terrain site.
Sara C. Pryor, Ryan C. Sullivan, and Justin T. Schoof
Atmos. Chem. Phys., 17, 14457–14471, https://doi.org/10.5194/acp-17-14457-2017, https://doi.org/10.5194/acp-17-14457-2017, 2017
Short summary
Short summary
The air temperature and water vapor content are increasing globally due to the increased concentration of "heat-trapping" (greenhouse) gases. But not all regions are warming at the same rate. This analysis is designed to improve understanding of the causes of recent trends and year-to-year variability in summertime heat indices over the eastern US and to present a new model that can be used to make projections of future events that may cause loss of life and/or decreased human well-being.
Paola Crippa, Ryan C. Sullivan, Abhinav Thota, and Sara C. Pryor
Atmos. Chem. Phys., 17, 1511–1528, https://doi.org/10.5194/acp-17-1511-2017, https://doi.org/10.5194/acp-17-1511-2017, 2017
Short summary
Short summary
Here we quantify WRF-CHEM sensitivity in simulating meteorological, chemical and aerosol properties as a function of spatial resolution.
We demonstrate that WRF-Chem at high resolution improves model performance of meteorological and gas-phase parameters and of mean and extreme aerosol properties over North America. A dry bias in specific humidity and precipitation in the coarse simulations is identified as cause of the better performance of the high-resolution simulations.
H. Wang, R. J. Barthelmie, P. Doubrawa, and S. C. Pryor
Atmos. Meas. Tech., 9, 4123–4139, https://doi.org/10.5194/amt-9-4123-2016, https://doi.org/10.5194/amt-9-4123-2016, 2016
Short summary
Short summary
This paper investigates how long a sampling duration of lidar measurements should be in order to accurately estimate radial velocity variance to obtain turbulence statistics. Using observations and statistical simulations, it is demonstrated that large probe volumes in lidar measurements increase the autocorrelation values, and consequently the uncertainty in radial velocity variance estimates. It is further shown that the random error can exceed 10 % for 30–60 min sampling duration.
Hui Wang, Rebecca J. Barthelmie, Sara C. Pryor, and Gareth. Brown
Atmos. Meas. Tech., 9, 1653–1669, https://doi.org/10.5194/amt-9-1653-2016, https://doi.org/10.5194/amt-9-1653-2016, 2016
P. Crippa, R. C. Sullivan, A. Thota, and S. C. Pryor
Atmos. Chem. Phys., 16, 397–416, https://doi.org/10.5194/acp-16-397-2016, https://doi.org/10.5194/acp-16-397-2016, 2016
Short summary
Short summary
We evaluate the performance of high-resolution simulations of the Weather Research and Forecasting model coupled with Chemistry in capturing spatiotemporal variability of aerosol optical properties by comparison with ground- and space- based remote-sensing observations and investigate causes of model biases. This work contributes to assessing the model's ability to describe drivers of aerosol direct radiative forcing in the contemporary climate and to improving confidence in future projections.
F. Yu, G. Luo, S. C. Pryor, P. R. Pillai, S. H. Lee, J. Ortega, J. J. Schwab, A. G. Hallar, W. R. Leaitch, V. P. Aneja, J. N. Smith, J. T. Walker, O. Hogrefe, and K. L. Demerjian
Atmos. Chem. Phys., 15, 13993–14003, https://doi.org/10.5194/acp-15-13993-2015, https://doi.org/10.5194/acp-15-13993-2015, 2015
Short summary
Short summary
The role of low-volatility organics in new particle formation (NPF) in the atmosphere is assessed. An empirical formulation in which formation rate is a function of the concentrations of sulfuric acid and low-volatility organics significantly overpredicts NPF in the summer.
Two different schemes predict quite different nucleation rates (including their spatial patterns), concentrations of cloud condensation nuclei, and aerosol first indirect radiative forcing in North America.
S. C. Pryor, K. E. Hornsby, and K. A. Novick
Atmos. Chem. Phys., 14, 11985–11996, https://doi.org/10.5194/acp-14-11985-2014, https://doi.org/10.5194/acp-14-11985-2014, 2014
Short summary
Short summary
What role do forests play in determining the concentration (and composition) of climate-relevant aerosol particles? This study seeks to address two aspects of this question. Firstly, we document high in-canopy removal of recently formed particles. Then we show evidence that growth rates of particles are a function of soil water availability via a reduction in canopy emissions of gases responsible for particle growth to climate-relevant sizes during drought conditions.
J. Ortega, A. Turnipseed, A. B. Guenther, T. G. Karl, D. A. Day, D. Gochis, J. A. Huffman, A. J. Prenni, E. J. T. Levin, S. M. Kreidenweis, P. J. DeMott, Y. Tobo, E. G. Patton, A. Hodzic, Y. Y. Cui, P. C. Harley, R. S. Hornbrook, E. C. Apel, R. K. Monson, A. S. D. Eller, J. P. Greenberg, M. C. Barth, P. Campuzano-Jost, B. B. Palm, J. L. Jimenez, A. C. Aiken, M. K. Dubey, C. Geron, J. Offenberg, M. G. Ryan, P. J. Fornwalt, S. C. Pryor, F. N. Keutsch, J. P. DiGangi, A. W. H. Chan, A. H. Goldstein, G. M. Wolfe, S. Kim, L. Kaser, R. Schnitzhofer, A. Hansel, C. A. Cantrell, R. L. Mauldin, and J. N. Smith
Atmos. Chem. Phys., 14, 6345–6367, https://doi.org/10.5194/acp-14-6345-2014, https://doi.org/10.5194/acp-14-6345-2014, 2014
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
Observations of wind farm wake recovery at an operating wind farm
Periods of constant wind speed: how long do they last in the turbulent atmospheric boundary layer?
Characterization of local wind profiles: a random forest approach for enhanced wind profile extrapolation
Simulations suggest offshore wind farms modify low-level jets
On the lidar-turbulence paradox and possible countermeasures
The actuator farm model for large eddy simulation (LES) of wind-farm-induced atmospheric gravity waves and farm–farm interaction
Understanding the impact of data gaps on long-term offshore wind resource estimates
Converging profile relationships for offshore wind speed and turbulence intensity
An analytical formulation for turbulent kinetic energy added by wind turbines based on large-eddy simulation
A simple steady-state inflow model of the neutral and stable atmospheric boundary layer applied to wind turbine wake simulations
Influences of lidar scanning parameters on wind turbine wake retrievals in complex terrain
Tall Wind Profile Validation Using Lidar Observations and Hindcast Data
Experimental evaluation of wind turbine wake turbulence impacts on a general aviation aircraft
Underestimation of strong wind speeds offshore in ERA5: evidence, discussion and correction
Brief communication: A simple axial induction modification to the Weather Research and Forecasting Fitch wind farm parameterization
Impact of swell waves on atmospheric surface turbulence: wave–turbulence decomposition methods
Flow acceleration statistics: a new paradigm for wind-driven loads, towards probabilistic turbine design
Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network
Method to predict the minimum measurement and experiment durations needed to achieve converged and significant results in a wind energy field experiment
Evaluation of wind farm parameterizations in the WRF model under different atmospheric stability conditions with high-resolution wake simulations
Renewable Energy Complementarity (RECom) maps – a comprehensive visualisation tool to support spatial diversification
Control-oriented modelling of wind direction variability
Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
Offshore low-level jet observations and model representation using lidar buoy data off the California coast
Measurement-driven large-eddy simulations of a diurnal cycle during a wake-steering field campaign
The fractal turbulent–non-turbulent interface in the atmosphere
TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows
The wind farm pressure field
Realistic turbulent inflow conditions for estimating the performance of a floating wind turbine
Brief communication: On the definition of the low-level jet
A decision-tree-based measure–correlate–predict approach for peak wind gust estimation from a global reanalysis dataset
Revealing inflow and wake conditions of a 6 MW floating turbine
Stochastic gradient descent for wind farm optimization
Modelling the impact of trapped lee waves on offshore wind farm power output
Applying a random time mapping to Mann-modeled turbulence for the generation of intermittent wind fields
From shear to veer: theory, statistics, and practical application
Quantification and correction of motion influence for nacelle-based lidar systems on floating wind turbines
Gaussian mixture models for the optimal sparse sampling of offshore wind resource
Dependence of turbulence estimations on nacelle lidar scanning strategies
Vertical extrapolation of Advanced Scatterometer (ASCAT) ocean surface winds using machine-learning techniques
An investigation of spatial wind direction variability and its consideration in engineering models
From gigawatt to multi-gigawatt wind farms: wake effects, energy budgets and inertial gravity waves investigated by large-eddy simulations
Investigations of correlation and coherence in turbulence from a large-eddy simulation
Validation of turbulence intensity as simulated by the Weather Research and Forecasting model off the US northeast coast
On the laminar–turbulent transition mechanism on megawatt wind turbine blades operating in atmospheric flow
Brief communication: A momentum-conserving superposition method applied to the super-Gaussian wind turbine wake model
Turbulence structures and entrainment length scales in large offshore wind farms
Effect of different source terms and inflow direction in atmospheric boundary modeling over the complex terrain site of Perdigão
Comparison of large eddy simulations against measurements from the Lillgrund offshore wind farm
Adjusted spectral correction method for calculating extreme winds in tropical-cyclone-affected water areas
Raghavendra Krishnamurthy, Rob K. Newsom, Colleen M. Kaul, Stefano Letizia, Mikhail Pekour, Nicholas Hamilton, Duli Chand, Donna Flynn, Nicola Bodini, and Patrick Moriarty
Wind Energ. Sci., 10, 361–380, https://doi.org/10.5194/wes-10-361-2025, https://doi.org/10.5194/wes-10-361-2025, 2025
Short summary
Short summary
This study examines how atmospheric phenomena affect the recovery of wind farm wake – the disturbed air behind turbines. In regions like Oklahoma, where wind farms are often clustered, understanding wake recovery is crucial. We found that wind farms can alter phenomena like low-level jets, which are common in Oklahoma, by deflecting them above the wind farm. As a result, the impact of wakes can be observed up to 1–2 km above ground level.
Daniela Moreno, Jan Friedrich, Matthias Wächter, Jörg Schwarte, and Joachim Peinke
Wind Energ. Sci., 10, 347–360, https://doi.org/10.5194/wes-10-347-2025, https://doi.org/10.5194/wes-10-347-2025, 2025
Short summary
Short summary
Unexpected load events measured on operating wind turbines are not accurately predicted by numerical simulations. We introduce the periods of constant wind speed as a possible cause of such events. We measure and characterize their statistics from atmospheric data. Further comparisons to standard modelled data and experimental turbulence data suggest that such events are not intrinsic to small-scale turbulence and are not accurately described by current standard wind models.
Farkhondeh (Hanie) Rouholahnejad and Julia Gottschall
Wind Energ. Sci., 10, 143–159, https://doi.org/10.5194/wes-10-143-2025, https://doi.org/10.5194/wes-10-143-2025, 2025
Short summary
Short summary
In wind energy, precise wind speed prediction at hub height is vital. Our study in the Dutch North Sea reveals that the on-site-trained random forest model outperforms the global reanalysis data, ERA5, in accuracy and precision. Trained within a 200 km range, the model effectively extends the wind speed vertically but experiences bias. It also outperforms ERA5 corrected with measurements in capturing wind speed variations and fine wind patterns, highlighting its potential for site assessment.
Daphne Quint, Julie K. Lundquist, and David Rosencrans
Wind Energ. Sci., 10, 117–142, https://doi.org/10.5194/wes-10-117-2025, https://doi.org/10.5194/wes-10-117-2025, 2025
Short summary
Short summary
Offshore wind farms will be built along the East Coast of the United States. Low-level jets (LLJs) – layers of fast winds at low altitudes – also occur here. LLJs provide wind resources and also influence moisture and pollution transport, so it is important to understand how they might change. We develop and validate an automated tool to detect LLJs and compare 1 year of simulations with and without wind farms. Here, we describe LLJ characteristics and how they change with wind farms.
Alfredo Peña, Ginka G. Yankova, and Vasiliki Mallini
Wind Energ. Sci., 10, 83–102, https://doi.org/10.5194/wes-10-83-2025, https://doi.org/10.5194/wes-10-83-2025, 2025
Short summary
Short summary
Lidars are vastly used in wind energy, but most users struggle when interpreting lidar turbulence measures. Here, we explain the difficulty in converting them into standard measurements. We show two ways of converting lidar to in situ turbulence measurements, both using neural networks: one of them is based on physics, while the other is purely data-driven. They show promising results when compared to high-quality turbulence measurements from a tall mast.
Sebastiano Stipa, Arjun Ajay, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 2301–2332, https://doi.org/10.5194/wes-9-2301-2024, https://doi.org/10.5194/wes-9-2301-2024, 2024
Short summary
Short summary
This study presents the actuator farm model, a new method for modeling wind turbines within large wind farms. The model greatly reduces computational cost when compared to traditional actuator wind turbine models and is beneficial for studying flow around large wind farms as well as the interaction between multiple wind farms. Results obtained from numerical simulations show excellent agreement with past wind turbine models, demonstrating its utility for future large-scale wind farm simulations.
Martin Georg Jonietz Alvarez, Warren Watson, and Julia Gottschall
Wind Energ. Sci., 9, 2217–2233, https://doi.org/10.5194/wes-9-2217-2024, https://doi.org/10.5194/wes-9-2217-2024, 2024
Short summary
Short summary
Offshore wind measurements are often affected by gaps. We investigated how these gaps affect wind resource assessments and whether filling them reduces their effect. We find that the effect of gaps on the estimated long-term wind resource is lower than expected and that data gap filling does not significantly change the outcome. These results indicate a need to reduce current wind data availability requirements for offshore measurement campaigns.
Gus Jeans
Wind Energ. Sci., 9, 2001–2015, https://doi.org/10.5194/wes-9-2001-2024, https://doi.org/10.5194/wes-9-2001-2024, 2024
Short summary
Short summary
An extensive set of met mast data offshore northwestern Europe are used to reduce uncertainty in offshore wind speed and turbulence intensity. The performance of widely used industry standard relationships is quantified, while some new empirical relationships are derived for practical application. Motivations include encouraging appropriate convergence of traditionally separate technical disciplines within the rapidly growing offshore wind energy industry.
Ali Khanjari, Asim Feroz, and Cristina Archer
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-128, https://doi.org/10.5194/wes-2024-128, 2024
Preprint under review for WES
Short summary
Short summary
Wind turbines add turbulence to the atmosphere behind them, especially 4–6 diameters downstream and near the rotor top. We propose an equation that predicts the distribution of added turbulence as a function of a turbine parameter (thrust coefficient) and an atmospheric parameter (undisturbed turbulence intensity before the turbine). We find that our equation performs well, although not perfectly. Eventually this equation can be used to better understand how wind turbines affect the atmosphere.
Maarten Paul van der Laan, Mark Kelly, Mads Baungaard, Antariksh Dicholkar, and Emily Louise Hodgson
Wind Energ. Sci., 9, 1985–2000, https://doi.org/10.5194/wes-9-1985-2024, https://doi.org/10.5194/wes-9-1985-2024, 2024
Short summary
Short summary
Wind turbines are increasing in size and operate more frequently above the atmospheric surface layer, which requires improved inflow models for numerical simulations of turbine interaction. In this work, a novel steady-state model of the atmospheric boundary layer (ABL) is introduced. Numerical wind turbine flow simulations subjected to shallow and tall ABLs are conducted, and the proposed model shows improved performance compared to other state-of-the-art steady-state models.
Rachel Robey and Julie K. Lundquist
Wind Energ. Sci., 9, 1905–1922, https://doi.org/10.5194/wes-9-1905-2024, https://doi.org/10.5194/wes-9-1905-2024, 2024
Short summary
Short summary
Measurements of wind turbine wakes with scanning lidar instruments contain complex errors. We model lidars in a simulated environment to understand how and why the measured wake may differ from the true wake and validate the results with observational data. The lidar smooths out the wake, making it seem more spread out and the slowdown of the winds less pronounced. Our findings provide insights into best practices for accurately measuring wakes with lidar and interpreting observational data.
Etienne Cheynet, Jan Markus Diezel, Hilde Haakenstad, Øyvind Breivik, Alfredo Peña, and Joachim Reuder
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-119, https://doi.org/10.5194/wes-2024-119, 2024
Revised manuscript accepted for WES
Short summary
Short summary
This study aims to help future large offshore wind turbines and airborne wind energy systems by providing insights into wind speeds at much higher altitudes than previously examined. We assessed three wind models (ERA5, NORA3, and NEWA) to predict wind speeds up to 500 m. Using lidar data from Norway and the North Sea, we found that ERA5 excels offshore, while NORA3 performs best onshore. However, the performance of the models depends on the locations and the evaluation criteria.
Jonathan D. Rogers
Wind Energ. Sci., 9, 1849–1868, https://doi.org/10.5194/wes-9-1849-2024, https://doi.org/10.5194/wes-9-1849-2024, 2024
Short summary
Short summary
This paper describes the results of a flight experiment to assess the existence of potential safety risks to a general aviation aircraft from added turbulence in the wake of a wind turbine. A general aviation aircraft was flown through the wake of an operating wind turbine at different downwind distances. Results indicated that there were small increases in disturbances to the aircraft due to added turbulence in the wake, but they never approached levels that would pose a safety risk.
Rémi Gandoin and Jorge Garza
Wind Energ. Sci., 9, 1727–1745, https://doi.org/10.5194/wes-9-1727-2024, https://doi.org/10.5194/wes-9-1727-2024, 2024
Short summary
Short summary
ERA5 has become the workhorse of most wind resource assessment applications, as it compares better with in situ measurements than other reanalyses. However, for design purposes, ERA5 suffers from a drawback: it underestimates strong wind speeds offshore (approx. from 10 m s−1). This is not widely discussed in the scientific literature. We address this bias and proposes a simple, robust correction. This article supports the growing need for use-case-specific validations of reanalysis datasets.
Lukas Vollmer, Balthazar Arnoldus Maria Sengers, and Martin Dörenkämper
Wind Energ. Sci., 9, 1689–1693, https://doi.org/10.5194/wes-9-1689-2024, https://doi.org/10.5194/wes-9-1689-2024, 2024
Short summary
Short summary
This study proposes a modification to a well-established wind farm parameterization used in mesoscale models. The wind speed at the location of the turbine, which is used to calculate power and thrust, is corrected to approximate the free wind speed. Results show that the modified parameterization produces more accurate estimates of the turbine’s power curve.
Mostafa Bakhoday Paskyabi
Wind Energ. Sci., 9, 1631–1645, https://doi.org/10.5194/wes-9-1631-2024, https://doi.org/10.5194/wes-9-1631-2024, 2024
Short summary
Short summary
The exchange of momentum and energy between the atmosphere and ocean depends on air–sea processes, especially wave-related ones. Precision in representing these interactions is vital for offshore wind turbine and farm design and operation. The development of a reliable wave–turbulence decomposition method to remove wave-induced interference from single-height wind measurements is essential for these applications and enhances our grasp of wind coherence within the wave boundary layer.
Mark Kelly
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-69, https://doi.org/10.5194/wes-2024-69, 2024
Revised manuscript accepted for WES
Short summary
Short summary
Industrial standards for wind turbine design are based on 10-minute statistics of wind speed at turbine hub-height, treating fluctuations as turbulence. But recent work shows the effect of strong transients is described by flow accelerations. We devise a method to measure the accelerations turbines encounter; the extremes offshore defy 10-minute statistics, due to various phenomena beyond turbulence. These findings are translated into a recipe supporting statistical turbine design.
Cássia Maria Leme Beu and Eduardo Landulfo
Wind Energ. Sci., 9, 1431–1450, https://doi.org/10.5194/wes-9-1431-2024, https://doi.org/10.5194/wes-9-1431-2024, 2024
Short summary
Short summary
Extrapolating the wind profile for complex terrain through the long short-term memory model outperformed the traditional power law methodology, which due to its universal nature cannot capture local features as the machine-learning methodology does. Moreover, considering the importance of investigating the wind potential and the need for alternative energy sources, it is motivating to find that a short observational campaign can produce better results than the traditional techniques.
Daniel R. Houck, Nathaniel B. de Velder, David C. Maniaci, and Brent C. Houchens
Wind Energ. Sci., 9, 1189–1209, https://doi.org/10.5194/wes-9-1189-2024, https://doi.org/10.5194/wes-9-1189-2024, 2024
Short summary
Short summary
Experiments offer incredible value to science, but results must come with an uncertainty quantification to be meaningful. We present a method to simulate a proposed experiment, calculate uncertainties, and determine the measurement duration (total time of measurements) and the experiment duration (total time to collect the required measurement data when including condition variability and time when measurement is not occurring) required to produce statistically significant and converged results.
Oscar García-Santiago, Andrea N. Hahmann, Jake Badger, and Alfredo Peña
Wind Energ. Sci., 9, 963–979, https://doi.org/10.5194/wes-9-963-2024, https://doi.org/10.5194/wes-9-963-2024, 2024
Short summary
Short summary
This study compares the results of two wind farm parameterizations (WFPs) in the Weather Research and Forecasting model, simulating a two-turbine array under three atmospheric stabilities with large-eddy simulations. We show that the WFPs accurately depict wind speeds either near turbines or in the far-wake areas, but not both. The parameterizations’ performance varies by variable (wind speed or turbulent kinetic energy) and atmospheric stability, with reduced accuracy in stable conditions.
Til Kristian Vrana and Harald G. Svendsen
Wind Energ. Sci., 9, 919–932, https://doi.org/10.5194/wes-9-919-2024, https://doi.org/10.5194/wes-9-919-2024, 2024
Short summary
Short summary
We developed new ways to plot comprehensive wind resource maps that show the revenue potential of different locations for future wind power developments. The relative capacity factor is introduced as an indicator showing the expected mean power output. The market value factor is introduced, which captures the expected mean market value relative to other wind parks. The Renewable Energy Complementarity (RECom) index combines the two into a single index, resulting in the RECom map.
Scott Dallas, Adam Stock, and Edward Hart
Wind Energ. Sci., 9, 841–867, https://doi.org/10.5194/wes-9-841-2024, https://doi.org/10.5194/wes-9-841-2024, 2024
Short summary
Short summary
This review presents the current understanding of wind direction variability in the context of control-oriented modelling of wind turbines and wind farms in a manner suitable to a wide audience. Motivation comes from the significant and commonly seen yaw error of horizontal axis wind turbines, which carries substantial negative impacts on annual energy production and the levellised cost of wind energy. Gaps in the literature are identified, and the critical challenges in this area are discussed.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Ville Vakkari, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 9, 821–840, https://doi.org/10.5194/wes-9-821-2024, https://doi.org/10.5194/wes-9-821-2024, 2024
Short summary
Short summary
Knowing the wind speed across the rotor of a wind turbine is key in making good predictions of the power production. However, models struggle to capture both the speed and the shape of the wind profile. Using machine learning methods based on the model data, we show that the predictions can be improved drastically. The work focuses on three coastal sites, spread over the Northern Hemisphere (the Baltic Sea, the North Sea, and the US Atlantic coast) with similar results for all sites.
Lindsay M. Sheridan, Raghavendra Krishnamurthy, William I. Gustafson Jr., Ye Liu, Brian J. Gaudet, Nicola Bodini, Rob K. Newsom, and Mikhail Pekour
Wind Energ. Sci., 9, 741–758, https://doi.org/10.5194/wes-9-741-2024, https://doi.org/10.5194/wes-9-741-2024, 2024
Short summary
Short summary
In 2020, lidar-mounted buoys owned by the US Department of Energy (DOE) were deployed off the California coast in two wind energy lease areas and provided valuable year-long analyses of offshore low-level jet (LLJ) characteristics at heights relevant to wind turbines. In addition to the LLJ climatology, this work provides validation of LLJ representation in atmospheric models that are essential for assessing the potential energy yield of offshore wind farms.
Eliot Quon
Wind Energ. Sci., 9, 495–518, https://doi.org/10.5194/wes-9-495-2024, https://doi.org/10.5194/wes-9-495-2024, 2024
Short summary
Short summary
Engineering models used to design wind farms generally do not account for realistic atmospheric conditions that can rapidly evolve from minute to minute. This paper uses a first-principles simulation technique to predict the performance of five wind turbines during a wind farm control experiment. Challenges included limited observations and atypical conditions. The simulation accurately predicts the aerodynamics of a turbine when it is situated partially within the wake of an upstream turbine.
Lars Neuhaus, Matthias Wächter, and Joachim Peinke
Wind Energ. Sci., 9, 439–452, https://doi.org/10.5194/wes-9-439-2024, https://doi.org/10.5194/wes-9-439-2024, 2024
Short summary
Short summary
Future wind turbines reach unprecedented heights and are affected by wind conditions that have not yet been studied in detail. With increasing height, a transition to laminar conditions with a turbulent–non-turbulent interface (TNTI) becomes more likely. In this paper, the presence and fractality of this TNTI in the atmosphere are studied. Typical fractalities known from ideal laboratory and numerical studies and a frequent occurrence of the TNTI at heights of multi-megawatt turbines are found.
Sebastiano Stipa, Arjun Ajay, Dries Allaerts, and Joshua Brinkerhoff
Wind Energ. Sci., 9, 297–320, https://doi.org/10.5194/wes-9-297-2024, https://doi.org/10.5194/wes-9-297-2024, 2024
Short summary
Short summary
In the current study, we introduce TOSCA (Toolbox fOr Stratified Convective Atmospheres), an open-source computational fluid dynamics (CFD) tool, and demonstrate its capabilities by simulating the flow around a large wind farm, operating in realistic flow conditions. This is one of the grand challenges of the present decade and can yield better insight into physical phenomena that strongly affect wind farm operation but which are not yet fully understood.
Ronald B. Smith
Wind Energ. Sci., 9, 253–261, https://doi.org/10.5194/wes-9-253-2024, https://doi.org/10.5194/wes-9-253-2024, 2024
Short summary
Short summary
Recent papers have investigated the impact of turbine drag on local wind patterns, but these studies have not given a full explanation of the induced pressure field. The pressure field blocks and deflects the wind and in other ways modifies farm efficiency. Current gravity wave models are complex and provide no estimation tools. We dig deeper into the cause of the pressure field and provide approximate closed-form expressions for pressure field effects.
Cédric Raibaudo, Jean-Christophe Gilloteaux, and Laurent Perret
Wind Energ. Sci., 8, 1711–1725, https://doi.org/10.5194/wes-8-1711-2023, https://doi.org/10.5194/wes-8-1711-2023, 2023
Short summary
Short summary
The work presented here proposes interfacing experimental measurements performed in a wind tunnel with simulations conducted with the aeroelastic code FAST and applied to a floating wind turbine model under wave-induced motion. FAST simulations using experiments match well with those obtained using the inflow generation method provided by TurbSim. The highest surge motion frequencies show a significant decrease in the mean power produced by the turbine and a mitigation of the flow dynamics.
Christoffer Hallgren, Jeanie A. Aird, Stefan Ivanell, Heiner Körnich, Rebecca J. Barthelmie, Sara C. Pryor, and Erik Sahlée
Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, https://doi.org/10.5194/wes-8-1651-2023, 2023
Short summary
Short summary
Low-level jets (LLJs) are special types of non-ideal wind profiles affecting both wind energy production and loads on a wind turbine. However, among LLJ researchers, there is no consensus regarding which definition to use to identify these profiles. In this work, we compare two different ways of identifying the LLJ – the falloff definition and the shear definition – and argue why the shear definition is better suited to wind energy applications.
Serkan Kartal, Sukanta Basu, and Simon J. Watson
Wind Energ. Sci., 8, 1533–1551, https://doi.org/10.5194/wes-8-1533-2023, https://doi.org/10.5194/wes-8-1533-2023, 2023
Short summary
Short summary
Peak wind gust is a crucial meteorological variable for wind farm planning and operations. Unfortunately, many wind farms do not have on-site measurements of it. In this paper, we propose a machine-learning approach (called INTRIGUE, decIsioN-TRee-based wInd GUst Estimation) that utilizes numerous inputs from a public-domain reanalysis dataset, generating long-term, site-specific peak wind gust series.
Nikolas Angelou, Jakob Mann, and Camille Dubreuil-Boisclair
Wind Energ. Sci., 8, 1511–1531, https://doi.org/10.5194/wes-8-1511-2023, https://doi.org/10.5194/wes-8-1511-2023, 2023
Short summary
Short summary
This study presents the first experimental investigation using two nacelle-mounted wind lidars that reveal the upwind and downwind conditions relative to a full-scale floating wind turbine. We find that in the case of floating wind turbines with small pitch and roll oscillating motions (< 1°), the ambient turbulence is the main driving factor that determines the propagation of the wake characteristics.
Julian Quick, Pierre-Elouan Rethore, Mads Mølgaard Pedersen, Rafael Valotta Rodrigues, and Mikkel Friis-Møller
Wind Energ. Sci., 8, 1235–1250, https://doi.org/10.5194/wes-8-1235-2023, https://doi.org/10.5194/wes-8-1235-2023, 2023
Short summary
Short summary
Wind turbine positions are often optimized to avoid wake losses. These losses depend on atmospheric conditions, such as the wind speed and direction. The typical optimization scheme involves discretizing the atmospheric inputs, then considering every possible set of these discretized inputs in every optimization iteration. This work presents stochastic gradient descent (SGD) as an alternative, which randomly samples the atmospheric conditions during every optimization iteration.
Sarah J. Ollier and Simon J. Watson
Wind Energ. Sci., 8, 1179–1200, https://doi.org/10.5194/wes-8-1179-2023, https://doi.org/10.5194/wes-8-1179-2023, 2023
Short summary
Short summary
This modelling study shows that topographic trapped lee waves (TLWs) modify flow behaviour and power output in offshore wind farms. We demonstrate that TLWs can substantially alter the wind speeds at individual wind turbines and effect the power output of the turbine and whole wind farm. The impact on wind speeds and power is dependent on which part of the TLW wave cycle interacts with the wind turbines and wind farm. Positive and negative impacts of TLWs on power output are observed.
Khaled Yassin, Arne Helms, Daniela Moreno, Hassan Kassem, Leo Höning, and Laura J. Lukassen
Wind Energ. Sci., 8, 1133–1152, https://doi.org/10.5194/wes-8-1133-2023, https://doi.org/10.5194/wes-8-1133-2023, 2023
Short summary
Short summary
The current turbulent wind field models stated in the IEC 61400-1 standard underestimate the probability of extreme changes in wind velocity. This underestimation can lead to the false calculation of extreme and fatigue loads on the turbine. In this work, we are trying to apply a random time-mapping technique to one of the standard turbulence models to adapt to such extreme changes. The turbulent fields generated are compared with a standard wind field to show the effects of this new mapping.
Mark Kelly and Maarten Paul van der Laan
Wind Energ. Sci., 8, 975–998, https://doi.org/10.5194/wes-8-975-2023, https://doi.org/10.5194/wes-8-975-2023, 2023
Short summary
Short summary
The turning of the wind with height, which is known as veer, can affect wind turbine performance. Thus far meteorology has only given idealized descriptions of veer, which has not yet been related in a way readily usable for wind energy. Here we derive equations for veer in terms of meteorological quantities and provide practically usable forms in terms of measurable shear (change in wind speed with height). Flow simulations and measurements at turbine heights support these developments.
Moritz Gräfe, Vasilis Pettas, Julia Gottschall, and Po Wen Cheng
Wind Energ. Sci., 8, 925–946, https://doi.org/10.5194/wes-8-925-2023, https://doi.org/10.5194/wes-8-925-2023, 2023
Short summary
Short summary
Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be well understood. A new model for quantification of these effects is introduced in our work.
Robin Marcille, Maxime Thiébaut, Pierre Tandeo, and Jean-François Filipot
Wind Energ. Sci., 8, 771–786, https://doi.org/10.5194/wes-8-771-2023, https://doi.org/10.5194/wes-8-771-2023, 2023
Short summary
Short summary
A novel data-driven method is proposed to design an optimal sensor network for the reconstruction of offshore wind resources. Based on unsupervised learning of numerical weather prediction wind data, it provides a simple yet efficient method for the siting of sensors, outperforming state-of-the-art methods for this application. It is applied in the main French offshore wind energy development areas to provide guidelines for the deployment of floating lidars for wind resource assessment.
Wei Fu, Alessandro Sebastiani, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 8, 677–690, https://doi.org/10.5194/wes-8-677-2023, https://doi.org/10.5194/wes-8-677-2023, 2023
Short summary
Short summary
Nacelle lidars with different beam scanning locations and two types of systems are considered for inflow turbulence estimations using both numerical simulations and field measurements. The turbulence estimates from a sonic anemometer at the hub height of a Vestas V52 turbine are used as references. The turbulence parameters are retrieved using the radial variances and a least-squares procedure. The findings from numerical simulations have been verified by the analysis of the field measurements.
Daniel Hatfield, Charlotte Bay Hasager, and Ioanna Karagali
Wind Energ. Sci., 8, 621–637, https://doi.org/10.5194/wes-8-621-2023, https://doi.org/10.5194/wes-8-621-2023, 2023
Short summary
Short summary
Wind observations at heights relevant to the operation of modern offshore wind farms, i.e. 100 m and more, are required to optimize their positioning and layout. Satellite wind retrievals provide observations of the wind field over large spatial areas and extensive time periods, yet their temporal resolution is limited and they are only representative at 10 m height. Machine-learning models are applied to lift these satellite winds to higher heights, directly relevant to wind energy purposes.
Anna von Brandis, Gabriele Centurelli, Jonas Schmidt, Lukas Vollmer, Bughsin' Djath, and Martin Dörenkämper
Wind Energ. Sci., 8, 589–606, https://doi.org/10.5194/wes-8-589-2023, https://doi.org/10.5194/wes-8-589-2023, 2023
Short summary
Short summary
We propose that considering large-scale wind direction changes in the computation of wind farm cluster wakes is of high relevance. Consequently, we present a new solution for engineering modeling tools that accounts for the effect of such changes in the propagation of wakes. The new model is evaluated with satellite data in the German Bight area. It has the potential to reduce uncertainty in applications such as site assessment and short-term power forecasting.
Oliver Maas
Wind Energ. Sci., 8, 535–556, https://doi.org/10.5194/wes-8-535-2023, https://doi.org/10.5194/wes-8-535-2023, 2023
Short summary
Short summary
The study compares small vs. large wind farms regarding the flow and power output with a turbulence-resolving simulation model. It shows that a large wind farm (90 km length) significantly affects the wind direction and that the wind speed is higher in the large wind farm wake. Both wind farms excite atmospheric gravity waves that also affect the power output of the wind farms.
Regis Thedin, Eliot Quon, Matthew Churchfield, and Paul Veers
Wind Energ. Sci., 8, 487–502, https://doi.org/10.5194/wes-8-487-2023, https://doi.org/10.5194/wes-8-487-2023, 2023
Short summary
Short summary
We investigate coherence and correlation and highlight their importance for disciplines like wind energy structural dynamic analysis, in which blade loading and fatigue depend on turbulence structure. We compare coherence estimates to those computed using a model suggested by international standards. We show the differences and highlight additional information that can be gained using large-eddy simulation, further improving analytical coherence models used in synthetic turbulence generators.
Sheng-Lun Tai, Larry K. Berg, Raghavendra Krishnamurthy, Rob Newsom, and Anthony Kirincich
Wind Energ. Sci., 8, 433–448, https://doi.org/10.5194/wes-8-433-2023, https://doi.org/10.5194/wes-8-433-2023, 2023
Short summary
Short summary
Turbulence intensity is critical for wind turbine design and operation as it affects wind power generation efficiency. Turbulence measurements in the marine environment are limited. We use a model to derive turbulence intensity and test how sea surface temperature data may impact the simulated turbulence intensity and atmospheric stability. The model slightly underestimates turbulence, and improved sea surface temperature data reduce the bias. Error with unrealistic mesoscale flow is identified.
Brandon Arthur Lobo, Özge Sinem Özçakmak, Helge Aagaard Madsen, Alois Peter Schaffarczyk, Michael Breuer, and Niels N. Sørensen
Wind Energ. Sci., 8, 303–326, https://doi.org/10.5194/wes-8-303-2023, https://doi.org/10.5194/wes-8-303-2023, 2023
Short summary
Short summary
Results from the DAN-AERO and aerodynamic glove projects provide significant findings. The effects of inflow turbulence on transition and wind turbine blades are compared to computational fluid dynamic simulations. It is found that the transition scenario changes even over a single revolution. The importance of a suitable choice of amplification factor is evident from the simulations. An agreement between the power spectral density plots from the experiment and large-eddy simulations is seen.
Frédéric Blondel
Wind Energ. Sci., 8, 141–147, https://doi.org/10.5194/wes-8-141-2023, https://doi.org/10.5194/wes-8-141-2023, 2023
Short summary
Short summary
Accurate wind farm flow predictions based on analytical wake models are crucial for wind farm design and layout optimization. Wake superposition methods play a key role and remain a substantial source of uncertainty. In the present work, a momentum-conserving superposition method is extended to the superposition of super-Gaussian-type velocity deficit models, allowing the full wake velocity deficit estimation and design of closely packed wind farms.
Abdul Haseeb Syed, Jakob Mann, Andreas Platis, and Jens Bange
Wind Energ. Sci., 8, 125–139, https://doi.org/10.5194/wes-8-125-2023, https://doi.org/10.5194/wes-8-125-2023, 2023
Short summary
Short summary
Wind turbines extract energy from the incoming wind flow, which needs to be recovered. In very large offshore wind farms, the energy is recovered mostly from above the wind farm in a process called entrainment. In this study, we analyzed the effect of atmospheric stability on the entrainment process in large offshore wind farms using measurements recorded by a research aircraft. This is the first time that in situ measurements are used to study the energy recovery process above wind farms.
Kartik Venkatraman, Trond-Ola Hågbo, Sophia Buckingham, and Knut Erik Teigen Giljarhus
Wind Energ. Sci., 8, 85–108, https://doi.org/10.5194/wes-8-85-2023, https://doi.org/10.5194/wes-8-85-2023, 2023
Short summary
Short summary
This paper is focused on the impact of modeling different effects, such as forest canopy and Coriolis forces, on the wind resource over a complex terrain site located near Perdigão, Portugal. A numerical model is set up and results are compared with field measurements. The results show that including a forest canopy improves the predictions close to the ground at some locations on the site, while the model with inflow from a precursor performed better at other locations.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Xiaoli Guo Larsén and Søren Ott
Wind Energ. Sci., 7, 2457–2468, https://doi.org/10.5194/wes-7-2457-2022, https://doi.org/10.5194/wes-7-2457-2022, 2022
Short summary
Short summary
A method is developed for calculating the extreme wind in tropical-cyclone-affected water areas. The method is based on the spectral correction method that fills in the missing wind variability to the modeled time series, guided by best track data. The paper provides a detailed recipe for applying the method and the 50-year winds of equivalent 10 min temporal resolution from 10 to 150 m in several tropical-cyclone-affected regions.
Cited articles
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: WRF-simulated low-level jets over Iowa: characterization and sensitivity studies, Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, 2021.
Aird, J. A., Barthelmie, R. J., Shepherd, T. J., and Pryor, S. C.: Occurrence of Low-Level Jets over the Eastern US Coastal Zone at Heights Relevant to Wind Energy, Energies 15, 445, https://doi.org/10.3390/en15020445, 2022.
American Clean Power: Clean Power Quarterly 2023 Q1, American Clean Power, https://cleanpower.org/resources/clean-power-quarterly-market-report-q1-2023/ (last access: 3 August 2023), 135, 2023.
Ayodele, T. R., Jimoh, A., Munda, J. L., and Tehile, A. J.: Challenges of grid integration of wind power on power system grid integrity: A review, International Journal of Renewable Energy Research, 2, 618–626, 2012.
Balsley, B. B., Frehlich, R. G., Jensen, M. L., Meillier, Y., and Muschinski, A.: Extreme gradients in the nocturnal boundary layer: Structure, evolution, and potential causes, J. Atmos. Sci., 60, 2496–2508, 2003.
Bamigbola, O. M., Ali, M. M., and Oke, M. O.: Mathematical modeling of electric power flow and the minimization of power losses on transmission lines, Appl. Math. Comput., 241, 214–221, 2014.
Barthelmie, R. J., Grisogono, B., and Pryor, S. C.: Observations and simulations of diurnal cycles of near-surface wind speeds over land and sea, J. Geophys. Res., 101, 21327–321337, https://doi.org/10.1029/96JD01520, 1996.
Barthelmie, R. J., Murray, F., and Pryor, S. C.: The economic benefit of short-term forecasting for wind energy in the UK electricity market, Energ. Policy, 36, 1687–1696, 2008.
Barthelmie, R. J., Doubrawa, P., Wang, H., Giroux, G., and Pryor, S. C.: Effects of an escarpment on flow parameters of relevance to wind turbines, Wind Energy, 19, 2271–2286, 2016.
Barthelmie, R. J., Shepherd, T. J., Aird, J. A., and Pryor, S. C.: Power and wind shear implications of large wind turbine scenarios in the U.S. Central Plains, Energies, 13, 4269 https://doi.org/10.3390/en13164269, 2020.
Barthelmie, R. J., Larsen, G. C., and Pryor, S. C.: Modeling Annual Electricity Production and Levelized Cost of Energy from the US East Coast Offshore Wind Energy Lease Areas, Energies, 16, 4550, https://doi.org/10.3390/en16124550, 2023.
Bingöl, F., Mann, J., and Larsen, G. C.: Light detection and ranging measurements of wake dynamics part I: one-dimensional scanning, Wind Energy, 13, 51–61, https://doi.org/10.1002/we.352, 2010.
Bischoff, O., Wurth, I., Gottschall, J., Gribben, B., Hughes, J., Stein, D., and Verhoef, H.: Floating Lidar Systems, IEA Expert Group Report on Recommended Practices, IEA Wind TCP RP 18 from Task 32, 89, https://iea-wind.org/portfolio-item/recommended-practice-18/ (last access: 3 August 2023), 2017.
Bistline, J. E. and Blanford, G. J.: The role of the power sector in net-zero energy systems, Energy and Climate Change, 2, 100045, https://doi.org/10.1016/j.egycc.2021.100045, 2021.
Blackadar, A. K.: Boundary layer wind maxima and their significance for the growth of nocturnal inversions, B. Am. Meteorol. Soc., 38, 283–290, 1957.
BOEM: Outer Continental Shelf Renewable Energy Leases Map Book, 33 pp., https://www.boem.gov/sites/default/files/
documents/renewable-energy/Outer%20Continental%20Shelf
%20Renewable%20Energy%20Leases%20January%202023b.
pdf (last access: 14 March 2023), 2023.
Brotzge, J. A., Wang, J., Thorncroft, C., Joseph, E., Bain, N., Bassill, N., Farruggio, N., Freedman, J., Hemker Jr, K., and Johnston, D.: A technical overview of the New York State Mesonet standard network, J. Atmos. Ocean. Tech., 37, 1827–1845, 2020 (data available at: http://www.nysmesonet.org/, last access: 3 August 2023).
Burleyson, C. D., Rahman, A., Rice, J. S., Smith, A. D., and Voisin, N.: Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States, Appl. Energ., 304, 117711, https://doi.org/10.1016/j.apenergy.2021.117711, 2021.
Castillo, V. Z., De Boer, H.-S., Muñoz, R. M., Gernaat, D. E., Benders, R., and van Vuuren, D.: Future global electricity demand load curves, Energy, 258, 124741, https://doi.org/10.1016/j.erss.2019.101337, 2022.
Coburn, J. J. and Pryor, S. C.: Projecting future energy production from operating wind farms in North America: Part 3: Variability, J. Appl. Meteorol. Clim., 62, 1523–1537, 2023.
DeMarco, A. and Basu, S.: On the tails of the wind ramp distributions, Wind Energy, 21, 892–905, 2018.
Diógenes, J. R. F., Claro, J., Rodrigues, J. C., and Loureiro, M. V.: Barriers to onshore wind energy implementation: A systematic review, Energy Research & Social Science, 60, 101337, https://doi.org/10.1016/j.tej.2020.106829, 2020.
Enevoldsen, P. and Jacobson, M. Z.: Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide, Energy Sustain. Dev., 60, 40–51, https://doi.org/10.1016/j.esd.2020.11.004, 2021.
Eryilmaz, D., Patria, M., and Heilbrun, C.: Assessment of the COVID-19 pandemic effect on regional electricity generation mix in NYISO, MISO and PJM markets, Electricity Journal, 33, 106829, https://doi.org/10.1088/1742-6596/1934/1/012001, 2020.
Esteban, M. D., Diez, J. J., López, J. S., and Negro, V.: Why offshore wind energy?, Renew. Energ., 36, 444–450, 2011.
Frehlich, R.: Simulation of coherent Doppler lidar performance in the weak-signal regime, J. Atmos. Ocean. Tech., 13, 646–658, 1996.
Gadde, S. N., Liu, L., and Stevens, R. J.: Effect of low-level jet on turbine aerodynamic blade loading using large-eddy simulations, J. Phys. Conf. Ser., 2021, 012001, https://doi.org/10.1088/1742-6596/1934/1/012001, 2021.
Gaertner, E., Rinker, J., Sethuraman, L., Zahle, Z., Anderson, B., Barter, G., Abbas, B., Meng, F., Bortolotti, F., Skrzypinski, W., Scott, G., Feil, R., Bredmose, H., Dykes, K., Shields, M., Allen, C., and Viselli, A.: Definition of the IEA 15-Megawatt Offshore Reference Wind Turbine, National Renewable Energy Laboratory, Golden, CO, NREL/TP-5000-75698, https://www.nrel.gov/docs/fy20osti/75698.pdf (last access: 3 August 2023), 2020 (code available at: https://github.com/IEAWindTask37/IEA-15-240-RWT, last access: 31 January 2024).
Gramcianinov, C. B., Campos, R. M., de Camargo, R., Hodges, K. I., Guedes Soares, C., and da Silva Dias, P. L.: Analysis of Atlantic extratropical storm tracks characteristics in 41 years of ERA5 and CFSR/CFSv2 databases, Ocean Eng., 216, 108111, https://doi.org/10.1016/j.oceaneng.2020.108111, 2020.
Gryning, S.-E., and Floors, R.: Carrier-to-noise-threshold filtering on off-shore wind lidar measurements, Sensors, 19, 592, https://doi.org/10.3390/s19030592, 2019.
Gryning, S.-E., Floors, R., Peña, A., Batchvarova, E., and Brümmer, B.: Weibull wind-speed distribution parameters derived from a combination of wind-lidar and tall-mast measurements over land, coastal and marine sites, Bound.-Lay. Meteorol., 159, 329–348, 2016.
Gutierrez, W., Ruiz-Columbie, A., Tutkun, M., and Castillo, L.: The structural response of a wind turbine under operating conditions with a low-level jet, Renewable and Sustainable Energy Reviews, 108, 380–391, 2019.
Haghi, H. V., Bina, M. T., and Golkar, M. A.: Nonlinear modeling of temporal wind power variations, IEEE T. Sustain. Energ., 4, 838–848, 2013.
Hallgren, C., Arnqvist, J., Ivanell, S., Körnich, H., Vakkari, V., and Sahlée, E.: Looking for an offshore low-level jet champion among recent reanalyses: a tight race over the Baltic Sea, Energies, 13, 3670, https://doi.org/10.3390/en13143670, 2020.
Hallgren, C., Aird, J. A., Ivanell, S., Körnich, H., Barthelmie, R. J., Pryor, S. C., and Sahlée, E.: Brief communication: On the definition of the low-level jet, Wind Energ. Sci., 8, 1651–1658, https://doi.org/10.5194/wes-8-1651-2023, 2023.
Haslett, J. and Raftery, A. E.: Space-time modelling with long-memory dependence: Assessing Ireland's wind power resource, J. R. Stat. Soc. C-Appl., 38, 1–21, 1989.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Holton, J. R.: The diurnal boundary layer wind oscillation above sloping terrain, Tellus, 19, 200–205, 1967.
Hur, S., Recalde-Camacho, L., and Leithead, W.: Detection and compensation of anomalous conditions in a wind turbine, Energy, 124, 74–86, 2017.
IEA: Wind Electricity, International Energy Agency, Paris, https://www.iea.org/reports/wind-electricity (last access: 3 August 2023), 2022.
IEC: IEC 61400-1 Edition 4.0 2019-02 Wind turbines – Part 1: Design requirements, International Electrotechnical Commission, Geneva, Switzerland, ISBN 978-2-8322-6253-5, 168 pp., 2019.
Irwin, J. S.: A theoretical variation of the wind profile power-law exponent as a function of surface roughness and stability, Atmos. Environ., 13, 191–194, 1979.
Kallistratova, M., Kouznetsov, R. D., Kuznetsov, D. D., Kuznetsova, I. N., Nakhaev, M., and Chirokova, G.: Summertime low-level jet characteristics measured by sodars over rural and urban areas, Meteorol. Z., 18, 289–295, 2009.
Kalverla, P. C., Holtslag, A. A. M., Ronda, R. J., and Steeneveld, G. J.: Quality of wind characteristics in recent wind atlases over the North Sea, Q. J. Roy. Meteor. Soc., 146, 1498–1515, https://doi.org/10.1002/qj.3748, 2020.
Kariniotakis, G., Pinson, P., Siebert, N., Giebel, G., and Barthelmie, R.: The state of the art in short term prediction of wind power-from an offshore perspective, SeaTech Week-Ocean Energy Conference ADEME-IFREMER, Brest, France, October 2004 13, 2004.
Kelberlau, F. and Mann, J.: Quantification of motion-induced measurement error on floating lidar systems, Atmos. Meas. Tech., 15, 5323–5341, https://doi.org/10.5194/amt-15-5323-2022, 2022.
Kirkegaard, J. K., Rudolph, D. P., Nyborg, S., Solman, H., Gill, E., Cronin, T., and Hallisey, M.: Tackling grand challenges in wind energy through a socio-technical perspective, Nature Energy, 8, 655–664, https://doi.org/10.1038/s41560-41023-01266-z, 2023.
Kiviluoma, J., Holttinen, H., Weir, D., Scharff, R., Söder, L., Menemenlis, N., Cutululis, N. A., Danti Lopez, I., Lannoye, E., and Estanqueiro, A.: Variability in large-scale wind power generation, Wind Energy, 19, 1649–1665, 2016.
Knoop, S., Ramakrishnan, P., and Wijnant, I.: Dutch Offshore Wind Atlas Validation against Cabauw Meteomast Wind Measurements, Energies, 13, 6558, https://doi.org/10.3390/en13246558, 2020.
Kumer, V.-M., Reuder, J., Dorninger, M., Zauner, R., and Grubišić, V.: Turbulent kinetic energy estimates from profiling wind LiDAR measurements and their potential for wind energy applications, Renew. Energ., 99, 898–910, 2016.
Lazard: Lazard's Levelized Cost of Energy Analysis–Version 16.0, https://www.lazard.com/research-insights/levelized-cost-of-energyplus/ (last access: 3 August 2023), Zurich, Switzerland, 2023.
McCabe, E. J. and Freedman, J. M.: Development of an Objective Methodology for Identifying the Sea-Breeze Circulation and Associated Low-Level Jet in the New York Bight, Weather Forecast., 38, 571–589, 2023.
Meyer, P. J. and Gottschall, J.: How do NEWA and ERA5 compare for assessing offshore wind resources and wind farm siting conditions?, J. Phys. Conf. Ser., 2022, 012009,.
Musial, W., Heimiller, D., Beiter, P., Scott, G., and Draxl, C.: 2016 Offshore Wind Energy Resource Assessment for the United States, Technical Report NREL/TP-5000-66599, p. 88, https://www.nrel.gov/docs/fy16osti/66599.pdf (last access: 16 July 2020), 2016.
Optis, M., Bodini, N., Debnath, M., and Doubrawa, P.: New methods to improve the vertical extrapolation of near-surface offshore wind speeds, Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, 2021.
Pichault, M., Vincent, C., Skidmore, G., and Monty, J.: Characterisation of intra-hourly wind power ramps at the wind farm scale and associated processes, Wind Energ. Sci., 6, 131–147, https://doi.org/10.5194/wes-6-131-2021, 2021.
Pinson, P., Chevallier, C., and Kariniotakis, G. N.: Trading wind generation from short-term probabilistic forecasts of wind power, IEEE T. Power Syst., 22, 1148–1156, 2007.
Potisomporn, P. and Vogel, C. R.: Spatial and temporal variability characteristics of offshore wind energy in the United Kingdom, Wind Energy, 25, 537–552, 2022.
Pryor, S. C. and Barthelmie, R. J.: Comparison of potential power production at on- and off- shore sites, Wind Energy, 2001, 173–181, 2002.
Pryor, S. C., Nielsen, M., Barthelmie, R. J., and Mann, J.: Can satellite sampling of offshore wind speeds realistically represent wind speed distributions? Part II: Quantifying uncertainties associated with sampling strategy and distribution fitting methods, J. Appl. Meteorol., 43, 739–750, https://doi.org/10.1175/2096.1, 2004.
Pryor, S. C., Conrick, R., Miller, C., Tytell, J., and Barthelmie, R. J.: Intense and extreme wind speeds observed by anemometer and seismic networks: An Eastern US case study, J. Appl. Meteorol. Clim., 53, 2417–2429, https://doi.org/10.1175/jamc-d-14-0091.1, 2014.
Pryor, S. C., Letson, F. W., and Barthelmie, R. J.: Variability in wind energy generation across the contiguous USA, J. Appl. Meteorol. Clim., 59, 2021–2039, https://doi.org/10.1175/JAMC-D-20-0162.1, 2020a.
Pryor, S. C., Barthelmie, R. J., Bukovsky, M. S., Leung, L. R., and Sakaguchi, K.: Climate change impacts on wind power generation, Nature Reviews Earth & Environment, 1, 627–643, 2020b.
Pryor, S. C., Barthelmie, R. J., and Shepherd, T. J.: Wind power production from very large offshore wind farms, Joule, 5, 2663–2686, https://doi.org/10.1016/j.joule.2021.09.002, 2021.
Pryor, S. C., Coburn, J. J., Barthelmie, R. J., and Shepherd, T. J.: Projecting Future Energy Production from Operating Wind Farms in North America: Part 1: Dynamical Downscaling, J. Appl. Meteorol. Clim., 62, 63–80, https://doi.org/10.1175/jamc-d-22-0044.1, 2023.
Sharmar, V. and Markina, M.: Validation of global wind wave hindcasts using ERA5, MERRA2, ERA-Interim and CFSRv2 reanalyzes, IOP C. Ser. Earth Env., 606, 012056, https://doi.org/10.1088/1755-1315/606/1/012056, 2020.
Shrestha, B., Brotzge, J. A., and Wang, J.: Evaluation of the New York State Mesonet Profiler Network data, Atmos. Meas. Tech., 15, 6011–6033, https://doi.org/10.5194/amt-15-6011-2022, 2022.
Simão, H., Powell, W., Archer, C., and Kempton, W.: The challenge of integrating offshore wind power in the US electric grid. Part II: Simulation of electricity market operations, Renew. Energ., 103, 418–431, 2017.
Smith, D. A., Harris, M., Coffey, A. S., Mikkelsen, T., Jørgensen, H. E., Mann, J., and Danielian, R.: Wind lidar evaluation at the Danish wind test site in Høvsøre, Wind Energy, 9, 87–93, 2006.
Solbrekke, I. M., Kvamstø, N. G., and Sorteberg, A.: Mitigation of offshore wind power intermittency by interconnection of production sites, Wind Energ. Sci., 5, 1663–1678, https://doi.org/10.5194/wes-5-1663-2020, 2020.
Staffell, I. and Pfenninger, S.: The increasing impact of weather on electricity supply and demand, Energy, 145, 65–78, 2018.
Stehly, T. and Duffy, P.: 2020 Cost of Wind Energy Review, NREL/TP-5000-81209, National Renewable Energy Laboratory, Golden, CO, https://www.nrel.gov/docs/fy22osti/81209.pdf (last access: 3 August 2023), 77, 2022.
Stensrud, D. J.: Importance of low-level jets to climate: A review, J. Climate, 9, 1698–1711, 1996.
Stone, K. M., Leiter, S. M., Kenney, R. D., Wikgren, B. C., Thompson, J. L., Taylor, J. K., and Kraus, S. D.: Distribution and abundance of cetaceans in a wind energy development area offshore of Massachusetts and Rhode Island, J. Coast. Conserv., 21, 527–543, https://doi.org/10.1007/s11852-017-0526-4, 2017.
Stull, R. B.: Practical Meteorology: An Algebra-based Survey of Atmospheric Science, Univ. of British Columbia, ISBN: 978-0-88865-283-6, 940 pp., 2017.
St. Martin, C. M., Lundquist, J. K., and Handschy, M. A.: Variability of interconnected wind plants: correlation length and its dependence on variability time scale, Environ. Res. Lett., 10, 044004, https://doi.org/10.1088/1748-9326/10/4/044004, 2015.
Troen, I. and Lundtang Petersen, E.: European Wind Atlas, Risø National Laboratory, Roskilde, 656 pp., 1989.
U.S. Department of the Interior: Clean Energy Future, https://www.doi.gov/priorities/clean-energy-future (last access: 3 August 2023), 2021.
U.S. White House: FACT SHEET: President Biden to Catalyze Global Climate Action through the Major Economies Forum on Energy and Climate, https://www.
whitehouse.gov/briefing-room/statements-releases/2023/04/20/
fact-sheet-president-biden-to-catalyze-global-climate-action-through-the-major-economies-forum-on-energy-and-climate/ (last access: 3 August 2023), 2023.
Wilks, D. S.: Statistical methods in the atmospheric sciences, International geophysics series, Academic Press, Oxford, UK, ISBN: 9780123850225, 2011.
Wiser, R. H., Bolinger, M., Hoen, B., Millstein, D., Rand, J., Barbose, G. L., Darghouth, N. R., Gorman, W., Jeong, S., and Mills, A. D.: Land-Based Wind Market Report: 2021 Edition, Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States), https://www.osti.gov/biblio/1818277 (last access: 3 August 2023), 88, 2021.
Zeng, J. and Qiao, W.: Support vector machine-based short-term wind power forecasting, 2011 IEEE/PES power systems conference and exposition, 20–23 March 2011, Phoenix, AZ, USA, 8, https://doi.org/10.1109/PSCE.2011.5772573, 2011.
Download
The requested paper has a corresponding corrigendum published. Please read the corrigendum first before downloading the article.
- Article
(4188 KB) - Full-text XML
- Corrigendum
-
Supplement
(482 KB) - BibTeX
- EndNote
Short summary
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore locations, we quantify the advantages of deploying wind turbines offshore in terms of the amount of electrical power produced and the higher reliability and predictability of the electrical power.
Using lidar-derived wind speed measurements at approx. 150 m height at onshore and offshore...
Altmetrics
Final-revised paper
Preprint