Articles | Volume 7, issue 3
https://doi.org/10.5194/wes-7-1093-2022
© 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-7-1093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 May 2022
Research article |
| 24 May 2022
| 24 May 2022
Large-eddy simulation of airborne wind energy farms
Thomas Haas
Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Jochem De Schutter
Department of Microsystems Engineering, University of Freiburg, Georges-Köhler-Allee 102, 79110 Freiburg, Germany
Moritz Diehl
Department of Microsystems Engineering, University of Freiburg, Georges-Köhler-Allee 102, 79110 Freiburg, Germany
Department of Mathematics, University of Freiburg, Georges-Köhler-Allee 102, 79110 Freiburg, Germany
Department of Mechanical Engineering, KU Leuven, Celestijnenlaan 300, 3001 Leuven, Belgium
Related authors
No articles found.
Ruben Borgers, Marieke Dirksen, Ine L. Wijnant, Andrew Stepek, Ad Stoffelen, Naveed Akhtar, Jérôme Neirynck, Jonas Van de Walle, Johan Meyers, and Nicole P. M. van Lipzig
Wind Energ. Sci., 9, 697–719, https://doi.org/10.5194/wes-9-697-2024, https://doi.org/10.5194/wes-9-697-2024, 2024
Short summary
Short summary
Wind farms at sea are becoming more densely clustered, which means that next to individual wind turbines interfering with each other in a single wind farm also interference between wind farms becomes important. Using a climate model, this study shows that the efficiency of wind farm clusters and the interference between the wind farms in the cluster depend strongly on the properties of the individual wind farms and are also highly sensitive to the spacing between the wind farms.
Nick Janssens and Johan Meyers
Wind Energ. Sci., 9, 65–95, https://doi.org/10.5194/wes-9-65-2024, https://doi.org/10.5194/wes-9-65-2024, 2024
Short summary
Short summary
Proper wind farm control may vastly contribute to Europe's plan to go carbon neutral. However, current strategies don't account for turbine–wake interactions affecting power extraction. High-fidelity models (e.g., LES) are needed to accurately model this but are considered too slow in practice. By coarsening the resolution, we were able to design an efficient LES-based controller with real-time potential. This may allow us to bridge the gap towards practical wind farm control in the near future.
Jérôme Neirynck, Jonas Van de Walle, Ruben Borgers, Sebastiaan Jamaer, Johan Meyers, Ad Stoffelen, and Nicole P. M. van Lipzig
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2023-116, https://doi.org/10.5194/wes-2023-116, 2023
Revised manuscript under review for WES
Short summary
Short summary
In our study, we assess how mesoscale weather systems influence wind speed variations and their impact on offshore wind energy production fluctuations. We've observed, for instance, that weather systems originating over land lead to sea wind speed variations. Additionally, we've noted that power fluctuations are typically more significant in summer, despite potentially larger winter wind speed variations. These findings are valuable for grid management and optimizing renewable energy deployment.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 8, 1153–1178, https://doi.org/10.5194/wes-8-1153-2023, https://doi.org/10.5194/wes-8-1153-2023, 2023
Short summary
Short summary
This study investigates the performance of pumping-mode ground-generation airborne wind energy systems by determining power-optimal flight trajectories based on realistic, k-means clustered, vertical wind velocity profiles. These profiles, derived from mesoscale weather simulations at an offshore and an onshore site in Europe, are incorporated into an optimal control model that maximizes average cycle power by optimizing the kite's trajectory.
Ishaan Sood, Elliot Simon, Athanasios Vitsas, Bart Blockmans, Gunner C. Larsen, and Johan Meyers
Wind Energ. Sci., 7, 2469–2489, https://doi.org/10.5194/wes-7-2469-2022, https://doi.org/10.5194/wes-7-2469-2022, 2022
Short summary
Short summary
In this work, we conduct a validation study to compare a numerical solver against measurements obtained from the offshore Lillgrund wind farm. By reusing a previously developed inflow turbulent dataset, the atmospheric conditions at the wind farm were recreated, and the general performance trends of the turbines were captured well. The work increases the reliability of numerical wind farm solvers while highlighting the challenges of accurately representing large wind farms using such solvers.
Paul Veers, Katherine Dykes, Sukanta Basu, Alessandro Bianchini, Andrew Clifton, Peter Green, Hannele Holttinen, Lena Kitzing, Branko Kosovic, Julie K. Lundquist, Johan Meyers, Mark O'Malley, William J. Shaw, and Bethany Straw
Wind Energ. Sci., 7, 2491–2496, https://doi.org/10.5194/wes-7-2491-2022, https://doi.org/10.5194/wes-7-2491-2022, 2022
Short summary
Short summary
Wind energy will play a central role in the transition of our energy system to a carbon-free future. However, many underlying scientific issues remain to be resolved before wind can be deployed in the locations and applications needed for such large-scale ambitions. The Grand Challenges are the gaps in the science left behind during the rapid growth of wind energy. This article explains the breadth of the unfinished business and introduces 10 articles that detail the research needs.
Johan Meyers, Carlo Bottasso, Katherine Dykes, Paul Fleming, Pieter Gebraad, Gregor Giebel, Tuhfe Göçmen, and Jan-Willem van Wingerden
Wind Energ. Sci., 7, 2271–2306, https://doi.org/10.5194/wes-7-2271-2022, https://doi.org/10.5194/wes-7-2271-2022, 2022
Short summary
Short summary
We provide a comprehensive overview of the state of the art and the outstanding challenges in wind farm flow control, thus identifying the key research areas that could further enable commercial uptake and success. To this end, we have structured the discussion on challenges and opportunities into four main areas: (1) insight into control flow physics, (2) algorithms and AI, (3) validation and industry implementation, and (4) integrating control with system design
(co-design).
Konstanze Kölle, Tuhfe Göçmen, Irene Eguinoa, Leonardo Andrés Alcayaga Román, Maria Aparicio-Sanchez, Ju Feng, Johan Meyers, Vasilis Pettas, and Ishaan Sood
Wind Energ. Sci., 7, 2181–2200, https://doi.org/10.5194/wes-7-2181-2022, https://doi.org/10.5194/wes-7-2181-2022, 2022
Short summary
Short summary
The paper studies wind farm flow control (WFFC) in simulations with variable electricity prices. The results indicate that considering the electricity price in the operational strategy can be beneficial with respect to the gained income compared to focusing on the power gain only. Moreover, revenue maximization by balancing power production and structural load reduction is demonstrated at the example of a single wind turbine.
Markus Sommerfeld, Martin Dörenkämper, Jochem De Schutter, and Curran Crawford
Wind Energ. Sci., 7, 1847–1868, https://doi.org/10.5194/wes-7-1847-2022, https://doi.org/10.5194/wes-7-1847-2022, 2022
Short summary
Short summary
This research explores the ground-generation airborne wind energy system (AWES) design space and investigates scaling effects by varying design parameters such as aircraft wing size, aerodynamic efficiency and mass. Therefore, representative simulated onshore and offshore wind data are implemented into an AWES trajectory optimization model. We estimate optimal annual energy production and capacity factors as well as a minimal operational lift-to-weight ratio.
Tuhfe Göçmen, Filippo Campagnolo, Thomas Duc, Irene Eguinoa, Søren Juhl Andersen, Vlaho Petrović, Lejla Imširović, Robert Braunbehrens, Jaime Liew, Mads Baungaard, Maarten Paul van der Laan, Guowei Qian, Maria Aparicio-Sanchez, Rubén González-Lope, Vinit V. Dighe, Marcus Becker, Maarten J. van den Broek, Jan-Willem van Wingerden, Adam Stock, Matthew Cole, Renzo Ruisi, Ervin Bossanyi, Niklas Requate, Simon Strnad, Jonas Schmidt, Lukas Vollmer, Ishaan Sood, and Johan Meyers
Wind Energ. Sci., 7, 1791–1825, https://doi.org/10.5194/wes-7-1791-2022, https://doi.org/10.5194/wes-7-1791-2022, 2022
Short summary
Short summary
The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of wind farm flow control benefits. Here we present the first part of the benchmark results for three blind tests with large-scale rotors and 11 participating models in total, via direct power comparisons at the turbines as well as the observed or estimated power gain at the wind farm level under wake steering control strategy.
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.
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271, https://doi.org/10.5194/wes-6-247-2021, https://doi.org/10.5194/wes-6-247-2021, 2021
Short summary
Short summary
This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.
Wim Munters and Johan Meyers
Wind Energ. Sci., 3, 409–425, https://doi.org/10.5194/wes-3-409-2018, https://doi.org/10.5194/wes-3-409-2018, 2018
Short summary
Short summary
Wake interactions in wind farms result in power losses for downstream turbines. We aim to mitigate these losses through coordinated control of the induced slowdown of the wind by each turbine. We further analyze results from earlier work towards the utilization of such control strategies in practice. Coherent vortex shedding is identified and mimicked by a sinusoidal control. The latter is shown to increase power in downstream turbines and is robust to turbine spacing and turbulence intensity.
Sjoerd Boersma, Bart Doekemeijer, Mehdi Vali, Johan Meyers, and Jan-Willem van Wingerden
Wind Energ. Sci., 3, 75–95, https://doi.org/10.5194/wes-3-75-2018, https://doi.org/10.5194/wes-3-75-2018, 2018
Short summary
Short summary
Controlling the flow within wind farms to reduce the fatigue loads and provide grid facilities such as the delivery of a demanded power is a challenging control problem due to the underlying time-varying non-linear wake dynamics. In this paper, a control-oriented dynamical wind farm model is presented and validated with high-fidelity wind farm models. In contrast to the latter models, the model presented in this work is computationally efficient and hence suitable for online wind farm control.
Carl R. Shapiro, Johan Meyers, Charles Meneveau, and Dennice F. Gayme
Wind Energ. Sci., 3, 11–24, https://doi.org/10.5194/wes-3-11-2018, https://doi.org/10.5194/wes-3-11-2018, 2018
Short summary
Short summary
We investigate the capability of wind farms to track a power reference signal to help ensure reliable power grid operations. The wind farm controller is based on a simple dynamic wind farm model and tested using high-fidelity simulations. We find that the dynamic nature of the wind farm model is vital for tracking the power signal, and the controlled wind farm would pass industry performance tests in most cases.
Vahid S. Bokharaie, Pieter Bauweraerts, and Johan Meyers
Wind Energ. Sci., 1, 311–325, https://doi.org/10.5194/wes-1-311-2016, https://doi.org/10.5194/wes-1-311-2016, 2016
Short summary
Short summary
Given a wind farm with known dimensions and number of wind turbines, we try to find the optimum positioning of wind turbines that maximises wind-farm energy production. We propose an optimisation approach that is based on a hybrid combination of large-eddy simulation (LES) and the Jensen model; in this approach optimisation is mainly performed using the Jensen model, and LES is used at a few points only during optimisation for online tuning of the Jensen model.
Related subject area
Wind and turbulence
Evaluation of obstacle modelling approaches for resource assessment and small wind turbine siting: case study in the northern Netherlands
Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models
Investigation into boundary layer transition using wall-resolved large-eddy simulations and modeled inflow turbulence
Evaluation of the global-blockage effect on power performance through simulations and measurements
Development of an automatic thresholding method for wake meandering studies and its application to the data set from scanning wind lidar
Turbulence statistics from three different nacelle lidars
RANS modeling of a single wind turbine wake in the unstable surface layer
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
Validation of wind resource and energy production simulations for small wind turbines in the United States
Four-dimensional wind field generation for the aeroelastic simulation of wind turbines with lidars
Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?
The five main influencing factors for lidar errors in complex terrain
Meso- to microscale modeling of atmospheric stability effects on wind turbine wake behavior in complex terrain
Validation of a coupled atmospheric–aeroelastic model system for wind turbine power and load calculations
Optimal closed-loop wake steering – Part 2: Diurnal cycle atmospheric boundary layer conditions
Development of a curled wake of a yawed wind turbine under turbulent and sheared inflow
Application of the Townsend–George theory for free shear flows to single and double wind turbine wakes – a wind tunnel study
On the measurement of stability parameter over complex mountainous terrain
Field measurements of wake meandering at a utility-scale wind turbine with nacelle-mounted Doppler lidars
The 3 km Norwegian reanalysis (NORA3) – a validation of offshore wind resources in the North Sea and the Norwegian Sea
On turbulence models and lidar measurements for wind turbine control
Seasonal effects in the long-term correction of short-term wind measurements using reanalysis data
On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus
Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling
The smoother the better? A comparison of six post-processing methods to improve short-term offshore wind power forecasts in the Baltic Sea
Statistical impact of wind-speed ramp events on turbines, via observations and coupled fluid-dynamic and aeroelastic simulations
Probabilistic estimation of the Dynamic Wake Meandering model parameters using SpinnerLidar-derived wake characteristics
Recovery processes in a large offshore wind farm
Extreme wind shear events in US offshore wind energy areas and the role of induced stratification
WRF-simulated low-level jets over Iowa: characterization and sensitivity studies
Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data
New methods to improve the vertical extrapolation of near-surface offshore wind speeds
Wind turbine load validation in wakes using wind field reconstruction techniques and nacelle lidar wind retrievals
A pressure-driven atmospheric boundary layer model satisfying Rossby and Reynolds number similarity
Design and analysis of a wake model for spatially heterogeneous flow
Evaluation of tilt control for wind-turbine arrays in the atmospheric boundary layer
Evaluation of idealized large-eddy simulations performed with the Weather Research and Forecasting model using turbulence measurements from a 250 m meteorological mast
Wind turbines in atmospheric flow: fluid–structure interaction simulations with hybrid turbulence modeling
Offshore wind farm global blockage measured with scanning lidar
Understanding and mitigating the impact of data gaps on offshore wind resource estimates
Investigating the loads and performance of a model horizontal axis wind turbine under reproducible IEC extreme operational conditions
Validation of the dynamic wake meandering model with respect to loads and power production
Method for airborne measurement of the spatial wind speed distribution above complex terrain
Axial induction controller field test at Sedini wind farm
Wake redirection at higher axial induction
An overview of wind-energy-production prediction bias, losses, and uncertainties
Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error
Set-point optimization in wind farms to mitigate effects of flow blockage induced by atmospheric gravity waves
Field experiment for open-loop yaw-based wake steering at a commercial onshore wind farm in Italy
Computational analysis of high-lift-generating airfoils for diffuser-augmented wind turbines
Caleb Phillips, Lindsay M. Sheridan, Patrick Conry, Dimitrios K. Fytanidis, Dmitry Duplyakin, Sagi Zisman, Nicolas Duboc, Matt Nelson, Rao Kotamarthi, Rod Linn, Marc Broersma, Timo Spijkerboer, and Heidi Tinnesand
Wind Energ. Sci., 7, 1153–1169, https://doi.org/10.5194/wes-7-1153-2022, https://doi.org/10.5194/wes-7-1153-2022, 2022
Short summary
Short summary
Adoption of distributed wind turbines for energy generation is hindered by challenges associated with siting and accurate estimation of the wind resource. This study evaluates classic and commonly used methods alongside new state-of-the-art models derived from simulations and machine learning approaches using a large dataset from the Netherlands. We find that data-driven methods are most effective at predicting production at real sites and new models reliably outperform classic methods.
Jana Fischereit, Kurt Schaldemose Hansen, Xiaoli Guo Larsén, Maarten Paul van der Laan, Pierre-Elouan Réthoré, and Juan Pablo Murcia Leon
Wind Energ. Sci., 7, 1069–1091, https://doi.org/10.5194/wes-7-1069-2022, https://doi.org/10.5194/wes-7-1069-2022, 2022
Short summary
Short summary
Wind turbines extract kinetic energy from the flow to create electricity. This induces a wake of reduced wind speed downstream of a turbine and consequently downstream of a wind farm. Different types of numerical models have been developed to calculate this effect. In this study, we compared models of different complexity, together with measurements over two wind farms. We found that higher-fidelity models perform better and the considered rapid models cannot fully capture the wake effect.
Brandon Arthur Lobo, Alois Peter Schaffarczyk, and Michael Breuer
Wind Energ. Sci., 7, 967–990, https://doi.org/10.5194/wes-7-967-2022, https://doi.org/10.5194/wes-7-967-2022, 2022
Short summary
Short summary
This research involves studying the flow around the section of a wind turbine blade, albeit at a lower Reynolds number or flow speed, using wall-resolved large-eddy simulations, a form of computer simulation that resolves the important scales of the flow. Among the many interesting results, it is shown that the energy entering the boundary layer around the airfoil or section of the blade is proportional to the square of the incoming flow turbulence intensity.
Alessandro Sebastiani, Alfredo Peña, Niels Troldborg, and Alexander Meyer Forsting
Wind Energ. Sci., 7, 875–886, https://doi.org/10.5194/wes-7-875-2022, https://doi.org/10.5194/wes-7-875-2022, 2022
Short summary
Short summary
The power performance of a wind turbine is often tested with the turbine standing in a row of several wind turbines, as it is assumed that the performance is not affected by the neighbouring turbines. We test this assumption with both simulations and measurements, and we show that the power performance can be either enhanced or lowered by the neighbouring wind turbines. Consequently, we also show how power performance testing might be biased when performed on a row of several wind turbines.
Maria Krutova, Mostafa Bakhoday-Paskyabi, Joachim Reuder, and Finn Gunnar Nielsen
Wind Energ. Sci., 7, 849–873, https://doi.org/10.5194/wes-7-849-2022, https://doi.org/10.5194/wes-7-849-2022, 2022
Short summary
Short summary
We described a new automated method to separate the wind turbine wake from the undisturbed flow. The method relies on the wind speed distribution in the measured wind field to select one specific threshold value and split the measurements into wake and background points. The purpose of the method is to reduce the amount of data required – the proposed algorithm does not need precise information on the wind speed or direction and can run on the image instead of the measured data.
Wei Fu, Alfredo Peña, and Jakob Mann
Wind Energ. Sci., 7, 831–848, https://doi.org/10.5194/wes-7-831-2022, https://doi.org/10.5194/wes-7-831-2022, 2022
Short summary
Short summary
Measuring the variability of the wind is essential to operate the wind turbines safely. Lidars of different configurations have been placed on the turbines’ nacelle to measure the inflow remotely. This work found that the multiple-beam lidar is the only one out of the three employed nacelle lidars that can give detailed information about the inflow variability. The other two commercial lidars, which have two and four beams, respectively, measure only the fluctuation in the along-wind direction.
Mads Baungaard, Maarten Paul van der Laan, and Mark Kelly
Wind Energ. Sci., 7, 783–800, https://doi.org/10.5194/wes-7-783-2022, https://doi.org/10.5194/wes-7-783-2022, 2022
Short summary
Short summary
Wind turbine wakes are dependent on the atmospheric conditions, and it is therefore important to be able to simulate in various different atmospheric conditions. This paper concerns the specific case of an unstable atmospheric surface layer, which is the lower part of the typical daytime atmospheric boundary layer. A simple flow model is suggested and tested for a range of single-wake scenarios, and it shows promising results for velocity deficit predictions.
Oliver Maas and Siegfried Raasch
Wind Energ. Sci., 7, 715–739, https://doi.org/10.5194/wes-7-715-2022, https://doi.org/10.5194/wes-7-715-2022, 2022
Short summary
Short summary
In the future there will be very large wind farm clusters in the German Bight. This study investigates how the wind field is affected by these very large wind farms and how much energy can be extracted by the wind turbines. Very large wind farms do not only reduce the wind speed but can also cause a change in wind direction or temperature. The extractable energy per wind turbine is much smaller for large wind farms than for small wind farms due to the reduced wind speed inside the wind farms.
Lindsay M. Sheridan, Caleb Phillips, Alice C. Orrell, Larry K. Berg, Heidi Tinnesand, Raj K. Rai, Sagi Zisman, Dmitry Duplyakin, and Julia E. Flaherty
Wind Energ. Sci., 7, 659–676, https://doi.org/10.5194/wes-7-659-2022, https://doi.org/10.5194/wes-7-659-2022, 2022
Short summary
Short summary
The small wind community relies on simplified wind models and energy production simulation tools to obtain energy generation expectations. We gathered actual wind speed and turbine production data across the US to test the accuracy of models and tools for small wind turbines. This study provides small wind installers and owners with the error metrics and sources of error associated with using models and tools to make performance estimates, empowering them to adjust expectations accordingly.
Yiyin Chen, Feng Guo, David Schlipf, and Po Wen Cheng
Wind Energ. Sci., 7, 539–558, https://doi.org/10.5194/wes-7-539-2022, https://doi.org/10.5194/wes-7-539-2022, 2022
Short summary
Short summary
Lidar-assisted control of wind turbines requires a wind field generator capable of simulating wind evolution. Out of this need, we extend the Veers method for 3D wind field generation to 4D and propose a two-step Cholesky decomposition approach. Based on this, we develop a 4D wind field generator – evoTurb – coupled with TurbSim and Mann turbulence generator. We further investigate the impacts of the spatial discretization in 4D wind fields on lidar simulations to provide practical suggestions.
Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young
Wind Energ. Sci., 7, 487–504, https://doi.org/10.5194/wes-7-487-2022, https://doi.org/10.5194/wes-7-487-2022, 2022
Short summary
Short summary
In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
Tobias Klaas-Witt and Stefan Emeis
Wind Energ. Sci., 7, 413–431, https://doi.org/10.5194/wes-7-413-2022, https://doi.org/10.5194/wes-7-413-2022, 2022
Short summary
Short summary
Light detection and ranging (lidar) has become a valuable technology to assess the wind resource at hub height of modern wind turbines. However, because of their measurement principle, common lidars suffer from errors at orographically complex, i.e. hilly or mountainous, sites. This study analyses the impact of the five main influencing factors in a non-dimensional, model-based parameter study.
Adam S. Wise, James M. T. Neher, Robert S. Arthur, Jeffrey D. Mirocha, Julie K. Lundquist, and Fotini K. Chow
Wind Energ. Sci., 7, 367–386, https://doi.org/10.5194/wes-7-367-2022, https://doi.org/10.5194/wes-7-367-2022, 2022
Short summary
Short summary
Wind turbine wake behavior in hilly terrain depends on various atmospheric conditions. We modeled a wind turbine located on top of a ridge in Portugal during typical nighttime and daytime atmospheric conditions and validated these model results with observational data. During nighttime conditions, the wake deflected downwards following the terrain. During daytime conditions, the wake deflected upwards. These results can provide insight into wind turbine siting and operation in hilly regions.
Sonja Krüger, Gerald Steinfeld, Martin Kraft, and Laura J. Lukassen
Wind Energ. Sci., 7, 323–344, https://doi.org/10.5194/wes-7-323-2022, https://doi.org/10.5194/wes-7-323-2022, 2022
Short summary
Short summary
Detailed numerical simulations of turbines in atmospheric conditions are challenging with regard to their computational demand. We coupled an atmospheric flow model and a turbine model in order to deliver extensive details about the flow and the turbine response within reasonable computational time. A comparison to measurement data was performed and showed a very good agreement. The efficiency of the tool enables applications such as load calculation in wind farms or during low-level-jet events.
Michael F. Howland, Aditya S. Ghate, Jesús Bas Quesada, Juan José Pena Martínez, Wei Zhong, Felipe Palou Larrañaga, Sanjiva K. Lele, and John O. Dabiri
Wind Energ. Sci., 7, 345–365, https://doi.org/10.5194/wes-7-345-2022, https://doi.org/10.5194/wes-7-345-2022, 2022
Short summary
Short summary
Wake steering control, in which turbines are intentionally misaligned with the incident wind, has demonstrated potential to increase wind farm energy. We investigate wake steering control methods in simulations of a wind farm operating in the terrestrial diurnal cycle. We develop a statistical wind direction forecast to improve wake steering in flows with time-varying states. Closed-loop wake steering control increases wind farm energy production, compared to baseline and open-loop control.
Paul Hulsman, Martin Wosnik, Vlaho Petrović, Michael Hölling, and Martin Kühn
Wind Energ. Sci., 7, 237–257, https://doi.org/10.5194/wes-7-237-2022, https://doi.org/10.5194/wes-7-237-2022, 2022
Short summary
Short summary
Due to the possibility of mapping the wake fast at multiple locations with the WindScanner, a thorough understanding of the development of the wake is acquired at different inflow conditions and operational conditions. The lidar velocity data and the energy dissipation rate compared favourably with hot-wire data from previous experiments, lending credibility to the measurement technique and methodology used here. This will aid the process to further improve existing wake models.
Ingrid Neunaber, Joachim Peinke, and Martin Obligado
Wind Energ. Sci., 7, 201–219, https://doi.org/10.5194/wes-7-201-2022, https://doi.org/10.5194/wes-7-201-2022, 2022
Short summary
Short summary
Wind turbines are often clustered within wind farms. A consequence is that some wind turbines may be exposed to the wakes of other turbines, which reduces their lifetime due to the wake turbulence. Knowledge of the wake is thus important, and we carried out wind tunnel experiments to investigate the wakes. We show how models that describe wakes of bluff bodies can help to improve the understanding of wind turbine wakes and wind turbine wake models, particularly by including a virtual origin.
Elena Cantero, Javier Sanz, Fernando Borbón, Daniel Paredes, and Almudena García
Wind Energ. Sci., 7, 221–235, https://doi.org/10.5194/wes-7-221-2022, https://doi.org/10.5194/wes-7-221-2022, 2022
Short summary
Short summary
The impact of atmospheric stability on wind energy is widely demonstrated, so we have to know how to characterise it.
This work based on a meteorological mast located in a complex terrain compares and evaluates different instrument set-ups and methodologies for stability characterisation. The methods are examined considering their theoretical background, implementation complexity, instrumentation requirements and practical use in connection with wind energy applications.
Peter Brugger, Corey Markfort, and Fernando Porté-Agel
Wind Energ. Sci., 7, 185–199, https://doi.org/10.5194/wes-7-185-2022, https://doi.org/10.5194/wes-7-185-2022, 2022
Short summary
Short summary
Wind turbines create a wake of reduced wind speeds downstream of the rotor. The wake does not necessarily have a straight, pencil-like shape but can meander similar to a smoke plume. We investigated this wake meandering and observed that the downstream transport velocity is slower than the wind speed contrary to previous assumptions and that the evolution of the atmospheric turbulence over time impacts wake meandering on distances typical for the turbine spacing in wind farms.
Ida Marie Solbrekke, Asgeir Sorteberg, and Hilde Haakenstad
Wind Energ. Sci., 6, 1501–1519, https://doi.org/10.5194/wes-6-1501-2021, https://doi.org/10.5194/wes-6-1501-2021, 2021
Short summary
Short summary
We validate new high-resolution data set (NORA3) for offshore wind power purposes for the North Sea and the Norwegian Sea. The aim of the validation is to ensure that NORA3 can act as a wind resource data set in the planning phase for future offshore wind power installations in the area of concern. The general conclusion of the validation is that NORA3 is well suited for wind power estimates but gives slightly conservative estimates of the offshore wind metrics.
Liang Dong, Wai Hou Lio, and Eric Simley
Wind Energ. Sci., 6, 1491–1500, https://doi.org/10.5194/wes-6-1491-2021, https://doi.org/10.5194/wes-6-1491-2021, 2021
Short summary
Short summary
This paper suggests that the impacts of different turbulence models should be considered as uncertainties while evaluating the benefits of lidar-assisted control (LAC) in wind turbine design. The value creation of LAC, evaluated using the Kaimal turbulence model, will be diminished if the Mann turbulence model is used instead. In particular, the difference in coherence is more significant for larger rotors.
Alexander Basse, Doron Callies, Anselm Grötzner, and Lukas Pauscher
Wind Energ. Sci., 6, 1473–1490, https://doi.org/10.5194/wes-6-1473-2021, https://doi.org/10.5194/wes-6-1473-2021, 2021
Short summary
Short summary
This study investigates systematic, seasonal biases in the long-term correction of short-term wind measurements (< 1 year). Two popular measure–correlate–predict (MCP) methods yield remarkably different results. Six reanalysis data sets serve as long-term data. Besides experimental results, theoretical findings are presented which link the mechanics of the methods and the properties of the reanalysis data sets to the observations. Finally, recommendations for wind park planners are derived.
Vasilis Pettas, Matthias Kretschmer, Andrew Clifton, and Po Wen Cheng
Wind Energ. Sci., 6, 1455–1472, https://doi.org/10.5194/wes-6-1455-2021, https://doi.org/10.5194/wes-6-1455-2021, 2021
Short summary
Short summary
This study aims to quantify the effect of inter-farm interactions based on long-term measurement data from the Alpha Ventus (AV) wind farm and the nearby FINO1 platform. AV was initially the only operating farm in the area, but in subsequent years several farms were built around it. This setup allows us to quantify the farm wake effects on the microclimate of AV and also on turbine loads and operational characteristics depending on the distance and size of the neighboring farms.
Rogier Floors, Merete Badger, Ib Troen, Kenneth Grogan, and Finn-Hendrik Permien
Wind Energ. Sci., 6, 1379–1400, https://doi.org/10.5194/wes-6-1379-2021, https://doi.org/10.5194/wes-6-1379-2021, 2021
Short summary
Short summary
Wind turbines are frequently placed in forests. We investigate the potential of using satellites to characterize the land surface for wind flow modelling. Maps of forest properties are generated from satellite data and converted to flow modelling maps. Validation is carried out at 10 sites. Using the novel satellite-based maps leads to lower errors of the power density than land cover databases, which demonstrates the value of using satellite-based land cover maps for flow modelling.
Christoffer Hallgren, Stefan Ivanell, Heiner Körnich, Ville Vakkari, and Erik Sahlée
Wind Energ. Sci., 6, 1205–1226, https://doi.org/10.5194/wes-6-1205-2021, https://doi.org/10.5194/wes-6-1205-2021, 2021
Short summary
Short summary
As wind power becomes more popular, there is a growing demand for accurate power production forecasts. In this paper we investigated different methods to improve wind power forecasts for an offshore location in the Baltic Sea, using both simple and more advanced techniques. The performance of the methods is evaluated for different weather conditions. Smoothing the forecast was found to be the best method in general, but we recommend selecting which method to use based on the forecasted weather.
Mark Kelly, Søren Juhl Andersen, and Ásta Hannesdóttir
Wind Energ. Sci., 6, 1227–1245, https://doi.org/10.5194/wes-6-1227-2021, https://doi.org/10.5194/wes-6-1227-2021, 2021
Short summary
Short summary
Via 11 years of measurements, we made a representative ensemble of wind ramps in terms of acceleration, mean speed, and shear. Constrained turbulence and large-eddy simulations were coupled to an aeroelastic model for each ensemble member. Ramp acceleration was found to dominate the maxima of thrust-associated loads, with a ramp-induced increase of 45 %–50 % plus ~ 3 % per 0.1 m/s2 of bulk ramp acceleration magnitude. The LES indicates that the ramps (and such loads) persist through the farm.
Davide Conti, Nikolay Dimitrov, Alfredo Peña, and Thomas Herges
Wind Energ. Sci., 6, 1117–1142, https://doi.org/10.5194/wes-6-1117-2021, https://doi.org/10.5194/wes-6-1117-2021, 2021
Short summary
Short summary
We carry out a probabilistic calibration of the Dynamic Wake Meandering (DWM) model using high-spatial- and high-temporal-resolution nacelle-based lidar measurements of the wake flow field. The experimental data were collected from the Scaled Wind Farm Technology (SWiFT) facility in Texas. The analysis includes the velocity deficit, wake-added turbulence, and wake meandering features under various inflow wind and atmospheric-stability conditions.
Tanvi Gupta and Somnath Baidya Roy
Wind Energ. Sci., 6, 1089–1106, https://doi.org/10.5194/wes-6-1089-2021, https://doi.org/10.5194/wes-6-1089-2021, 2021
Short summary
Short summary
Wind turbines extract momentum from atmospheric flow and convert that to electricity. This study explores recovery processes in wind farms that replenish the momentum so that wind farms can continue to function. Experiments with a numerical model show that momentum transport by turbulent eddies from above the wind turbines is the major contributor to recovery except for strong wind conditions and low wind turbine density, where horizontal advection can also play a major role.
Mithu Debnath, Paula Doubrawa, Mike Optis, Patrick Hawbecker, and Nicola Bodini
Wind Energ. Sci., 6, 1043–1059, https://doi.org/10.5194/wes-6-1043-2021, https://doi.org/10.5194/wes-6-1043-2021, 2021
Short summary
Short summary
As the offshore wind industry emerges on the US East Coast, a comprehensive understanding of the wind resource – particularly extreme events – is vital to the industry's success. We leverage a year of data of two floating lidars to quantify and characterize the frequent occurrence of high-wind-shear and low-level-jet events, both of which will have a considerable impact on turbine operation. We find that almost 100 independent long events occur throughout the year.
Jeanie A. Aird, Rebecca J. Barthelmie, Tristan J. Shepherd, and Sara C. Pryor
Wind Energ. Sci., 6, 1015–1030, https://doi.org/10.5194/wes-6-1015-2021, https://doi.org/10.5194/wes-6-1015-2021, 2021
Short summary
Short summary
Low-level jets (LLJs) are pronounced maxima in wind speed profiles affecting wind turbine performance and longevity. We present a climatology of LLJs over Iowa using output from the Weather Research and Forecasting (WRF) model and determine the rotor plane conditions when they occur. LLJ characteristics are highly sensitive to the identification criteria applied, and different (unique) LLJs are extracted with each criterion. LLJ characteristics also vary with different model output resolution.
Janna Kristina Seifert, Martin Kraft, Martin Kühn, and Laura J. Lukassen
Wind Energ. Sci., 6, 997–1014, https://doi.org/10.5194/wes-6-997-2021, https://doi.org/10.5194/wes-6-997-2021, 2021
Short summary
Short summary
Fluctuations in the power output of wind turbines are one of the major challenges in the integration and utilisation of wind energy. By analysing the power output fluctuations of wind turbine pairs in an offshore wind farm, we show that their correlation depends on their location within the wind farm and their inflow. The main outcome is that these correlation dependencies can be characterised by statistics of the power output of the wind turbines and sorted by a clustering algorithm.
Mike Optis, Nicola Bodini, Mithu Debnath, and Paula Doubrawa
Wind Energ. Sci., 6, 935–948, https://doi.org/10.5194/wes-6-935-2021, https://doi.org/10.5194/wes-6-935-2021, 2021
Short summary
Short summary
Offshore wind turbines are huge, with rotor blades soon to extend up to nearly 300 m. Accurate modeling of winds across these heights is crucial for accurate estimates of energy production. However, we lack sufficient observations at these heights but have plenty of near-surface observations. Here we show that a basic machine-learning model can provide very accurate estimates of winds in this area, and much better than conventional techniques.
Davide Conti, Vasilis Pettas, Nikolay Dimitrov, and Alfredo Peña
Wind Energ. Sci., 6, 841–866, https://doi.org/10.5194/wes-6-841-2021, https://doi.org/10.5194/wes-6-841-2021, 2021
Short summary
Short summary
We define two lidar-based procedures for improving the accuracy of wind turbine load assessment under wake conditions. The first approach incorporates lidar observations directly into turbulence fields serving as inputs for aeroelastic simulations; the second approach imposes lidar-fitted wake deficit time series on the turbulence fields. The uncertainty in the lidar-based power and load predictions is quantified for a variety of scanning configurations and atmosphere turbulence conditions.
Maarten Paul van der Laan, Mark Kelly, and Mads Baungaard
Wind Energ. Sci., 6, 777–790, https://doi.org/10.5194/wes-6-777-2021, https://doi.org/10.5194/wes-6-777-2021, 2021
Short summary
Short summary
Wind farms operate in the atmospheric boundary layer, and their performance is strongly dependent on the atmospheric conditions. We propose a simple model of the atmospheric boundary layer that can be used as an inflow model for wind farm simulations for isolating a number of atmospheric effects – namely, the change in wind direction with height and atmospheric boundary layer depth. In addition, the simple model is shown to be consistent with two similarity theories.
Alayna Farrell, Jennifer King, Caroline Draxl, Rafael Mudafort, Nicholas Hamilton, Christopher J. Bay, Paul Fleming, and Eric Simley
Wind Energ. Sci., 6, 737–758, https://doi.org/10.5194/wes-6-737-2021, https://doi.org/10.5194/wes-6-737-2021, 2021
Short summary
Short summary
Most current wind turbine wake models struggle to accurately simulate spatially variant wind conditions at a low computational cost. In this paper, we present an adaptation of NREL's FLOw Redirection and Induction in Steady State (FLORIS) wake model, which calculates wake losses in a heterogeneous flow field using local weather measurement inputs. Two validation studies are presented where the adapted model consistently outperforms previous versions of FLORIS that simulated uniform flow only.
Carlo Cossu
Wind Energ. Sci., 6, 663–675, https://doi.org/10.5194/wes-6-663-2021, https://doi.org/10.5194/wes-6-663-2021, 2021
Short summary
Short summary
We deal with wake redirection, which is a promising approach designed to mitigate turbine–wake interactions which have a negative impact on the performance and lifetime of wind farms. We show that substantial power gains can be obtained by tilting the rotors of spanwise-periodic wind-turbine arrays in the atmospheric boundary layer (ABL). Optimal relative rotor sizes and spanwise spacings exist, which maximize the global power extracted from the wind.
Alfredo Peña, Branko Kosović, and Jeffrey D. Mirocha
Wind Energ. Sci., 6, 645–661, https://doi.org/10.5194/wes-6-645-2021, https://doi.org/10.5194/wes-6-645-2021, 2021
Short summary
Short summary
We investigate the ability of a community-open weather model to simulate the turbulent atmosphere by comparison with measurements from a 250 m mast at a flat site in Denmark. We found that within three main atmospheric stability regimes, idealized simulations reproduce closely the characteristics of the observations with regards to the mean wind, direction, turbulent fluxes, and turbulence spectra. Our work provides foundation for the use of the weather model in multiscale real-time simulations.
Christian Grinderslev, Niels Nørmark Sørensen, Sergio González Horcas, Niels Troldborg, and Frederik Zahle
Wind Energ. Sci., 6, 627–643, https://doi.org/10.5194/wes-6-627-2021, https://doi.org/10.5194/wes-6-627-2021, 2021
Short summary
Short summary
This study investigates aero-elasticity of wind turbines present in the turbulent and chaotic wind flow of the lower atmosphere, using fluid–structure interaction simulations. This method combines structural response computations with high-fidelity modeling of the turbulent wind flow, using a novel turbulence model which combines the capabilities of large-eddy simulations for atmospheric flows with improved delayed detached eddy simulations for the separated flow near the rotor.
Jörge Schneemann, Frauke Theuer, Andreas Rott, Martin Dörenkämper, and Martin Kühn
Wind Energ. Sci., 6, 521–538, https://doi.org/10.5194/wes-6-521-2021, https://doi.org/10.5194/wes-6-521-2021, 2021
Short summary
Short summary
A wind farm can reduce the wind speed in front of it just by its presence and thus also slightly impact the available power. In our study we investigate this so-called global-blockage effect, measuring the inflow of a large offshore wind farm with a laser-based remote sensing method up to several kilometres in front of the farm. Our results show global blockage under a certain atmospheric condition and operational state of the wind farm; during other conditions it is not visible in our data.
Julia Gottschall and Martin Dörenkämper
Wind Energ. Sci., 6, 505–520, https://doi.org/10.5194/wes-6-505-2021, https://doi.org/10.5194/wes-6-505-2021, 2021
Kamran Shirzadeh, Horia Hangan, Curran Crawford, and Pooyan Hashemi Tari
Wind Energ. Sci., 6, 477–489, https://doi.org/10.5194/wes-6-477-2021, https://doi.org/10.5194/wes-6-477-2021, 2021
Short summary
Short summary
Wind energy systems work coherently in atmospheric flows which are gusty. This causes highly variable power productions and high fatigue loads on the system, which together hold back further growth of the wind energy market. This study demonstrates an alternative experimental procedure to investigate some extreme wind condition effects on wind turbines based on the IEC standard. This experiment can be improved upon and used to develop new control concepts, mitigating the effect of gusts.
Inga Reinwardt, Levin Schilling, Dirk Steudel, Nikolay Dimitrov, Peter Dalhoff, and Michael Breuer
Wind Energ. Sci., 6, 441–460, https://doi.org/10.5194/wes-6-441-2021, https://doi.org/10.5194/wes-6-441-2021, 2021
Short summary
Short summary
This analysis validates the DWM model based on loads and power production measured at an onshore wind farm. Special focus is given to the performance of a version of the DWM model that was previously recalibrated with a lidar system at the site. The results of the recalibrated wake model agree very well with the measurements. Furthermore, lidar measurements of the wind speed deficit and the wake meandering are incorporated in the DWM model definition in order to decrease the uncertainties.
Christian Ingenhorst, Georg Jacobs, Laura Stößel, Ralf Schelenz, and Björn Juretzki
Wind Energ. Sci., 6, 427–440, https://doi.org/10.5194/wes-6-427-2021, https://doi.org/10.5194/wes-6-427-2021, 2021
Short summary
Short summary
Wind farm sites in complex terrain are subject to local wind phenomena, which are difficult to quantify but have a huge impact on a wind turbine's annual energy production. Therefore, a wind sensor was applied on an unmanned aerial vehicle and validated against stationary wind sensors with good agreement. A measurement over complex terrain showed local deviations from the mean wind speed of approx. ± 30 %, indicating the importance of an extensive site evaluation to reduce investment risk.
Ervin Bossanyi and Renzo Ruisi
Wind Energ. Sci., 6, 389–408, https://doi.org/10.5194/wes-6-389-2021, https://doi.org/10.5194/wes-6-389-2021, 2021
Short summary
Short summary
This paper describes the design and field testing of a controller for reducing wake interactions on a wind farm. Reducing the power of some turbines weakens their wakes, allowing other turbines to produce more power so that the total wind farm power may increase. There have been doubts that this is feasible, but these field tests on a full-scale wind farm indicate that this goal has been achieved, also providing convincing validation of the model used for designing the controller.
Carlo Cossu
Wind Energ. Sci., 6, 377–388, https://doi.org/10.5194/wes-6-377-2021, https://doi.org/10.5194/wes-6-377-2021, 2021
Short summary
Short summary
In this study wake redirection and axial-induction control are combined to mitigate turbine–wake interactions, which have a negative impact on the performance and lifetime of wind farms. The results confirm that substantial power gains are obtained when overinduction is combined with tilt control. More importantly, the approach is extended to the case of yaw control, showing that large power gain enhancements are obtained by means of static overinductive yaw control.
Joseph C. Y. Lee and M. Jason Fields
Wind Energ. Sci., 6, 311–365, https://doi.org/10.5194/wes-6-311-2021, https://doi.org/10.5194/wes-6-311-2021, 2021
Short summary
Short summary
This review paper evaluates the energy prediction bias in the wind resource assessment process, and the overprediction bias is decreasing over time. We examine the estimated and observed losses and uncertainties in energy production from the literature, according to the proposed framework in the International Electrotechnical Commission 61400-15 standard. The considerable uncertainties call for further improvements in the prediction methodologies and more observations for validation.
Daniel Vassallo, Raghavendra Krishnamurthy, and Harindra J. S. Fernando
Wind Energ. Sci., 6, 295–309, https://doi.org/10.5194/wes-6-295-2021, https://doi.org/10.5194/wes-6-295-2021, 2021
Short summary
Short summary
Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting and is especially useful when combined with other forecasting techniques. This study utilizes a popular machine learning algorithm, random forest, in an attempt to predict the forecasting error of a statistical forecasting model. Various atmospheric characteristics are used as random forest inputs in an effort to discern the most useful atmospheric information for this purpose.
Luca Lanzilao and Johan Meyers
Wind Energ. Sci., 6, 247–271, https://doi.org/10.5194/wes-6-247-2021, https://doi.org/10.5194/wes-6-247-2021, 2021
Short summary
Short summary
This research paper investigates the potential of thrust set-point optimization in large wind farms for mitigating gravity-wave-induced blockage effects for the first time, with the aim of increasing the wind-farm energy extraction. The optimization tool is applied to almost 2000 different atmospheric states. Overall, power gains above 4 % are observed for 77 % of the cases.
Bart M. Doekemeijer, Stefan Kern, Sivateja Maturu, Stoyan Kanev, Bastian Salbert, Johannes Schreiber, Filippo Campagnolo, Carlo L. Bottasso, Simone Schuler, Friedrich Wilts, Thomas Neumann, Giancarlo Potenza, Fabio Calabretta, Federico Fioretti, and Jan-Willem van Wingerden
Wind Energ. Sci., 6, 159–176, https://doi.org/10.5194/wes-6-159-2021, https://doi.org/10.5194/wes-6-159-2021, 2021
Short summary
Short summary
This article presents the results of a field experiment investigating wake steering on an onshore wind farm. The measurements show that wake steering leads to increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions. The results suggest that further research is necessary before wake steering will consistently lead to energy gains in wind farms.
Aniruddha Deepak Paranjape, Anhad Singh Bajaj, Shaheen Thimmaiah Palanganda, Radha Parikh, Raahil Nayak, and Jayakrishnan Radhakrishnan
Wind Energ. Sci., 6, 149–157, https://doi.org/10.5194/wes-6-149-2021, https://doi.org/10.5194/wes-6-149-2021, 2021
Short summary
Short summary
This project is a comparative study that takes into consideration various airfoils from the Selig, NACA, and Eppler families and models them as diffusers of the wind turbine. The efficiency of the diffuser-augmented wind turbine can be enhanced by optimizing the geometry of the diffuser shape. Their subsequent performance trends were then analyzed, and the lower-performing airfoils were systematically eliminated to leave us with an optimum design.
Cited articles
Alemi Ardakani, H. and Bridges, T. J.: Review of the 3-2-1 Euler Angles: a
yaw–pitch–roll sequence, Tech. rep., http://personal.maths.surrey.ac.uk/T.Bridges/SLOSH/3-2-1-Eulerangles.pdf (last access: 19 May 2022), 2010. a
Allaerts, D. and Meyers, J.: Large eddy simulation of a large wind-turbine
array in a conventionally neutral atmospheric boundary layer, Phys. Fluids, 27, 065108, https://doi.org/10.1063/1.4922339, 2015. a
Allaerts, D. and Meyers, J.: Boundary-layer development and gravity waves in
conventionally neutral wind farms, J. Fluid Mech., 814, 95–130, 2017. a
Andersson, J. A. E., Gillis, J., Horn, G., Rawlings, J. B., and Diehl, M.: CasADi: a software framework for nonlinear optimization and optimal control, Mathematical Programming Computation, Springer, https://doi.org/10.1007/s12532-018-0139-4, 2018. a
Archer, C. L.: An Introduction to Meteorology for Airborne Wind Energy, in:
Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 81–94, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a, b
Argatov, I. and Silvennoinen, R.: Efficiency of Traction Power Conversion Based on Crosswind Motion, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer-Verlag, Berlin, Heidelberg, 65–79,
https://doi.org/10.1007/978-3-642-39965-7_4, 2013. a
Barthelmie, R. J., Pryor, S. C., Frandsen, S. T., Hansen, K. S., Schepers, J. G., Rados, K., Schlez, W., Neubert, A., Jensen, L. E., and Neckelmann, S.:
Quantifying the Impact of Wind Turbine Wakes on Power Output at Offshore Wind
Farms, J. Atmos. Ocean. Tech., 27, 1302–1317, https://doi.org/10.1175/2010JTECHA1398.1, 2010. a
Bauer, F., Kennel, R. M., Hackl, C. M., Campagnolo, F., Patt, M., and Schmehl, R.: Drag power kite with very high lift coefficient, Renew. Energy, 118, 290–305, https://doi.org/10.1016/j.renene.2017.10.073, 2018. a
Bulder, B., Bedon, G., and Bot, E.: Optimal wind farm power density analysis
for future offshore wind farms, Tech. rep., http://resolver.tudelft.nl/uuid:dfe0ce2f-04fb-4db2-80db-52bc02cfb515 (last access: 19 May 2022), 2018. 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, b, c, d
Cherubini, A., Papini, A., Vertechy, R., and Fontana, M.: Airborne Wind Energy Systems: A review of the technologies, Renew. Sustain. Energ. Rev., 51, 1461–1476, https://doi.org/10.1016/j.rser.2015.07.053, 2015. a
De Lellis, M., Reginatto, R., Saraiva, R., and Trofino, A.: The Betz limit
applied to Airborne Wind Energy, Renew. Energy, 127, 32–40,
https://doi.org/10.1016/j.renene.2018.04.034, 2018. a
De Schutter, J., Bronnenmeyer, T., Paelinck, R., Diehl, M., and Leuthold, R.:
Optimal control of stacked multi-kite systems for utility-scale airborne wind
energy, in: 2019 IEEE 58th Conference on Decision and Control (CDC), https://doi.org/10.1109/CDC40024.2019.9030026, 2019. a, b
Diehl, M.: Airborne wind energy: Basic concepts and physical foundations, in:
Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 3–22, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a, b
Dykes, K.: Optimization of Wind Farm Design for Objectives Beyond LCOE, vol. 1618, IOP Publishing, https://doi.org/10.1088/1742-6596/1618/4/042039, 2020. a
Gaunaa, M., Forsting, A. M., and Trevisi, F.: An engineering model for the
induction of crosswind kite power systems, J. Phys.: Conf. Ser., 1618, 032010, https://doi.org/10.1088/1742-6596/1618/3/032010, 2020. a
Gohl, F. and Luchsinger, R. H.: Simulation Based Wing Design for Kite Power,
in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 325–338, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a
Gros, S. and Diehl, M.: Modeling of airborne wind energy systems in natural
coordinates, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and
Schmehl, R., Springer-Verlag, Berlin, Heidelberg, 181–203, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a, b, c
Gros, S. and Zanon, M.: Numerical Optimal Control With Periodicity Constraints in the Presence of Invariants, IEEE T. Automat. Control, 63, 2818–2832, https://doi.org/10.1109/TAC.2017.2772039, 2018. a
Gros, S., Zanon, M., and Diehl, M.: Control of Airborne Wind Energy systems
based on Nonlinear Model Predictive Control and Moving Horizon Estimation,
in: 2013 European Control Conference (ECC), 1017–1022, https://doi.org/10.23919/ECC.2013.6669713, 2013. a
Haas, T. and Meyers, J.: Comparison study between wind turbine and power kite
wakes, J. Phys.: Conf. Ser., 854, 012019, https://doi.org/10.1088/1742-6596/854/1/012019, 2017. a, b
Haas, T. and Meyers, J.: AWESCO Wind Field Datasets, Zenodo [data set],
https://doi.org/10.5281/zenodo.1418676, 2019. a, b
Haas, T. and Meyers, J.: Large-eddy simulation of airborne wind energy farms:
AWES virtual flight data, Zenodo [data set], https://doi.org/10.5281/zenodo.5705563, 2021. a
Haas, T., De Schutter, J., Diehl, M., and Meyers, J.: Wake characteristics of
pumping mode airborne wind energy systems, J. Phys.: Conf. Ser., 1256, 012016, https://doi.org/10.1088/1742-6596/1256/1/012016, 2019b. a
Horn, G., Gros, S., and Diehl, M.: Numerical trajectory optimization for
airborne wind energy systems described by high fidelity aircraft models, in:
Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R.,
Springer-Verlag, Berlin, Heidelberg, 205–218, https://doi.org/10.1007/978-3-642-39965-7_5, 2013. a
Houska, B. and Diehl, M.: Optimal control for power generating kites, in: 2007 European Control Conference (ECC), 3560–3567,
https://doi.org/10.23919/ECC.2007.7068861, 2007. a
HSL: A collection of Fortran codes for large scale scientific computation, http://www.hsl.rl.ac.uk (last access: 19 May 2022), 2011. a
Jeong, J. and Hussain, F.: On the identification of a vortex, J. Fluid Mech., 285, 69–94, https://doi.org/10.1017/S0022112095000462, 1995. a
Jimenez, A., Crespo, A., Migoya, E., and Garcia, J.: Advances in large-eddy
simulation of a wind turbine wake, J. Phys.: Conf. Ser., 75, 012041, https://doi.org/10.1088/1742-6596/75/1/012041, 2007. a
Kaufman-Martin, S., Naclerio, N., May, P., and Luzzatto-Fegiz, P.: An
entrainment-based model for annular wakes, with applications to airborne wind
energy, Wind Energy, 25, 419–431, https://doi.org/10.1002/we.2679, 2022. a
Kheiri, M., Bourgault, F., Saberi Nasrabad, V., and Victor, S.: On the
aerodynamic performance of crosswind kite power systems, J. Wind Eng. Indust. Aerodyn., 181, 1–13, https://doi.org/10.1016/j.jweia.2018.08.006, 2018. a, b
Leuthold, R., Gros, S., and Diehl, M.: Induction in Optimal Control of
Multiple-Kite Airborne Wind Energy Systems, in: Proceedings of 20th IFAC
World Congress, Toulouse, France, https://doi.org/10.1016/j.ifacol.2017.08.026, 2017. a, b
Leuthold, R., De Schutter, J., Malz, E., Licitra, G., Gros, S., and Diehl, M.: Operational Regions of a Multi-Kite AWE System, in: 2018 European Control Conference (ECC), 52–57, https://doi.org/10.23919/ECC.2018.8550199, 2018. a
Leuthold, R., Crawford, C., Gros, S., and Diehl, M.: Engineering Wake Induction Model For Axisymmetric Multi-Kite Systems, J. Phys.: Conf. Ser., 1256, 012009, https://doi.org/10.1088/1742-6596/1256/1/012009, 2019. a
Licitra, G., Sieberling, S., Engelen, S., Williams, P., Ruiterkamp, R., and
Diehl, M.: Optimal control for minimizing power consumption during holding
patterns for airborne wind energy pumping system, in: 2016 European Control Conference (ECC), 1574–1579, https://doi.org/10.1109/ECC.2016.7810515, 2016. a
Licitra, G., Williams, P., Gillis, J., Ghandchi, S., Sieberling, S.,
Ruiterkamp, R., and Diehl, M.: Aerodynamic Parameter Identification for an
Airborne Wind Energy Pumping System, in: 20th IFAC World Congress, IFAC-PapersOnLine, 50, 11951–11958, https://doi.org/10.1016/j.ifacol.2017.08.1038, 2017. a
Licitra, G., Koenemann, J., Bürger, A., Williams, P., Ruiterkamp, R., and
Diehl, M.: Performance assessment of a rigid wing Airborne Wind Energy pumping system, Energy, 173, 569–585, https://doi.org/10.1016/j.energy.2019.02.064, 2019. a
Loyd, M. L.: Crosswind Kite Power, J. Energy, 4, 106–111,
https://doi.org/10.2514/3.48021, 1980. a
Malz, E., Zanon, M., and Gros, S.: A Quantification of the Performance Loss of Power Averaging in Airborne Wind Energy Farms, in: 2018 European Control Conference (ECC), https://doi.org/10.23919/ECC.2018.8550357, 2018. a
Malz, E., Koenemann, J., Sieberling, S., and Gros, S.: A reference model for
airborne wind energy systems for optimization and control, Renew. Energy, 140, 1004–1011, https://doi.org/10.1016/j.renene.2019.03.111, 2019. a, b, c, d
Martinez-Tossas, L. A., Churchfield, M. J., Yilmaz, A. E., Sarlak, H., Johnson, P. L., Sorensen, J. N., Meyers, J., and Meneveau, C.: Comparison of four large-eddy simulation research codes and effects of model coefficient and inflow turbulence in actuator-line-based wind turbine modeling, J. Renew. Sustain. Energ., 10, 033301, https://doi.org/10.1063/1.5004710, 2018. a
Mason, P. J. and Thomson, D. J.: Stochastic backscatter in large-eddy
simulations of boundary layers, J. Fluid Mechan., 242, 51–78,
https://doi.org/10.1017/S0022112092002271, 1992. a
Munters, W., Meneveau, C., and Meyers, J.: Turbulent Inflow Precursor Method
with Time-Varying Direction for Large-Eddy Simulations and Applications to
Wind Farms, Bound.-Lay. Meteorol., 159, 305–328, https://doi.org/10.1007/s10546-016-0127-z, 2016. a, b
Pope, S. B.: Turbulent Flows, Cambridge University Press,
https://doi.org/10.1017/CBO9780511840531, 2000. a
Porte-Agel, F., Bastankhah, M., and Shamsoddin, S.: Wind-turbine and wind-farm flows: a review, Bound.-Lay. Meteorol., 174, 1–59, 2020. a
Rapp, S., Schmehl, R., Oland, E., and Haas, T.: Cascaded Pumping Cycle Control for Rigid Wing Airborne Wind Energy Systems, J. Guidance Control
Dynam., 42, 2456–2473, https://doi.org/10.2514/1.G004246, 2019. a
Read, R.: Kite Networks for HarvestingWind Energy, in: Airborne Wind Energy:
Advances in Technology Development and Research, edited by: Schmehl, R.,
Springer, Singapore, 515–517, https://doi.org/10.1007/978-981-10-1947-0, 2018. a
Roque, L. A., Paiva, L. T., Fernandes, M. C., Fontes, D. B., and Fontes, F. A.: Layout optimization of an airborne wind energy farm for maximum power
generation, in: the 6th International Conference on Energy and Environment Research – Energy and environment: challenges towards circular economy, Energy Rep., 6, 165–171, https://doi.org/10.1016/j.egyr.2019.08.037, 2020. a
Schelbergen, M. and Schmehl, R.: Validation of the quasi-steady performance
model for pumping airborne wind energy systems, J. Phys.: Conf. Ser., 1618, 32003, https://doi.org/10.1088/1742-6596/1618/3/032003, 2020. a
Sorensen, J. N. and Shen, W. Z.: Numerical Modeling of Wind Turbine Wakes, J. Fluids Eng., 124, 393, https://doi.org/10.1115/1.1471361, 2002. a
Sternberg, J., Goit, J., Gros, S., Meyers, J., and Diehl, M.: Robust and
stable periodic flight of power generating kite systems in a turbulent wind
flow field, IFAC Proceedings Volumes (IFAC-PapersOnline), 45, 140–145,
https://doi.org/10.3182/20120913-4-IT-4027.00009, 2012. 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
Storey, R. C., Norris, S. E., and Cater, J. E.: An actuator sector method for
efficient transient wind turbine simulation, Wind Energy, 18, 699–711,
https://doi.org/10.1002/we.1722, 2015. a, b
Trevisi, F., Gaunaa, M., and Mcwilliam, M.: The Influence of Tether Sag on
Airborne Wind Energy Generation, J. Phys.: Conf. Ser., 1618, 032006, https://doi.org/10.1088/1742-6596/1618/3/032006, 2020. a
Troen, I. and Lundtang Petersen, E.: European Wind Atlas, Tech. rep., Risø National Laboratory, ISBN 87-550-1482-8, 1989. a
Troldborg, N., Sorensen, J. N., and Mikkelsen, R.: Numerical simulations of
wake characteristics of a wind turbine in uniform inflow, Wind Energy, 13,
86–99, https://doi.org/10.1002/we.345, 2010.
a, b, c
Vitsas, A. and Meyers, J.: Multiscale aeroelastic simulations of large wind
farms in the atmospheric boundary layer, J. Phys.: Conf. Ser., 753, 082020, https://doi.org/10.1088/1742-6596/753/8/082020, 2016. a
Wächter, A.: An Interior Point Algorithm for Large-Scale Nonlinear Optimization with Applications in Process Engineering, PhD thesis, Carnegie Mellon University, http://users.iems.northwestern.edu/~andreasw/pubs/waechter_thesis.pdf (last access: 19 May 2022), 2002. a
XFLR5:
XFLR5: XFLR5 is an analysis tool for airfoils, wings and
planes, https://sourceforge.net/projects/xflr5/ (last access: 19 May 2022), 2021. a
XFOIL:
XFOIL: Subsonic Airfoil Development System,
https://web.mit.edu/drela/Public/web/xfoil/ (last access: 19 May 2022), 2021. a
Zanon, M.: Efficient Nonlinear Model Predictive Control Formulations for
Economic Objectives with Aerospace and Automotive Applications, https://limo.libis.be/primo-explore/fulldisplay?docid=LIRIAS1693005&context=L&vid=Lirias&search_scope=Lirias&tab=default_tab&fromSitemap=1 (last access: 19 May 2022), 2015. a
Zanon, M., Gros, S., Andersson, J., and Diehl, M.: Airborne Wind Energy Based
on Dual Airfoils, IEEE T. Control Syst. Technol., 21, 1215–1222, https://doi.org/10.1109/TCST.2013.2257781, 2013a. a
Zanon, M., Gros, S., and Diehl, M.: Model predictive control of rigid-airfoil
airborne wind energy systems, in: Airborne Wind Energy, edited by: Ahrens, U., Diehl, M., and Schmehl, R., Springer-Verlag, Berlin, Heidelberg,
219–234, https://doi.org/10.1007/978-3-642-39965-7_5, 2013b. a, b, c
Short summary
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight simulator. The virtual flight simulator combines numerical techniques from flow simulation and kite control. Using advanced control algorithms, the systems can operate efficiently in the park despite turbulent flow conditions. For the three configurations considered in the study, we observe significant wake effects, reducing the power yield of the parks.
In this work, we study parks of large-scale airborne wind energy systems using a virtual flight...
Altmetrics
Final-revised paper
Preprint