Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2407-2022
© Author(s) 2022. 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-7-2407-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
09 Dec 2022
Research article |
| 09 Dec 2022
| 09 Dec 2022
The Jensen wind farm parameterization
Yulong Ma
Center for Research in Wind (CReW), University of Delaware, Newark, DE 19716, USA
Center for Research in Wind (CReW), University of Delaware, Newark, DE 19716, USA
Ahmadreza Vasel-Be-Hagh
Department of Mechanical Engineering, Tennessee Technological University, Cookeville, TN 38505, USA
Related authors
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Cristina Lozej Archer
Wind Energ. Sci., 10, 1433–1438, https://doi.org/10.5194/wes-10-1433-2025, https://doi.org/10.5194/wes-10-1433-2025, 2025
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Two approximate analytical expressions are derived, one for the variance of wind speed and the other for turbulence intensity, based on one simple assumption: that the turbulent fluctuations in wind are small with respect to the mean. The formulations perform well when applied to the observations from the American WAKE experimeNt (AWAKEN) field campaign conducted in 2023.
Ali Khanjari, Asim Feroz, and Cristina L. Archer
Wind Energ. Sci., 10, 887–905, https://doi.org/10.5194/wes-10-887-2025, https://doi.org/10.5194/wes-10-887-2025, 2025
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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. Ultimately this equation can be used to better understand how wind turbines affect the atmosphere.
Maryam Golbazi and Cristina Archer
Atmos. Chem. Phys., 23, 15057–15075, https://doi.org/10.5194/acp-23-15057-2023, https://doi.org/10.5194/acp-23-15057-2023, 2023
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We use scientific models to study the impact of ship emissions on air quality along the US East Coast. We find an increase in three major pollutants (PM2.5, NO2, and SO2) in coastal regions. However, we detect a reduction in ozone (O3) levels in major coastal cities. This reduction is linked to the significant emissions of nitrogen oxides (NOx) from ships, which scavenged O3, especially in highly polluted urban areas experiencing an NOx-limited regime.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
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Swell impacts on an offshore wind farm in stable boundary layer: wake flow and energy budget analysis
An analytical formulation for turbulence kinetic energy added by wind turbines based on large-eddy simulation
Tall wind profile validation of ERA5, NORA3, and NEWA datasets using lidar observations
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Machine-learning-based estimate of the wind speed over complex terrain using the long short-term memory (LSTM) recurrent neural network
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Daphne Quint, Julie K. Lundquist, Nicola Bodini, and David Rosencrans
Wind Energ. Sci., 10, 1269–1301, https://doi.org/10.5194/wes-10-1269-2025, https://doi.org/10.5194/wes-10-1269-2025, 2025
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Offshore wind farms along the US East Coast can have limited effects on local weather. To study these effects, we include wind farms near Massachusetts and Rhode Island, and we test different amounts of turbulence in our model. We analyze changes in wind, temperature, and turbulence. Simulated effects on surface temperature and turbulence change depending on how much turbulence is added to the model. The extent of the wind farm wake depends on how deep the atmospheric boundary layer is.
Robert S. Arthur, Alex Rybchuk, Timothy W. Juliano, Gabriel Rios, Sonia Wharton, Julie K. Lundquist, and Jerome D. Fast
Wind Energ. Sci., 10, 1187–1209, https://doi.org/10.5194/wes-10-1187-2025, https://doi.org/10.5194/wes-10-1187-2025, 2025
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This paper evaluates a new model configuration for wind energy forecasting in complex terrain. We compare model results to observations in the Altamont Pass (California, USA), where wind channeling through a mountain gap leads to increased energy production. We demonstrate that the new model configuration performs similarly to a more established approach, with some evidence of improved wind speed predictions, and provide guidance for future model testing.
Xu Ning and Mostafa Bakhoday-Paskyabi
Wind Energ. Sci., 10, 1101–1122, https://doi.org/10.5194/wes-10-1101-2025, https://doi.org/10.5194/wes-10-1101-2025, 2025
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Waves interact with the overlying wind field by modifying the stresses at the atmosphere–ocean interface. We develop and employ a parameterization method of wave-induced stresses in the numerical simulation of an offshore wind farm in a stable atmospheric boundary layer. This work demonstrates how swells change the kinetic energy transport and induce wind veer and wake deflection, leading to significant variations in the power output of wind turbines at different positions of the wind farm.
Ali Khanjari, Asim Feroz, and Cristina L. Archer
Wind Energ. Sci., 10, 887–905, https://doi.org/10.5194/wes-10-887-2025, https://doi.org/10.5194/wes-10-887-2025, 2025
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. Ultimately this equation can be used to better understand how wind turbines affect the atmosphere.
Etienne Cheynet, Jan Markus Diezel, Hilde Haakenstad, Øyvind Breivik, Alfredo Peña, and Joachim Reuder
Wind Energ. Sci., 10, 733–754, https://doi.org/10.5194/wes-10-733-2025, https://doi.org/10.5194/wes-10-733-2025, 2025
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This study analyses wind speed data at heights up to 500 m to support the design of future large offshore wind turbines and airborne wind energy systems. We compared three wind models (ERA5, NORA3, and NEWA) with lidar measurements at five sites using four performance metrics. ERA5 and NORA3 performed equally well offshore, with NORA3 typically outperforming the other two models onshore. More generally, the optimal choice of model depends on site, altitude, and evaluation criteria.
Mark Kelly
Wind Energ. Sci., 10, 535–558, https://doi.org/10.5194/wes-10-535-2025, https://doi.org/10.5194/wes-10-535-2025, 2025
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Industrial standards for wind turbine design are based on 10 min statistics of wind speed at turbine hub height, treating fluctuations as turbulence. But recent work shows that the effect of strong transients is described by flow acceleration. We devise a method to measure the acceleration that turbines encounter; the extremes offshore defy 10 min averages due to various phenomena beyond turbulence. These findings are translated into a recipe supporting statistical design.
Caroline Braud, Pascal Keravec, Ingrid Neunaber, Sandrine Aubrun, Jean-Luc Attie, Pierre Durand, Philippe Ricaud, Jean-François Georgis, Emmanuel Leclerc, Lise Mourre, and Claire Taymans
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2025-12, https://doi.org/10.5194/wes-2025-12, 2025
Revised manuscript accepted for WES
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A meteorological 3 year dataset from an operational wind farm of six 2 MW turbines, has been made available. This includes a meteorological mast equipped with sonic anemometers at four different heights and radiometer measurements for atmospheric stability analysis. Simultaneously, supervisory control and data acquisition (SCADA) and the scanned geometry of the turbine blades are provided. This database has been made accessible to the research community (https://awit.aeris-data.fr).
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
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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
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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.
Edward Hart
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-187, https://doi.org/10.5194/wes-2024-187, 2025
Revised manuscript accepted for WES
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A parametric model for the wind direction rose is presented, with testing on real offshore wind farm data indicating the model is indeed representative. The presented model provides opportunities for standardisation and enables more systematic analyses of wind direction impacts and sensitivities.
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
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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
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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
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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
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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
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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.
Aliza Abraham, Matteo Puccioni, Arianna Jordan, Emina Maric, Nicola Bodini, Nicholas Hamilton, Stefano Letizia, Petra M. Klein, Elizabeth Smith, Sonia Wharton, Jonathan Gero, Jamey D. Jacob, Raghavendra Krishnamurthy, Rob K. Newsom, Mikhail Pekour, and Patrick Moriarty
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-148, https://doi.org/10.5194/wes-2024-148, 2024
Revised manuscript accepted for WES
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This study is the first to use real-world atmospheric measurements to show that large wind plants can increase the height of the planetary boundary layer, the part of the atmosphere near the surface where life takes place. The planetary boundary layer height governs processes like pollutant transport and cloud formation, and is a key parameter for modeling the atmosphere. The results of this study provide important insights into interactions between wind plants and their local environment.
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
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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.
Lauren A. Mudd and Peter J. Vickery
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-123, https://doi.org/10.5194/wes-2024-123, 2024
Revised manuscript accepted for WES
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This research presents an assessment of hurricane risk to offshore wind turbine systems in the Gulf of Mexico. Hurricanes that frequent this area can potentially exceed design limits prescribed by the International Electrotechnical Commission (IEC) wind design standards. Translations between the well-established Saffir-Simpson scale and the IEC design classes were developed to convert to communicate of hurricane severity in terms of design load conditions familiar to wind turbine designers.
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
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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
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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.
Lindsay M. Sheridan, Jiali Wang, Caroline Draxl, Nicola Bodini, Caleb Phillips, Dmitry Duplyakin, Heidi Tinnesand, Raj K. Rai, Julia E. Flaherty, Larry K. Berg, Chunyong Jung, and Ethan Young
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-115, https://doi.org/10.5194/wes-2024-115, 2024
Revised manuscript accepted for WES
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Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and inter-annual trends in the wind resource to support customers interested in small and midsize wind energy.
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
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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
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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
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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.
Maxime Thiébaut, Frédéric Delbos, Cristina Benzo, Loïc Mahe, and Florent Guinot
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-93, https://doi.org/10.5194/wes-2024-93, 2024
Revised manuscript accepted for WES
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This paper explores potential technical enhancements to wind lidar profilers for improved turbulence measurement. The study separately tests the effects of increased sampling rate and reduced probe length against a commercial lidar of the same type. Various turbulence metrics were quantified to evaluate the impact of these technical modifications. The results indicate that increasing the sampling rate is the most valuable enhancement for turbulence measurement.
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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Rebecca Foody, Jacob Coburn, Jeanie A. Aird, Rebecca J. Barthelmie, and Sara C. Pryor
Wind Energ. Sci., 9, 263–280, https://doi.org/10.5194/wes-9-263-2024, https://doi.org/10.5194/wes-9-263-2024, 2024
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
Cited articles
Abkar, M. and Porté-Agel, F.: A new wind-farm parameterization for
large-scale atmospheric models, J. Renew. Sustain. Energ., 7, 013121, https://doi.org/10.1063/1.4907600, 2015. a, b, c
Archer, C. and Vasel-Be-Hagh, A.: Wake steering via yaw control in
multi-turbine wind farms: Recommendations based on large-eddy simulation,
Sustain. Energ. Technol. Assess., 33, 34–43, https://doi.org/10.1016/j.seta.2019.03.002, 2019. a
Archer, C. L., Mirzaeisefat, S., and Lee, S.: Quantifying the sensitivity of
wind farm performance to array layout options using large-eddy simulation,
Geophys. Res. Lett., 40, 4963–4970, https://doi.org/10.1002/grl.50911, 2013. a
Archer, C. L., Colle, B. A., Veron, D. L., Veron, F., and Sienkiewicz, M. J.:
On the predominance of unstable atmospheric conditions in the marine
boundary layer offshore of the U.S. northeastern coast, J. Geophys. Res., 121, 8869–8885, https://doi.org/10.1002/2016JD024896, 2016. a
Barthelmie, R. J., Hansen, K., Frandsen, S. T., Rathmann, O., Schepers, J. G., Schlez, W., Phillips, J., Rados, K., Zervos, A., Politis, E., and
Chaviaropoulos, P.: Modelling and measuring flow and wind turbine wakes in
large wind farms offshore, Wind Energy, 12, 431–444, https://doi.org/10.1002/we.348,
2009. a
Byrkjedal, Ø., Bredesen, R., Keck, R.-E., Sondell, N., and Berge, E.:
Properties of a wind farm wake as simulated by a numerical weather prediction
model for the Smøla wind farm, in: European Wind Energy Association
Conference and Exhibition 2014, EWEA 2014, 10–13 March 2014, Barcelona, Spain, 2014. a
Charnock, H.: Wind stress on a water surface, Q. J. Roy. Meteorol. Soc., 81, 639–640, https://doi.org/10.1002/qj.49708135027, 1955. a
Choukulkar, A., Pichugina, Y., Clack, C. T. M., Calhoun, R., Banta, R., Brewer, A., and Hardesty, M.: A new formulation for rotor equivalent wind speed for wind resource assessment and wind power forecasting, Wind Energy, 19, 1439–1452, https://doi.org/10.1002/we.1929, 2016. a
Dahlberg, J.-Å.: Assessment of the Lillgrund windfarm, Tech. Rep. 6_1 LG
Pilot Report, Vattenfall, Stockholm, Sweden, 29 pp., https://docplayer.net/35369081-Assessment-of-the-lillgrund-windfarm.html (last access: 5 December 2022), 2009. a
Eriksson, O., Lindvall, J., Breton, S.-P., and Ivanell, S.: Wake downstream of the Lillgrund wind farm - A comparison between LES using the actuator disc
method and a wind farm parametrization in WRF, J. Phys.: Conf. Ser., 625, 012028, https://doi.org/10.1088/1742-6596/625/1/012028, 2015. a, b, c
Eriksson, O., Baltscheffsky, M., Breton, S.-P., Söderberg, S., and Ivanell, S.: The Long distance wake behind Horns Rev I studied using large eddy simulations and a wind turbine parameterization in WRF, J. Phys.: Conf. Ser., 854, 012012, https://doi.org/10.1088/1742-6596/854/1/012012, 2017. a
Fitch, A. C.: Climate impacts of large-scale wind farms as parameterized in a
global climate model, J. Climate, 28, 6160–6180, https://doi.org/10.1175/JCLI-D-14-00245.1, 2015. a
Fitch, A. C., Olson, J. B., Lundquist, J. K., Dudhia, J., Gupta, A. K.,
Michalakes, J., and Barstad, I.: Local and Mesoscale Impacts of Wind Farms as
Parameterized in a Mesoscale NWP Model, Mon. Weather Rev., 140, 3017–3038, https://doi.org/10.1175/MWR-D-11-00352.1, 2012. a, b
Fitch, A. C., Lundquist, J. K., and Olson, J. B.: Mesoscale Influences of Wind Farms throughout a Diurnal Cycle, Mon. Weather Rev., 141, 2173–2198,
https://doi.org/10.1175/MWR-D-12-00185.1, 2013a. a
Fitch, A. C., Olson, J. B., and Lundquist, J. K.: Parameterization of wind
farms in climate models, J. Climate, 26, 6439–6458,
https://doi.org/10.1175/JCLI-D-12-00376.1, 2013b. a
Fleming, P., King, J., Dykes, K., Simley, E., Roadman, J., Scholbrock, A.,
Murphy, P., Lundquist, J., Moriarty, P., Fleming, K., van Dam, J., Bay, C.,
Mudafort, R., Lopez, H., Skopek, J., Scott, M., Ryan, B., Guernsey, C., and
Brake, D.: Initial results from a field campaign of wake steering applied at
a commercial wind farm – Part 1, Wind Energ. Sci., 4, 273–285,
https://doi.org/10.5194/wes-4-273-2019, 2019. a
Frandsen, S., Barthelmie, R., Pryor, S., Rathmann, O., Larsen, S., and
Hojstrup, J.: Analytical modelling of wind speed deficit in large offshore
wind farms, Wind Energy, 9, 39–53, https://doi.org/10.1002/we.189, 2006. a
Gaumond, M., Réthoré, P. E., Ott, S., Peña, A., Bechmann, A., and
Hansen, K. S.: Evaluation of the wind direction uncertainty and its impact on
wake modeling at the Horns Rev offshore wind farm, Wind Energy, 17, 1169–1178, https://doi.org/10.1002/we.1625, 2014. a, b, c
Ge, M., Wu, Y., Liu, Y., and Li, Q.: A two-dimensional model based on the
expansion of physical wake boundary for wind-turbine wakes, Appl. Energy, 233, 975–984, 2019. a
Ghaisas, N., Archer, C., Xie, S., Wu, S., and Maguire, E.: Evaluation of
layout and atmospheric stability effects in wind farms using large-eddy
simulation, Wind Energy, 20, 1227–1240, https://doi.org/10.1002/we.2091, 2017. a
Ghaisas, N. S. and Archer, C. L.: Geometry-Based Models for Studying the
Effects of Wind Farm Layout, J. Atmos. Ocean. Tech., 33, 481–501, https://doi.org/10.1175/JTECH-D-14-00199.1, 2016. a, b
Göçmen, T., van der Laan, P., Réthoré, P. E., Diaz, A. P.,
Larsen, G. C., and Ott, S.: Wind turbine wake models developed at the
Technical University of Denmark: A review, Renew. Sustain. Energ. Rev., 60, 752–769, https://doi.org/10.1016/j.rser.2016.01.113, 2016. a, b
Iungo, G. V., Santhanagopalan, V., Ciri, U., Viola, F., Zhan, L., Rotea, M. A., and Leonardi, S.: Parabolic RANS solver for low-computational-cost
simulations of wind turbine wakes, Wind Energy, 21, 184–197, https://doi.org/10.1002/we.2154, 2018. a
Jensen, N. O.: A note on wind generator interaction, Tech Rep Risø-M-2411,
Risø National Laboratory, Denmark, 16 pp.,
https://orbit.dtu.dk/files/55857682/ris_m_2411.pdf (last access: 5 December 2022), 1983. a
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J.,
Montávez, J. P., and García-Bustamante, E.: A Revised Scheme for the
WRF Surface Layer Formulation, Mon. Weather Rev., 140, 898–918,
https://doi.org/10.1175/MWR-D-11-00056.1, 2012. a
Jiménez, P. A., Navarro, J., Palomares, A. M., and Dudhia, J.: Mesoscale
modeling of offshore wind turbine wakes at the wind farm resolving scale: A
composite-based analysis with the Weather Research and Forecasting model over Horns Rev, Wind Energy, 18, 559–566, https://doi.org/10.1002/we.1708, 2015. a, b, c
Johlas, H. M., Martínez-Tossas, L., Lackner, M., Schmidt, D., and
Churchfield, M.: Large eddy simulations of offshore wind turbine wakes for
two floating platform types, J. Phys.: Conf. Ser., 1452, 012034, https://doi.org/10.1088/1742-6596/1452/1/012034, 2020. a
Katic, I., Højstrup, J., and Jensen, N. O.: A simple model for cluster
efficiency, in: European Wind Energy Association Conference and Exhibition,
7–9 October 1986, Rome, Italy, 407–410,
https://orbit.dtu.dk/files/106427419/A_Simple_Model_for_Cluster_Efficiency_EWEC_86_.pdf
(last access: 5 December 2022), 1986. a, b, c
Keane, A., Aguirre, P., Ferchland, H., Clive, P., and Gallacher, D.: An
analytical model for a full wind turbine wake, J. Phys.: Conf. Ser., 753, 032039, https://doi.org/10.1088/1742-6596/753/3/032039, 2016. a
Kirchner-Bossi, N. and Porté-Agel, F.: Realistic Wind Farm Layout
Optimization through Genetic Algorithms Using a Gaussian Wake Model,
Energies, 11, 3268, https://doi.org/10.3390/en11123268, 2018. a
Larsen, G. C.: A simple wake calculation procedure, Tech Rep Risø-M-2760,
Risø National Laboratory, Denmark, 53 pp., https://backend.orbit.dtu.dk/ws/portalfiles/portal/55567186/ris_m_2760.pdf
(last access: 5 December 2022), 1988. a
Lee, J. C. and Lundquist, J. K.: Evaluation of the wind farm parameterization
in the Weather Research and Forecasting model (version 3.8.1) with
meteorological and turbine power data, Geosci. Model Dev., 10, 4229–4244, https://doi.org/10.5194/gmd-10-4229-2017, 2017. a
Lissaman, P. B. S.: Energy effectiveness of arbitrary arrays of wind turbines, J. Energy, 3, 323–328, https://doi.org/10.2514/3.62441, 1979. a
Lu, H. and Porté-Agel, F.: Large-eddy simulation of a very large wind
farm in a stable atmospheric boundary layer, Phys. Fluids, 23, 065101, https://doi.org/10.1063/1.3589857, 2011. a
Lundquist, J. K., DuVivier, K. K., Kaffine, D., and Tomaszewski, J. M.: Costs
and consequences of wind turbine wake effects arising from uncoordinated wind
energy development, Nat. Energy, 4, 26–34, https://doi.org/10.1038/s41560-018-0281-2, 2019. a
Ma, Y., Archer, C. L., and Vasel-Be-Hagh, A.: Comparison of individual versus
ensemble wind farm parameterizations inclusive of sub-grid wakes for the
WRF model, Wind Energy, 25, 1573–1595, https://doi.org/10.1002/we.2758, 2022. a
Machefaux, E., Larsen, G. C., and Leon, J. P. M.: Engineering models for
merging wakes in wind farm optimization applications, J. Phys.: Conf. Ser., 625, 012037, https://doi.org/10.1088/1742-6596/625/1/012037, 2015. a
Nakanishi, M. and Niino, H.: Development of an improved turbulence closure
model for the atmospheric boundary layer, J. Meteorol. Soc. Jpn. 87, 895–912, https://doi.org/10.2151/jmsj.87.895, 2009. a, b
Nouri, R., Vasel-Be-Hagh, A., and Archer, C. L.: The Coriolis force and the
direction of rotation of the blades significantly affect the wake of wind
turbines, Appl. Energy, 277, 115511, https://doi.org/10.1016/j.apenergy.2020.115511, 2020. a
Pan, Y., Yan, C., and Archer, C. L.: Precipitation reduction during Hurricane
Harvey with simulated offshore wind farms, Environ. Res. Lett., 13, 084007, https://doi.org/10.1088/1748-9326/aad245, 2018. a
Peña, A., Réthoré, P.-E., and Rathmann, O.: Modeling large offshore wind farms under different atmospheric stability regimes with the Park wake model, Renew. Energy, 70, 164–171, https://doi.org/10.1016/j.renene.2014.02.019, 2014. a
Peña, A., Hansen, K. S., Ott, S., and van der Laan, M. P.: On wake
modeling, wind-farm gradients, and AEP predictions at the Anholt wind
farm, Wind Energ. Sci., 3, 191–202, https://doi.org/10.5194/wes-3-191-2018, 2018. a
Porté-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-turbine and
wind-farm flows: A review, Bound.-Lay. Meteorol., 174, 1–59,
https://doi.org/10.1007/s10546-019-00473-0, 2020. a
Pryor, S. C., Barthelmie, R. J., and Shepherd, T. J.: The Influence of
Real-World Wind Turbine Deployments on Local to Mesoscale Climate, J. Geophys. Res.-Atmos., 123, 5804–5826, https://doi.org/10.1029/2017JD028114, 2018. a
Pryor, S. C., Shepherd, T. J., Barthelmie, R. J., Hahmann, A. N., and Volker,
P.: Wind Farm Wakes Simulated Using WRF, J. Phys.: Conf. Ser., 1256, 012025, https://doi.org/10.1088/1742-6596/1256/1/012025, 2019. a
Rai, R., Berg, L., Kosović, B., Haupt, S., Mirocha, J., Ennis, B., and Draxl, C.: Evaluation of the impact of horizontal grid spacing in terra incognita on coupled mesoscale–microscale simulations using the WRF framework, Mon. Weather Rev., 147, 1007–1027, https://doi.org/10.1175/MWR-D-18-0282.1, 2019. a
Ritter, M., Pieralli, S., and Odening, M.: Neighborhood effects in wind farm
performance: A regression approach, Energies, 10, 365, https://doi.org/10.3390/en10030365, 2017. a
Rivas, R. A., Clausen, J., Hansen, K. S., and Jensen, L. E.: Solving the
Turbine Positioning Problem for Large Offshore Wind Farms by Simulated
Annealing, Wind Eng., 33, 287–297, https://doi.org/10.1260/0309-524x.33.3.287, 2009. a
Shepherd, T. J., Barthelmie, R. J., and Pryor, S. C.: Sensitivity of Wind
Turbine Array Downstream Effects to the Parameterization Used in WRF, J. Appl. Meteorol. Clim., 59, 333–361, https://doi.org/10.1175/JAMC-D-19-0135.1, 2020. a
Simisiroglou, N., Polatidis, H., and Ivanell, S.: Wind farm power production
assessment: Introduction of a new actuator disc method and comparison with
existing models in the context of a case study, Appl. Sci., 9, 431, https://doi.org/10.3390/app9030431, 2019. a
Simley, E., Fleming, P., and King, J.: Design and analysis of a wake steering
controller with wind direction variability, Wind Energ. Sci., 5, 451–468,
https://doi.org/10.5194/wes-5-451-2020, 2020. a
Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda,
M. G., Huang, X.-Y., Wang, W., and Powers, J. G.: A description of the
Advanced Research WRF version 3, Tech. Rep. NCAR/TN-475+STR, National Center for Atmospheric Research, Boulder, Colorado, 125 pp., https://doi.org/10.5065/D68S4MVH, 2008. a
Skamarock, W. C., Klemp, J. B., Duda, M. G., Fowler, L. D., Park, S. H., and
Ringler, T. D.: A multiscale nonhydrostatic atmospheric model using
centroidal Voronoi tesselations and C-grid staggering, Mon. Weather Rev., 140, 3090–3105, https://doi.org/10.1175/MWR-D-11-00215.1, 2012. a
Staid, A., VerHulst, C., and Guikema, S. D.: A comparison of methods for
assessing power output in non-uniform onshore wind farms, Wind Energy, 21,
42–52, https://doi.org/10.1002/we.2143, 2018. a
Stevens, R. and Meneveau, C.: Large eddy simulation study of extended wind
farms with large inter turbine spacing, J. Phys.: Conf. Ser., 1618, 062011, https://doi.org/10.1088/1742-6596/1618/6/062011, 2020. a
Stevens, R. J. A. M., Dennice, F. G., and Meneveau, C.: Generalized coupled
wake boundary layer model: applications and comparisons with field and LES
data for two wind farms, Wind Energy, 19, 2023–2040, 2016. a
St. Pé, A., Sperling, M., Brodie, J. F., and Delgado, R.: Classifying
rotor-layer wind to reduce offshore available power uncertainty, Wind Energy,
21, 461–473, https://doi.org/10.1002/we.2159, 2018. a
Tian, L., Zhu, W., Shen, W., Song, Y., and Zhao, N.: Prediction of multi-wake
problems using an improved Jensen wake model, Renew. Energy, 102, 457–469, 2017. a
van der Laan, M. P., Sørensen, N. N., Réthoré, P.-E., Mann, J.,
Kelly, M. C., Troldborg, N., Hansen, K. S., and Murcia, J. P.: The
k–ϵ–fP model applied to wind farms, Wind Energy, 18, 2065–2084, https://doi.org/10.1002/we.1804, 2015. a
van der Laan, M. P., Peña, A., Volker, P., Hansen, K. S., Sørensen,
N. N., Ott, S., and Hasager, C. B.: Challenges in simulating coastal effects
on an offshore wind farm, J. Phys.: Con. Ser., 854, 012046, https://doi.org/10.1088/1742-6596/854/1/012046, 2017. a
Vasel-Be-Hagh, A. and Archer, C. L.: Wind farm hub height optimization, Appl. Energy, 195, 905–921, https://doi.org/10.1016/j.apenergy.2017.03.089, 2017.
a
Volker, P. J., Badger, J., Hahmann, A. N., and Ott, S.: The explicit wake
parametrisation V1.0: A wind farm parametrisation in the mesoscale model
WRF, Geosci. Model Dev., 8, 3715–3731, https://doi.org/10.5194/gmd-8-3715-2015, 2015. a, b
Volker, P. J. H., Hahmann, A. N., Badger, J., and Jörgensen, H. E.:
Prospects for generating electricity by large onshore and offshore wind farms, Environ. Res. Lett., 12, 034022, https://doi.org/10.1088/1748-9326/aa5d86, 2017. a
Voutsinas, S., Rados, K., and Zervos, A.: On the analysis of wake effects in
wind parks, Wind Eng., 14, 204–219, 1990. a
Wu, S. and Archer, C. L.: Near-ground effects of wind turbines: Observations
and physical mechanisms, Mon. Weather Rev., 149, 879–898, https://doi.org/10.1175/MWR-D-20-0186.1, 2021. a
Wu, Y.-T. and Porté-Agel, F.: Modeling turbine wakes and power losses
within a wind farm using LES: An application to the Horns Rev offshore wind farm, Renew. Energy, 75, 945–955, https://doi.org/10.1016/j.renene.2014.06.019, 2015. a
Xia, G., Zhou, L., Minder, J. R., Fovell, R. G., and Jiménez, P. A.:
Simulating impacts of real-world wind farms on land surface temperature
using the WRF model: Physical mechanisms, Clim. Dynam., 53, 1723–1739,
https://doi.org/10.1007/s00382-019-04725-0, 2019. a
Xie, S. and Archer, C. L.: Self-similarity and turbulence characteristics of
wind turbine wakes via large-eddy simulation, Wind Energy, 18, 1815–1838,
https://doi.org/10.1002/we.1792, 2015. a, b, c
Xie, S. and Archer, C. L.: A Numerical Study of Wind-Turbine Wakes for Three
Atmospheric Stability Conditions, Bound.-Lay. Meteorol., 165, 87–112,
https://doi.org/10.1007/s10546-017-0259-9, 2017. a, b
Zong, H. and Porté-Agel, F.: A momentum-conserving wake superposition
method for wind farm power prediction, J. Fluid Mech., 889, A8, https://doi.org/10.1017/jfm.2020.77, 2020. a
Short summary
Wind turbine wakes are important because they reduce the power production of wind farms and may cause unintended impacts on the weather around wind farms. Weather prediction models, like WRF and MPAS, are often used to predict both power and impacts of wind farms, but they lack an accurate treatment of wind farm wakes. We developed the Jensen wind farm parameterization, based on the existing Jensen model of an idealized wake. The Jensen parameterization is accurate and computationally efficient.
Wind turbine wakes are important because they reduce the power production of wind farms and may...
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