Articles | Volume 9, issue 4
https://doi.org/10.5194/wes-9-919-2024
© 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-9-919-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Apr 2024
Research article |
| 15 Apr 2024
| 15 Apr 2024
Renewable Energy Complementarity (RECom) maps – a comprehensive visualisation tool to support spatial diversification
Til Kristian Vrana
CORRESPONDING AUTHOR
SINTEF Energi, Trondheim, Norway
Equinor, Trondheim, Norway
Harald G. Svendsen
SINTEF Energi, Trondheim, Norway
Related authors
Mark O'Malley, Hannele Holttinen, Nicolaos Cutululis, Til Kristian Vrana, Jennifer King, Vahan Gevorgian, Xiongfei Wang, Fatemeh Rajaei-Najafabadi, and Andreas Hadjileonidas
Wind Energ. Sci., 9, 2087–2112, https://doi.org/10.5194/wes-9-2087-2024, https://doi.org/10.5194/wes-9-2087-2024, 2024
Short summary
Short summary
The rising share of wind power poses challenges to cost-effective integration while ensuring reliability. Balancing the needs of the power system and contributions of wind power is crucial for long-term value. Research should prioritize wind power advantages over competitors, focussing on internal challenges. Collaboration with other technologies is essential for addressing the fundamental objectives of power systems – maintaining reliable supply–demand balance at the lowest cost.
Mark O'Malley, Hannele Holttinen, Nicolaos Cutululis, Til Kristian Vrana, Jennifer King, Vahan Gevorgian, Xiongfei Wang, Fatemeh Rajaei-Najafabadi, and Andreas Hadjileonidas
Wind Energ. Sci., 9, 2087–2112, https://doi.org/10.5194/wes-9-2087-2024, https://doi.org/10.5194/wes-9-2087-2024, 2024
Short summary
Short summary
The rising share of wind power poses challenges to cost-effective integration while ensuring reliability. Balancing the needs of the power system and contributions of wind power is crucial for long-term value. Research should prioritize wind power advantages over competitors, focussing on internal challenges. Collaboration with other technologies is essential for addressing the fundamental objectives of power systems – maintaining reliable supply–demand balance at the lowest cost.
Harald Georg Svendsen and John Olav Giæver Tande
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-45, https://doi.org/10.5194/wes-2024-45, 2024
Preprint withdrawn
Short summary
Short summary
Norway is planning a doubling of its power generation capacity with 30 GW of offshore wind over the next 25 years. To integrate this variable energy resource in the power system requires a good understanding of its characteristics. This paper demonstrates that wind power in the north is uncorrelated with wind in the south, that there is a large smoothing effect on power output, but nevertheless periods of low total wind power output for several days.
Related subject area
Thematic area: Wind and the atmosphere | Topic: Wind and turbulence
Performance of wind assessment datasets in United States coastal areas
Simulated meteorological impacts of offshore wind turbines and sensitivity to the amount of added turbulence kinetic energy
Evaluating mesoscale model predictions of diurnal speedup events in the Altamont Pass Wind Resource Area of California
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
Flow acceleration statistics: a new paradigm for wind-driven loads, towards probabilistic turbine design
Three-year database of atmospheric measurements combined with associated operating parameters from a wind farm of 2MW turbines and rotor geometry
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?
Brief communication: An elliptical parameterisation of the wind direction rose
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
Operational wind plants increase planetary boundary layer height: An observational study
Converging profile relationships for offshore wind speed and turbulence intensity
Gulf of Mexico Hurricane Risk Assessment for Offshore Wind Energy Sites
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
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
Enhancing turbulent fluctuation measurement with tailored wind lidar profilers
Impact of swell waves on atmospheric surface turbulence: wave–turbulence decomposition methods
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
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
Data assimilation of realistic boundary-layer flows for wind-turbine applications – An LES study
TOSCA – an open-source, finite-volume, large-eddy simulation (LES) environment for wind farm flows
Quantitative comparison of power production and power quality onshore and offshore: a case study from the eastern United States
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
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, Ethan Young, and Rao Kotamarthi
Wind Energ. Sci., 10, 1551–1574, https://doi.org/10.5194/wes-10-1551-2025, https://doi.org/10.5194/wes-10-1551-2025, 2025
Short summary
Short summary
Three recent wind resource datasets are assessed for their skills in representing annual average wind speeds and seasonal, diurnal, and interannual trends in the wind resource in coastal locations to support customers interested in small and midsize wind energy.
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
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.
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
Short summary
Short summary
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
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.
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.
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
Short summary
Short summary
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
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.
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.
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.
Linus Wrba, Antonia Englberger, Andreas Dörnbrack, Gerard Kilroy, and Norman Wildmann
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-12, https://doi.org/10.5194/wes-2024-12, 2024
Revised manuscript under review for WES
Short summary
Short summary
It is crucial to understand the loads and power production of wind turbines under different atmospheric situations (e.g. night and day changes). Computational simulations are a widely used tool to get more knowledge of the performance and the wake of wind turbines. In this study realistic velocity profiles of the atmosphere are used as input for simulations so that these simulations become more realistic. The generated realistic flow is used as inflow for wind-turbine simulations.
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.
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
Short summary
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.
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.
Cited articles
Antweiler, W. and Muesgens, F.: On the long-term merit order effect of renewable energies, Energ. Econ., 99, 105275, https://doi.org/10.1016/j.eneco.2021.105275, 2021. a
Arup: Future Offshore Wind Scenarios (FOWS) to 2050, https://www.futureoffshorewindscenarios.co.uk/ (last access: 20 February 2024), 2022. a
Dujardin, J., Schillinger, M., Kahl, A., Savelsberg, J., Schlecht, I., and Lordan-Perret, R.: Optimized market value of alpine solar photovoltaic installations, Renew. Energ., 186, 878–888, https://doi.org/10.1016/j.renene.2022.01.016, 2022. a
ENTSO-E: TYNDP 2022 Scenario Report, https://2022.entsos-tyndp-scenarios.eu/ (last access: 20 February 2024), 2022. a
European Commission: An EU Strategy to harness the potential of offshore renewable energy for a climate neutral future, EC Communication, https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:52020DC0741 (last access: 20 February 2024), 2020. a
European Marine Observation and Data Network (EMODnet): Human Activities/Wind Farms, https://emodnet.ec.europa.eu/ (last access: 20 February 2024), 2023. a
Hasager, C. B., Barthelmie, R. J., Christiansen, M. B., Nielsen, M., and Pryor, S. C.: Quantifying offshore wind resources from satellite wind maps: study area the North Sea, Wind Energy, 9, 63–74, https://doi.org/10.1002/we.190, 2006. a
Hirth, L.: The market value of variable renewables: The effect of solar wind power variability on their relative price, Energ. Econ., 38, 218–236, https://doi.org/10.1016/j.eneco.2013.02.004, 2013. a
Hodge, B. S., Jain, H., Brancucci, C., Seo, G., Korpås, M., Kiviluoma, J., Holttinen, H., Smith, J. C., Orths, A., Estanqueiro, A., Söder, L., Flynn, D., Vrana, T. K., Kenyon, R. W., and Kroposki, B.: Addressing technical challenges in 100 % variable inverter-based renewable energy power systems, Wiley Interdisciplinary Reviews: Energy and Environment, 9, e376, https://doi.org/10.1002/wene.376, 2020. a
Ketterer, J. C.: The impact of wind power generation on the electricity price in Germany, Energ. Econ., 44, 270–280, https://doi.org/10.1016/j.eneco.2014.04.003, 2014. a
Martinez, A. and Iglesias, G.: Mapping of the levelised cost of energy for floating offshore wind in the European Atlantic, Renew. Sust. Energ. Rev., 154, 111889, https://doi.org/10.1016/j.rser.2021.111889, 2022. a
Molod, A., Takacs, L., Suarez, M., and Bacmeister, J.: Development of the GEOS-5 atmospheric general circulation model: evolution from MERRA to MERRA2, Geosci. Model Dev., 8, 1339–1356, https://doi.org/10.5194/gmd-8-1339-2015, 2015. a
Pfenninger, S. and Staffell, I.: Hourly wind capacity factors for the EU-28 plus Norway and Switzerland, based on MERRA-2, Renewables.ninja [data set], https://www.renewables.ninja/downloads (last access: 20 February 2024), 2023. a
Rijkswaterstaat: Wind op zee na 2030, https://windopzee.nl/onderwerpen/wind-zee/wanneer-hoeveel/wind-zee-2030-0/ (last access: 20 February 2024), 2022. a
Sensfuß, F., Ragwitz, M., and Genoese, M.: The merit-order effect: A detailed analysis of the price effect of renewable electricity generation on spot market prices in Germany, Energ. Policy, 36, 3086–3094, https://doi.org/10.1016/j.enpol.2008.03.035, 2008. a
Sørensen, J. N., Larsen, G. C., and Cazin-Bourguignon, A.: Production and Cost Assessment of Offshore Wind Power in the North Sea, J. Phys. Conf. Ser., 1934, 012019, https://doi.org/10.1088/1742-6596/1934/1/012019, 2021.
Staffell, I. and Green, R.: How does wind farm performance decline with age?, Renew. Energy, 66, 775–786, https://doi.org/10.1016/j.renene.2013.10.041, 2014. a
Staffell, I. and Pfenninger, S.: Using bias-corrected reanalysis to simulate current and future wind power output, Energy, 114, 1224–1239, https://doi.org/10.1016/j.energy.2016.08.068, 2016. a
Svendsen, H. G.: time-series analysis, Zenodo [data set and code], https://doi.org/10.5281/zenodo.10683602, 2024.
Vrana, T. K., Holttinen, H., Korpås, M., Härtel, P., Couto, A., Koivisto, M., Flynn, D., Lantz, E., Svendsen, H., Estanqueiro, A., and Frew, B.: Improving wind power market value with various aspects of diversification, EEM 2023 Conference, Lappeenranta, Finland, 6–8 June 2023, https://doi.org/10.1109/EEM58374.2023.10161990, 2023. a
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.
We developed new ways to plot comprehensive wind resource maps that show the revenue potential...
Altmetrics
Final-revised paper
Preprint