Articles | Volume 8, issue 5
https://doi.org/10.5194/wes-8-787-2023
© Author(s) 2023. 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-8-787-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 May 2023
Research article |
| 22 May 2023
| 22 May 2023
Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation
Peter Baas
CORRESPONDING AUTHOR
Whiffle, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Remco Verzijlbergh
Whiffle, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Department of Engineering Systems and Services, Delft University of Technology, Jaffalaan 5, 2628 BX Delft, the Netherlands
Pim van Dorp
Whiffle, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Harm Jonker
Whiffle, Molengraaffsingel 8, 2629 JD Delft, the Netherlands
Department of Geoscience and Remote Sensing, Delft University of Technology, Stevinweg 1, 2628 CN Delft, the Netherlands
Related authors
Bernard Postema, Remco Verzijlbergh, Pim van Dorp, Peter Baas, and Harm Jonker
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-54, https://doi.org/10.5194/wes-2024-54, 2024
Preprint under review for WES
Short summary
Short summary
Atmospheric large-eddy simulation is a technique that simulates weather conditions high detail, and is used to plan new wind farms. This research presents ways to estimate the long-term (10-year) power production of a wind farm, without having to simulate 10 years of weather, but much shorter (one year or less). The results show that the methods reduce the uncertainty in power production estimates by an order of magnitude, and that wind observations can be included as well to add more insight.
Gerard Schepers, Pim van Dorp, Remco Verzijlbergh, Peter Baas, and Harmen Jonker
Wind Energ. Sci., 6, 983–996, https://doi.org/10.5194/wes-6-983-2021, https://doi.org/10.5194/wes-6-983-2021, 2021
Short summary
Short summary
In this article the aeroelastic loads on a 10 MW turbine in response to unconventional wind conditions selected from a year-long large-eddy simulation on a site at the North Sea are evaluated. Thereto an assessment is made of the practical importance of these wind conditions within an aeroelastic context based on high-fidelity wind modelling. Moreover the accuracy of BEM-based methods for modelling such wind conditions is assessed.
Bernard Postema, Remco Verzijlbergh, Pim van Dorp, Peter Baas, and Harm Jonker
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2024-54, https://doi.org/10.5194/wes-2024-54, 2024
Preprint under review for WES
Short summary
Short summary
Atmospheric large-eddy simulation is a technique that simulates weather conditions high detail, and is used to plan new wind farms. This research presents ways to estimate the long-term (10-year) power production of a wind farm, without having to simulate 10 years of weather, but much shorter (one year or less). The results show that the methods reduce the uncertainty in power production estimates by an order of magnitude, and that wind observations can be included as well to add more insight.
Gerard Schepers, Pim van Dorp, Remco Verzijlbergh, Peter Baas, and Harmen Jonker
Wind Energ. Sci., 6, 983–996, https://doi.org/10.5194/wes-6-983-2021, https://doi.org/10.5194/wes-6-983-2021, 2021
Short summary
Short summary
In this article the aeroelastic loads on a 10 MW turbine in response to unconventional wind conditions selected from a year-long large-eddy simulation on a site at the North Sea are evaluated. Thereto an assessment is made of the practical importance of these wind conditions within an aeroelastic context based on high-fidelity wind modelling. Moreover the accuracy of BEM-based methods for modelling such wind conditions is assessed.
Related subject area
Thematic area: Fluid mechanics | Topic: Wakes and wind farm aerodynamics
Hyperparameter tuning framework for calibrating analytical wake models using SCADA data of an offshore wind farm
Synchronised WindScanner field measurements of the induction zone between two closely spaced wind turbines
Wind farm structural response and wake dynamics for an evolving stable boundary layer: computational and experimental comparisons
Improvements to the dynamic wake meandering model by incorporating the turbulent Schmidt number
An actuator sector model for wind power applications: a parametric study
Wind tunnel investigations of an individual pitch control strategy for wind farm power optimization
The near-wake development of a wind turbine operating in stalled conditions – Part 1: Assessment of numerical models
Data-driven optimisation of wind farm layout and wake steering with large-eddy simulations
Floating wind turbine motion signature in the far-wake spectral content – a wind tunnel experiment
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 1: Large-eddy-simulation study
Breakdown of the velocity and turbulence in the wake of a wind turbine – Part 2: Analytical modelling
Free-vortex models for wind turbine wakes under yaw misalignment – a validation study on far-wake effects
A method to correct for the effect of blockage and wakes on power performance measurements
Vortex model of the aerodynamic wake of airborne wind energy systems
A new RANS-based wind farm parameterization and inflow model for wind farm cluster modeling
The wind farm as a sensor: learning and explaining orographic and plant-induced flow heterogeneities from operational data
Multi-point in situ measurements of turbulent flow in a wind turbine wake and inflow with a fleet of uncrewed aerial systems
Addressing deep array effects and impacts to wake steering with the cumulative-curl wake model
Actuator line model using simplified force calculation methods
Brief communication: A clarification of wake recovery mechanisms
Predictive and stochastic reduced-order modeling of wind turbine wake dynamics
Wind turbine wake simulation with explicit algebraic Reynolds stress modeling
Including realistic upper atmospheres in a wind-farm gravity-wave model
Diederik van Binsbergen, Pieter-Jan Daems, Timothy Verstraeten, Amir R. Nejad, and Jan Helsen
Wind Energ. Sci., 9, 1507–1526, https://doi.org/10.5194/wes-9-1507-2024, https://doi.org/10.5194/wes-9-1507-2024, 2024
Short summary
Short summary
Wind farm yield assessment often relies on analytical wake models. Calibrating these models can be challenging due to the stochastic nature of wind. We developed a calibration framework that performs a multi-phase optimization on the tuning parameters using time series SCADA data. This yields a parameter distribution that more accurately reflects reality than a single value. Results revealed notable variation in resultant parameter values, influenced by nearby wind farms and coastal effects.
Anantha Padmanabhan Kidambi Sekar, Paul Hulsman, Marijn Floris van Dooren, and Martin Kühn
Wind Energ. Sci., 9, 1483–1505, https://doi.org/10.5194/wes-9-1483-2024, https://doi.org/10.5194/wes-9-1483-2024, 2024
Short summary
Short summary
We present induction zone measurements conducted with two synchronised lidars at a two-turbine wind farm. The induction zone flow was characterised for free, fully waked and partially waked flows. Due to the short turbine spacing, the lidars captured the interaction of the atmospheric boundary layer, induction zone and wake, evidenced by induction asymmetry and induction zone–wake interactions. The measurements will aid the process of further improving existing inflow and wake models.
Kelsey Shaler, Eliot Quon, Hristo Ivanov, and Jason Jonkman
Wind Energ. Sci., 9, 1451–1463, https://doi.org/10.5194/wes-9-1451-2024, https://doi.org/10.5194/wes-9-1451-2024, 2024
Short summary
Short summary
This paper presents a three-way verification and validation between an engineering-fidelity model, a high-fidelity model, and measured data for the wind farm structural response and wake dynamics during an evolving stable boundary layer of a small wind farm, generally with good agreement.
Peter Brugger, Corey D. Markfort, and Fernando Porté-Agel
Wind Energ. Sci., 9, 1363–1379, https://doi.org/10.5194/wes-9-1363-2024, https://doi.org/10.5194/wes-9-1363-2024, 2024
Short summary
Short summary
The dynamic wake meandering model (DWMM) assumes that wind turbine wakes are transported like a passive tracer by the large-scale turbulence of the atmospheric boundary layer. We show that both the downstream transport and the lateral transport of the wake have differences from the passive tracer assumption. We then propose to include the turbulent Schmidt number into the DWMM to account for the less efficient transport of momentum and show that it improves the quality of the model predictions.
Mohammad Mehdi Mohammadi, Hugo Olivares-Espinosa, Gonzalo Pablo Navarro Diaz, and Stefan Ivanell
Wind Energ. Sci., 9, 1305–1321, https://doi.org/10.5194/wes-9-1305-2024, https://doi.org/10.5194/wes-9-1305-2024, 2024
Short summary
Short summary
This paper has put forward a set of recommendations regarding the actuator sector model implementation details to improve the capability of the model to reproduce similar results compared to those obtained by an actuator line model, which is one of the most common ways used for numerical simulations of wind farms, while providing significant computational savings. This includes among others the velocity sampling method and a correction of the sampled velocities to calculate the blade forces.
Franz V. Mühle, Florian M. Heckmeier, Filippo Campagnolo, and Christian Breitsamter
Wind Energ. Sci., 9, 1251–1271, https://doi.org/10.5194/wes-9-1251-2024, https://doi.org/10.5194/wes-9-1251-2024, 2024
Short summary
Short summary
Wind turbines influence each other, and these wake effects limit the power production of downstream turbines. Controlling turbines collectively and not individually can limit such effects. We experimentally investigate a control strategy increasing mixing in the wake. We want to see the potential of this so-called Helix control for power optimization and understand the flow physics. Our study shows that the control technique leads to clearly faster wake recovery and thus higher power production.
Pascal Weihing, Marion Cormier, Thorsten Lutz, and Ewald Krämer
Wind Energ. Sci., 9, 933–962, https://doi.org/10.5194/wes-9-933-2024, https://doi.org/10.5194/wes-9-933-2024, 2024
Short summary
Short summary
This study evaluates different approaches to simulate the near-wake flow of a wind turbine. The test case is in off-design conditions of the wind turbine, where the flow is separated from the blades and therefore very difficult to predict. The evaluation of simulation techniques is key to understand their limitations and to deepen the understanding of the near-wake physics. This knowledge can help to derive new wind farm design methods for yield-optimized farm layouts.
Nikolaos Bempedelis, Filippo Gori, Andrew Wynn, Sylvain Laizet, and Luca Magri
Wind Energ. Sci., 9, 869–882, https://doi.org/10.5194/wes-9-869-2024, https://doi.org/10.5194/wes-9-869-2024, 2024
Short summary
Short summary
This paper proposes a computational method to maximise the power production of wind farms through two strategies: layout optimisation and yaw angle optimisation. The proposed method relies on high-fidelity computational modelling of wind farm flows and is shown to be able to effectively maximise wind farm power production. Performance improvements relative to conventional optimisation strategies based on low-fidelity models can be attained, particularly in scenarios of increased flow complexity.
Benyamin Schliffke, Boris Conan, and Sandrine Aubrun
Wind Energ. Sci., 9, 519–532, https://doi.org/10.5194/wes-9-519-2024, https://doi.org/10.5194/wes-9-519-2024, 2024
Short summary
Short summary
This paper studies the consequences of floater motions for the wake properties of a floating wind turbine. Since wake interactions are responsible for power production loss in wind farms, it is important that we know whether the tools that are used to predict this production loss need to be upgraded to take into account these aspects. Our wind tunnel study shows that the signature of harmonic floating motions can be observed in the far wake of a wind turbine, when motions have strong amplitudes.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 97–117, https://doi.org/10.5194/wes-9-97-2024, https://doi.org/10.5194/wes-9-97-2024, 2024
Short summary
Short summary
Wind turbine wakes affect the production and lifecycle of downstream turbines. They can be predicted with the dynamic wake meandering (DWM) method. In this paper, the authors break down the velocity and turbulence in the wake of a wind turbine into several terms. They show that it is implicitly assumed in the DWM that some of these terms are neglected. With high-fidelity simulations, it is shown that this can lead to some errors, in particular for the maximum turbulence added by the wake.
Erwan Jézéquel, Frédéric Blondel, and Valéry Masson
Wind Energ. Sci., 9, 119–139, https://doi.org/10.5194/wes-9-119-2024, https://doi.org/10.5194/wes-9-119-2024, 2024
Short summary
Short summary
Analytical models allow us to quickly compute the decreased power output and lifetime induced by wakes in a wind farm. This is achieved by evaluating the modified velocity and turbulence in the wake. In this work, we present a new model based on the velocity and turbulence breakdowns presented in Part 1. This new model is physically based, allows us to compute the whole turbulence profile (rather than the maximum value) and is built to take atmospheric stability into account.
Maarten J. van den Broek, Delphine De Tavernier, Paul Hulsman, Daan van der Hoek, Benjamin Sanderse, and Jan-Willem van Wingerden
Wind Energ. Sci., 8, 1909–1925, https://doi.org/10.5194/wes-8-1909-2023, https://doi.org/10.5194/wes-8-1909-2023, 2023
Short summary
Short summary
As wind turbines produce power, they leave behind wakes of slow-moving air. We analyse three different models to predict the effects of these wakes on downstream wind turbines. The models are validated with experimental data from wind tunnel studies for steady and time-varying conditions. We demonstrate that the models are suitable for optimally controlling wind turbines to improve power production in large wind farms.
Alessandro Sebastiani, James Bleeg, and Alfredo Peña
Wind Energ. Sci., 8, 1795–1808, https://doi.org/10.5194/wes-8-1795-2023, https://doi.org/10.5194/wes-8-1795-2023, 2023
Short summary
Short summary
The power curve of a wind turbine indicates the turbine power output in relation to the wind speed. Therefore, power curves are critically important to estimate the production of future wind farms as well as to assess whether operating wind farms are functioning correctly. Since power curves are often measured in wind farms, they might be affected by the interactions between the turbines. We show that these effects are not negligible and present a method to correct for them.
Filippo Trevisi, Carlo E. D. Riboldi, and Alessandro Croce
Wind Energ. Sci., 8, 999–1016, https://doi.org/10.5194/wes-8-999-2023, https://doi.org/10.5194/wes-8-999-2023, 2023
Short summary
Short summary
Modeling the aerodynamic wake of airborne wind energy systems (AWESs) is crucial to properly estimating power production and to designing such systems. The velocities induced at the AWES from its own wake are studied with a model for the near wake and one for the far wake, using vortex methods. The model is validated with the lifting-line free-vortex wake method implemented in QBlade.
Maarten Paul van der Laan, Oscar García-Santiago, Mark Kelly, Alexander Meyer Forsting, Camille Dubreuil-Boisclair, Knut Sponheim Seim, Marc Imberger, Alfredo Peña, Niels Nørmark Sørensen, and Pierre-Elouan Réthoré
Wind Energ. Sci., 8, 819–848, https://doi.org/10.5194/wes-8-819-2023, https://doi.org/10.5194/wes-8-819-2023, 2023
Short summary
Short summary
Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses. In this work, an efficient numerical method is presented that can be used to estimate these energy losses. The novel method is verified with higher-fidelity numerical models and validated with measurements of an existing wind farm cluster.
Robert Braunbehrens, Andreas Vad, and Carlo L. Bottasso
Wind Energ. Sci., 8, 691–723, https://doi.org/10.5194/wes-8-691-2023, https://doi.org/10.5194/wes-8-691-2023, 2023
Short summary
Short summary
The paper presents a new method in which wind turbines in a wind farm act as local sensors, in this way detecting the flow that develops within the power plant. Through this technique, we are able to identify effects on the flow generated by the plant itself and by the orography of the terrain. The new method not only delivers a flow model of much improved quality but can also help in understanding phenomena that drive the farm performance.
Tamino Wetz and Norman Wildmann
Wind Energ. Sci., 8, 515–534, https://doi.org/10.5194/wes-8-515-2023, https://doi.org/10.5194/wes-8-515-2023, 2023
Short summary
Short summary
In the present study, for the first time, the SWUF-3D fleet of multirotors is deployed for field measurements on an operating 2 MW wind turbine (WT) in complex terrain. The fleet of multirotors has the potential to fill the meteorological gap of observations in the near wake of WTs with high-temporal and high-spatial-resolution wind vector measurements plus temperature, humidity and pressure. The flow up- and downstream of the WT is measured simultaneously at multiple spatial positions.
Christopher J. Bay, Paul Fleming, Bart Doekemeijer, Jennifer King, Matt Churchfield, and Rafael Mudafort
Wind Energ. Sci., 8, 401–419, https://doi.org/10.5194/wes-8-401-2023, https://doi.org/10.5194/wes-8-401-2023, 2023
Short summary
Short summary
This paper introduces the cumulative-curl wake model that allows for the fast and accurate prediction of wind farm energy production wake interactions. The cumulative-curl model expands several existing wake models to make the simulation of farms more accurate and is implemented in a computationally efficient manner such that it can be used for wind farm layout design and controller development. The model is validated against high-fidelity simulations and data from physical wind farms.
Gonzalo Pablo Navarro Diaz, Alejandro Daniel Otero, Henrik Asmuth, Jens Nørkær Sørensen, and Stefan Ivanell
Wind Energ. Sci., 8, 363–382, https://doi.org/10.5194/wes-8-363-2023, https://doi.org/10.5194/wes-8-363-2023, 2023
Short summary
Short summary
In this paper, the capacity to simulate transient wind turbine wake interaction problems using limited wind turbine data has been extended. The key novelty is the creation of two new variants of the actuator line technique in which the rotor blade forces are computed locally using generic load data. The analysis covers a partial wake interaction case between two wind turbines for a uniform laminar inflow and for a turbulent neutral atmospheric boundary layer inflow.
Maarten Paul van der Laan, Mads Baungaard, and Mark Kelly
Wind Energ. Sci., 8, 247–254, https://doi.org/10.5194/wes-8-247-2023, https://doi.org/10.5194/wes-8-247-2023, 2023
Short summary
Short summary
Understanding wind turbine wake recovery is important to mitigate energy losses in wind farms. Wake recovery is often assumed or explained to be dependent on the first-order derivative of velocity. In this work we show that wind turbine wakes recover mainly due to the second-order derivative of the velocity, which transport momentum from the freestream towards the wake center. The wake recovery mechanisms and results of a high-fidelity numerical simulation are illustrated using a simple model.
Søren Juhl Andersen and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 2117–2133, https://doi.org/10.5194/wes-7-2117-2022, https://doi.org/10.5194/wes-7-2117-2022, 2022
Short summary
Short summary
Simulating the turbulent flow inside large wind farms is inherently complex and computationally expensive. A new and fast model is developed based on data from high-fidelity simulations. The model captures the flow dynamics with correct statistics for a wide range of flow conditions. The model framework provides physical insights and presents a generalization of high-fidelity simulation results beyond the case-specific scenarios, which has significant potential for future turbulence modeling.
Mads Baungaard, Stefan Wallin, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 1975–2002, https://doi.org/10.5194/wes-7-1975-2022, https://doi.org/10.5194/wes-7-1975-2022, 2022
Short summary
Short summary
Wind turbine wakes in the neutral atmospheric surface layer are simulated with Reynolds-averaged Navier–Stokes (RANS) using an explicit algebraic Reynolds stress model. Contrary to standard two-equation turbulence models, it can predict turbulence anisotropy and complex physical phenomena like secondary motions. For the cases considered, it improves Reynolds stress, turbulence intensity, and velocity deficit predictions, although a more top-hat-shaped profile is observed for the latter.
Koen Devesse, Luca Lanzilao, Sebastiaan Jamaer, Nicole van Lipzig, and Johan Meyers
Wind Energ. Sci., 7, 1367–1382, https://doi.org/10.5194/wes-7-1367-2022, https://doi.org/10.5194/wes-7-1367-2022, 2022
Short summary
Short summary
Recent research suggests that offshore wind farms might form such a large obstacle to the wind that it already decelerates before reaching the first turbines. Part of this phenomenon could be explained by gravity waves. Research on these gravity waves triggered by mountains and hills has found that variations in the atmospheric state with altitude can have a large effect on how they behave. This paper is the first to take the impact of those vertical variations into account for wind farms.
Cited articles
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in
conventionally neutral wind farms, J. Fluid Mech., 814, 95–130,
https://doi.org/10.1017/jfm.2017.11, 2017. a
Allaerts, D., Vanden Broucke, S., Van Lipzig, N., and Meyers, J.: Annual
impact of wind-farm gravity waves on the Belgian–Dutch offshore wind-farm
cluster, J. Phys.: Conf. Ser., 1037, 072006, https://doi.org/10.1088/1742-6596/1037/7/072006, 2018. a
Bleeg, J., Purcell, M., Ruisi, R., and Traiger, E.: Wind farm blockage and the consequences of neglecting its impact on energy production, Energies, 11, 1609, https://doi.org/10.3390/en11061609, 2018. a, b
Böing, S.: The interaction between deep convective clouds and their
environment, PhD thesis, TU Delft,
https://doi.org/10.4233/uuid:aa9e6037-b9cb-4ea0-9eb0-a47bf1dfc940, 2014. a
Calaf, M., Meneveau, C., and Meyers, J.: Large eddy simulation study of fully
developed wind-turbine array boundary layers, Phys. Fluids, 22, 1–16,
https://doi.org/10.1063/1.862466, 2010. a
Charnock, H.: Wind stress on a water surface, Q. J. Roy. Meteorol. Soc., 81, 639–640, 1955. a
ECMWF: IFS Manual Part IV: Physical Processes. IFS Documentation Cy43R3,
Tech. rep., ECMWF,
https://www.ecmwf.int/en/elibrary/17736-part-iv-physical-processes (last access: 18 May 2023), 2017. a
European Commission: Offshore Renewable Energy Strategy,
https://energy.ec.europa.eu/topics/renewable-energy/eu-strategy-offshore-renewable-energy_en
(last access: 18 May 2023), 2020. a
Fischereit, J., Brown, R., Gia Larsen, X., Badger, J., and Hawkes, G.: Wind
farm wakes simulated using WRF, Bound.-Lay. Meteorol., 182, 175–224, 2022. a
Fitch, A., Olson, J., Lundquist, J., Dudhia, J., Gupta, A., 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
Grabowski, W.: Toward Cloud Resolving Modeling of Large-Scale Tropical
Circulations: A Simple Cloud Microphysics Parameterization, J. Atmos. Sci.,
55, 3283–3298, 1998. a
Hersbach, H., Bell, B., P., B., Hirahara, S., Horányi, A., Muñoz-Sabater, J.,
Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C.,
Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J.,
Bonavita, De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R.,
Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S.,
Hogan, R., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P.,
Lupu, C., Radnoti, G., De Rosnay, P., Rozum, I., Vamborg, F., Villaume, S.,
and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteorol. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Heus, T., Van Heerwaarden, C. C., Jonker, H. J. J., Siebesma, A. P., Axelsen, S., Van Den Dries, K., Geoffroy, O., Moene, A. F., Pino, D., De Roode, S. R., and Vila-Guerau de Arellano, J.: Formulation of the Dutch Atmospheric Large-Eddy Simulation (DALES) and overview of its applications, Geosci. Model Dev., 3, 415–444, https://doi.org/10.5194/gmd-3-415-2010, 2010. a, b
Khairoutdinov, M. and Randall, D.: Cloud Resolving Modeling of the ARM Summer 1997 IOP: Model Formulation, Results, Uncertainties, and Sensitivities, J. Atmos. Sci., 60, 607–625, 2003. a
Lanzilao, L. and Meyers, J.: Effects of self-induced gravity waves on finite
wind-farm operations using a large-eddy simulation framework, J. Phys.:
Conf. Ser., 2265, 022043, https://doi.org/10.1088/1742-6596/2265/2/022043, 2022. a
Maas, O. and Raasch, S.: Wake properties and power output of very large wind
farms for different meteorological conditions and turbine spacings: A
large-eddy simulation case study for the German Bight, Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022, 2022. a, b, c, d
Mehta, D., van Zuijlen, A. H., Koren, B., Holierhoek, J. G., and Bijl, H.:
Large Eddy Simulation of wind farm aerodynamics: A review, J. Wind Eng. Indust. Aerodynam., 133, 1–17, https://doi.org/10.1016/j.jweia.2014.07.002, 2014. a
Meyers, J. and Meneveau, C.: Large Eddy Simulations of Large Wind-Turbine
Arrays in the Atmospheric Boundary Layer, in: 48th AIAA Aerospace Sciences
Meeting Including the New Horizons Forum and Aerospace Exposition, 4–7 January 2010, Orlando, Florida, USA, 1–10,
https://doi.org/10.2514/6.2010-827, 2010. a
Neggers, R. A. J., Siebesma, A. P., and Heus, T.: Continuous single-column
model evaluation at a permanent meteorological supersite, B. Am. Meteorol. Soc., 93, 1389–1400, https://doi.org/10.1175/BAMS-D-11-00162.1, 2012. 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., Shepard, T., Bartherlmie, R., Hahmann, A., 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
Rozema, W., Bae, H. J., Moin, P., and Verstappen, R.: Minimum-dissipation
models for large-eddy simulation, Phys. Fluids, 27, 085107, https://doi.org/10.1063/1.4928700, 2015. a
RVO: General Information IJmuiden Ver,
https://offshorewind.rvo.nl/cms/view/5c06ac88-c12f-4903-89f3-27d66937b7e9/general-information-ijmuiden-ver (last access: 18 May 2023), 2022. a
Schalkwijk, J., Griffith, E. J., Post, F. H., and Jonker, H. J. J.:
High-performance simulations of turbulent clouds on a desktop PC, B.
Am. Meteorol. Soc., 93, 307–314, 2012. a
Schalkwijk, J., Jonker, H., Siebesma, A., and Van Meijgaard, E.: Weather
forecasting using GPU-based large-Eddy simulations, B. Am. Meteorol. Soc., 96, 715–723, https://doi.org/10.1175/BAMS-D-14-00114.1, 2015a. a
Schalkwijk, J., Jonker, H. J. J., Siebesma, A. P., and Bosveld, F. C.: A
Year-Long Large-Eddy Simulation of the Weather over Cabauw: an Overview,
Mon. Weather Rev., 143, 828–844, https://doi.org/10.1175/MWR-D-14-00293.1, 2015b. a
Schneemann, J., Theuer, F., Rott, A., Dörenkämper, M., and Kühn, M.: Offshore wind farm global blockage measured with scanning lidar, Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, 2021. a
Stevens, R. J. and Meneveau, C.: Flow Structure and Turbulence in Wind Farms, Annu. Rev. Fluid Mech., 49, 311–339, https://doi.org/10.1146/annurev-fluid-010816-060206, 2017. a, b
Stevens, R. J., Graham, J., and Meneveau, C.: A concurrent precursor inflow
method for Large Eddy Simulations and applications to finite length wind
farms, Renew. Energy, 68, 46–50, https://doi.org/10.1016/j.renene.2014.01.024, 2014. a
Strickland, J., Srinidhi, N., and Stevens, R.: Wind farm blockage in a stable atmospheric boundary layer, Renew. Energy, 197, 50–58,
https://doi.org/10.1016/j.renene.2022.07.108, 2022. a, b
The Esbjerg Declaration: The Esbjerg Declaration on The North Sea as a Green Power Plant of Europe, https://open.overheid.nl/documenten/ronl-1e299d084fbc5bfc2968d934ca2f4a97b3931d9f/pdf (last access: 18 May 2023), 2022.
a
Veers, P., Dykes, K., Lantz, E., Barth, S., Bottasso, C. L., Carlson, O.,
Clifton, A., Green, J., Green, P., Holttinen, H., Laird, D., Lehtomäki, V.,
Lundquist, J. K., Manwell, J., Marquis, M., Meneveau, C., Moriarty, P.,
Munduate, X., Muskulus, M., Naughton, J., Pao, L., Paquette, J., Peinke, J.,
Robertson, A., Rodrigo, J. S., Sempreviva, A. M., Smith, J. C., Tuohy, A.,
and Wiser, R.: Grand challenges in the science of wind energy, Science, 366,
eaau2027, https://doi.org/10.1126/science.aau2027, 2019. a
Verzijlbergh, R. A.: Atmospheric flows in large wind farms, Europhys. News,
52, 20–23, https://doi.org/10.1051/epn/2021502, 2021. a
Volker, P., Badger, J., Hahmann, A., 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
Wu, K. and Porté-Agel, F.: Flow Adjustment Inside and Around Large
Finite-Size Wind Farms, Energies, 10, 2164, https://doi.org/10.3390/en10122164, 2017. a, b
Short summary
This work studies the energy production and wake losses of large offshore wind farms with a large-eddy simulation model. Therefore, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW wind farm scenarios. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Also, a clear impact of atmospheric stability on the energy production is found.
This work studies the energy production and wake losses of large offshore wind farms with a...
Altmetrics
Final-revised paper
Preprint