Articles | Volume 6, issue 2
https://doi.org/10.5194/wes-6-311-2021
© Author(s) 2021. 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-6-311-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An overview of wind-energy-production prediction bias, losses, and uncertainties
Joseph C. Y. Lee
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
M. Jason Fields
CORRESPONDING AUTHOR
National Wind Technology Center, National Renewable Energy Laboratory,
Golden, CO 80401, USA
Related authors
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
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This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Mike Optis, Jordan Perr-Sauer, Caleb Philips, Anna E. Craig, Joseph C. Y. Lee, Travis Kemper, Shuangwen Sheng, Eric Simley, Lindy Williams, Monte Lunacek, John Meissner, and M. Jason Fields
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-12, https://doi.org/10.5194/wes-2019-12, 2019
Preprint withdrawn
Short summary
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As global wind capacity continues to grow, the need for accurate operational analyses of a rapidly growing fleet of wind power plants has increased in proportion. To address this need, the National Renewable Energy Laboratory has released OpenOA, an open-source codebase for operational analysis of wind farms. It is envisioned that OpenOA will evolve into a widely used codebase supported by a large group of global wind energy experts. This paper provides a summary of OpenOA.
Joseph C. Y. Lee, M. Jason Fields, and Julie K. Lundquist
Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018, https://doi.org/10.5194/wes-3-845-2018, 2018
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To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
Andrew Clifton, Sarah Barber, Andrew Bray, Peter Enevoldsen, Jason Fields, Anna Maria Sempreviva, Lindy Williams, Julian Quick, Mike Purdue, Philip Totaro, and Yu Ding
Wind Energ. Sci., 8, 947–974, https://doi.org/10.5194/wes-8-947-2023, https://doi.org/10.5194/wes-8-947-2023, 2023
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Wind energy creates huge amounts of data, which can be used to improve plant design, raise efficiency, reduce operating costs, and ease integration. These all contribute to cheaper and more predictable energy from wind. But realising the value of data requires a digital transformation that brings
grand challengesaround data, culture, and coopetition. This paper describes how the wind energy industry could work with R&D organisations, funding agencies, and others to overcome them.
Joseph C. Y. Lee, Peter Stuart, Andrew Clifton, M. Jason Fields, Jordan Perr-Sauer, Lindy Williams, Lee Cameron, Taylor Geer, and Paul Housley
Wind Energ. Sci., 5, 199–223, https://doi.org/10.5194/wes-5-199-2020, https://doi.org/10.5194/wes-5-199-2020, 2020
Short summary
Short summary
This work summarizes the results of the intelligence-sharing initiative of the Power Curve Working Group. Participants in this share exercise applied a handful of selected power curve modeling correction methods on their power performance test data, and they submitted the results for the coauthors to analyze. In this paper, we describe the share exercise, explain the analysis methodologies, and perform statistical tests to evaluate the correction methods in various inflow conditions.
Mike Optis, Jordan Perr-Sauer, Caleb Philips, Anna E. Craig, Joseph C. Y. Lee, Travis Kemper, Shuangwen Sheng, Eric Simley, Lindy Williams, Monte Lunacek, John Meissner, and M. Jason Fields
Wind Energ. Sci. Discuss., https://doi.org/10.5194/wes-2019-12, https://doi.org/10.5194/wes-2019-12, 2019
Preprint withdrawn
Short summary
Short summary
As global wind capacity continues to grow, the need for accurate operational analyses of a rapidly growing fleet of wind power plants has increased in proportion. To address this need, the National Renewable Energy Laboratory has released OpenOA, an open-source codebase for operational analysis of wind farms. It is envisioned that OpenOA will evolve into a widely used codebase supported by a large group of global wind energy experts. This paper provides a summary of OpenOA.
Joseph C. Y. Lee, M. Jason Fields, and Julie K. Lundquist
Wind Energ. Sci., 3, 845–868, https://doi.org/10.5194/wes-3-845-2018, https://doi.org/10.5194/wes-3-845-2018, 2018
Short summary
Short summary
To find the ideal way to quantify long-term wind-speed variability, we compare 27 metrics using 37 years of wind and energy data. We conclude that the robust coefficient of variation can effectively assess and correlate wind-speed and energy-production variabilities. We derive adequate results via monthly mean data, whereas uncertainty arises in interannual variability calculations. We find that reliable estimates of wind-speed variability require 10 ± 3 years of monthly mean wind data.
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
Large-eddy simulation of airborne wind energy farms
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
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
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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
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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.
Thomas Haas, Jochem De Schutter, Moritz Diehl, and Johan Meyers
Wind Energ. Sci., 7, 1093–1135, https://doi.org/10.5194/wes-7-1093-2022, https://doi.org/10.5194/wes-7-1093-2022, 2022
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
Abascal, A., Herrero, M., Torrijos, M., Dumont, J., Álvarez, M., and
Casso, P.: An approach for estimating energy losses due to ice in
pre-construction energy assessments, in: WindEurope 2019, WindEurope, Bilbao,
Spain, 2019.
Abiven, C., Brady, O., and Triki, I.: Mesoscale and CFD Coupling for Wind
Resource Assessment, in: AWEA Wind Resource and Project Energy Assessment
Workshop 2013, AWEA, Las Vegas, NV, 2013.
Abiven, C., Parisse, A., Watson, G., and Brady, O.: CFD Wake Modeling: Where
Do We Stand?, in: AWEA Wind Resource and Project Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014.
Albers, A., Klug, H., and Westermann, D.: Outdoor Comparison of Cup Anemometers, in: German wind energy conference, DEWEK 2000, Wilhelmshaven, Germany, p. 5, 2000.
Albers, A., Franke, K., Wagner, R., Courtney, M., and Boquet, M.:
Ground-based remote sensor uncertainty – a case study for a wind lidar,
available at:
https://www.researchgate.net/publication/267780849_Ground-based_remote_sensor_uncertainty_-_a_case_study_for_a_wind_lidar (last access: 31 October 2020), 2013.
Anderson, M.: Seasonality, Stability and MCP, in: AWEA Wind Resource and Project Energy Assessment Workshop 2008, AWEA, Portland, OR, 2008.
Apple, J.: Wind Farm Power Curves: Guidelines for New Applications, in: AWEA
Wind Resource and Project Energy Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
AWS Truepower: Closing The Gap On Plant Underperformance: A Review and
Calibration of AWS Truepower's Energy Estimation Methods, AWS Truepower, LLC, Albany, NY, 2009.
AWS Truepower: AWS Truepower Loss and Uncertainty Methods, Albany, NY,
available at:
https://www.awstruepower.com/assets/AWS-Truepower-Loss-and-Uncertainty-Memorandum-5-Jun-2014.pdf
(last access: 29 August 2017), 2014.
Balfrey, D.: Data Processing, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2010, AWEA, Oklahoma City, OK, 2010.
Barthelmie, R. J., Murray, F., and Pryor, S. C.: The economic benefit of
short-term forecasting for wind energy in the UK electricity market, Energy
Policy, 36, 1687–1696, https://doi.org/10.1016/J.ENPOL.2008.01.027, 2008.
Baughman, E.: Error Distributions, Tails, and Outliers, in: AWEA Wind Resource and Project Energy Assessment Workshop 2016, AWEA, Minneapolis, MN, 2016.
Beaucage, P., Kramak, B., Robinson, N., and Brower, M. C.: Modeling the
dynamic behavior of wind farm power generation: Building upon SCADA system
analysis, in: AWEA Wind Resource and Project Energy Assessment Workshop 2016,
AWEA, Minneapolis, MN, 2016.
Bernadett, D., Brower, M., Van Kempen, S., Wilson, W., and Kramak, B.: 2012 Backcast Study: Verifying AWS Truepower's Energy and Uncertainty Estimates, AWS Truepower, LLC, Albany, NY, 2012.
Bernadett, D., Brower, M., and Ziesler, C.: Loss Adjustment Refinement, in:
AWEA Wind Resource and Project Energy Assessment Workshop 2016, AWEA, Minneapolis, MN, 2016.
Bird, L., Cochran, J., and Wang, X.: Wind and Solar Energy Curtailment:
Experience and Practices in the United States, NREL/TP-6A20-60983, National Renewable Energy Laboratory, Golden, CO, 2014.
Bleeg, J.: Accounting for Blockage Effects in Energy Production Assessments,
in: AWEA Wind Resource and Project Energy Assessment Workshop 2018, AWEA,
Austin, TX, 2018.
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.
Breakey, M.: An Armchair Meteorological Campaign Manager: A Retrospective
Analysis, in: AWEA Wind Resource and Project Energy Assessment Workshop 2019,
AWEA, Renton, WA, 2019.
Brower, M.: What do you mean you're not sure? Concepts in uncertainty and risk management, in: AWEA Wind Resource and Project Energy Assessment Workshop 2011, AWEA, Seattle, WA, 2011.
Brower, M. C.: Wind resource assessment: a practical guide to developing a
wind project, Wiley, Hoboken, NJ, 2012.
Brower, M. C.: Measuring and Managing Uncertainty, in: AWEA Wind Resource and
Project Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014.
Brower, M. C.: State of the P50, in: AWEA WINDPOWER 2017, AWEA, Anaheim, CA, 2017.
Brower, M. C. and Robinson, N. M.: Validation of the openWind Deep Array Wake Model (DAWM), in: AWEA Wind Resource and Project Energy Assessment Workshop 2013, AWEA, Las Vegas, NV, 2013.
Brower, M. C., Robinson, N. M., and Hale, E.: Wind Flow Modeling Uncertainty:
Quantification and Application to Monitoring Strategies and Project Design,
AWS Truepower, LLC, Albany, NY, 2010.
Brower, M. C., Bernadett, D., Van Kempen, S., Wilson, W., and Kramak, B.:
Actual vs. Predicted Plant Production: The Role of Turbine Performance, in:
AWEA Wind Resource and Project Energy Assessment Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Brower, M. C., Robinson, N. M., and Vila, S.: Wind Flow Modeling Uncertainty:
Theory and Application to Monitoring Strategies and Project Design, AWS Truepower, LLC, Albany, NY, 2015.
Brown, G.: Wakes: Ten Rows and Beyond, a Cautionary Tale!, in: AWEA Wind Resource and Project Energy Assessment Workshop 2012, AWEA, Pittsburgh, PA,
2012.
Byrkjedal, Ø., Hansson, J., and van der Velde, H.: Development of operational forecasting for icing and wind power at cold climate sites, in
IWAIS 2015: 16th International Workshop on Atmospheric Icing of Structures,
IWAIS, Uppsala, Sweden, p. 4, 2015.
Clark, S.: Wind Tunnel Comparison of Anemometer Calibration, in: AWEA Wind
Resource and Project Energy Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
Clifton, A., Smith, A., and Fields, M.: Wind Plant Preconstruction Energy
Estimates: Current Practice and Opportunities, NREL/TP-5000-64735, National Renewable Energy Laboratory, Golden, CO, 2016.
Clive, P.: Wind Farm Performance, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2010, AWEA, Oklahoma City, OK, 2010.
Colmenar-Santos, A., Campíez-Romero, S., Enríquez-Garcia, L.,
Pérez-Molina, C., Colmenar-Santos, A., Campíez-Romero, S.,
Enríquez-Garcia, L. A., and Pérez-Molina, C.: Simplified Analysis of
the Electric Power Losses for On-Shore Wind Farms Considering Weibull Distribution Parameters, Energies, 7, 6856–6885, https://doi.org/10.3390/en7116856, 2014.
Comstock, K.: Uncertainty and Risk Management in Wind Resource Assessment, in: AWEA Wind Resource and Project Energy Assessment Workshop 2011, AWEA,
Seattle, WA, 2011.
Comstock, K.: Identifying Pitfalls and Quantifying Uncertainties in Operating Project Re-Evaluation, in: AWEA Wind Resource and Project Energy Assessment Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Conroy, N., Deane, J. P., and, Ó Gallachóir, B. P.: Wind turbine
availability: Should it be time or energy based? – A case study in Ireland,
Renew. Energy, 36, 2967–2971, https://doi.org/10.1016/J.RENENE.2011.03.044, 2011.
Cox, S.: Validation of 25 offshore pre-construction energy forecasts against
real operational wind farm data, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
Craig, A., Optis, M., Fields, M. J., and Moriarty, P.: Uncertainty quantification in the analyses of operational wind power plant performance, J. Phys. Conf. Ser., 1037, 052021, https://doi.org/10.1088/1742-6596/1037/5/052021, 2018.
Crease, J.: CFD Modelling of Mast Effects on Anemometer Readings, in:
WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Crescenti, G. H., Poulos, G. S., and Bosche, J.: Valuable Lessons From Outliers In A Wind Energy Resource Assessment Benchmark Study, in: AWEA Wind
Resource and Project Energy Assessment Workshop 2019, AWEA, Renton, WA, 2019.
Cushman, A.: Industry Survey of Wind Farm Availability, in: AWEA Wind Resource and Project Energy Assessment Workshop 2009, AWEA, Minneapolis, MN, 2009.
Dahlberg, J.-Å.: Assessment of the Lillgrund Windfarm, Report no. 21858-1, Vattenfall Vindkraft AB, Stockholm, Sweden, 28 pp., 2009.
Derrick, A.: Uncertainty: The Classical Approach, in: AWEA Wind Resource and
Project Energy Assessment Workshop 2009, AWEA, Minneapolis, MN, 2009.
Drees, H. M. and Weiss, D. J.: Compilation of Power Performance Test Results, in: AWEA Wind Resource and Project Energy Assessment Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Drunsic, M. W.: Actual vs. Predicted Wind Project Performance: Is the Industry Closing the Gap?, in: AWEA Wind Resource and Project Energy Assessment Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Dutrieux, A.: How long should be long term to reduce uncertainty on annual
wind energy assessment, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Ehrmann, R. S., Wilcox, B., White, E. B., and Maniaci, D. C.: Effect of
Surface Roughness on Wind Turbine Performance, SAND2017-10669, Sandia National Laboratories, Albuquerque, NM and Livermore, CA, 2017.
Elkinton, M.: Strengthening Wake Models: DNV GL Validations & Advancements, in: AWEA Wind Resource and Project Energy Assessment Workshop 2013, AWEA, Las Vegas, NV, 2013.
Elkinton, M.: Current view of P50 estimate accuracy based on validation efforts, in: AWEA WINDPOWER 2017, AWEA, Anaheim, CA, 2017.
EMD International A/S: WindPRO 2.4., EMD International A/S, Aalborg, Denmark, 2004.
Faghani, D.: Measurement Uncertainty of Ground-Based Remote Sensing, in: AWEA
Wind Resource and Project Energy Assessment Workshop 2011, AWEA, Seattle, WA, 2011.
Faghani, D., Desrosiers, E., Aït-Driss, B., and Poulin, M.: Use of Remote Sensing in Addressing Bias & Uncertainty in Wind Measurements, in: AWEA Wind Resource and Project Energy Assessment Workshop 2008, AWEA, Portland, OR, 2008.
Faubel, A.: Digitalisation: Creating Value in O&M, in: WindEurope 2019,
WindEurope, Bilbao, Spain, 2019.
Filippelli, M., Bernadett, D., Sloka, L., Mazoyer, P., and Fleming, A.:
Concurrent Power Performance Measurements, in: AWEA Wind Resource and Project
Energy Assessment Workshop 2017, AWEA, Snowbird, UT, 2017.
Filippelli, M., Sherwin, B., and Fields, J.: IEC 61400-15 Working Group
Update, in: AWEA Wind Resource and Project Energy Assessment Workshop 2018,
AWEA, Austin, TX, 2018.
Friis Pedersen, T., Gjerding, S., Enevoldsen, P., Hansen, J. K., and
Jørgensen, H. K.: Wind turbine power performance verification in complex
terrain and wind farms, report no. Risoe-R 1330(EN), Forskningscenter Risoe, Roskilde, Denmark, 2002.
Garrad Hassan and Partners Ltd: GH WindFarmer Theory Manual, Bristol, England, 2009.
Geer, T.: Towards a more realistic uncertainty model, in: AWEA Wind Resource
and Project Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014.
Geer, T.: Identifying production risk in preconstruction assessments: Can we
do it?, in: AWEA Wind Resource and Project Energy Assessment Workshop 2015,
AWEA, New Orleans, LA, 2015.
Germer, S. and Kleidon, A.: Have wind turbines in Germany generated electricity as would be expected from the prevailing wind conditions in 2000–2014?, edited by: Leahy, P., PLoS One, 14, e0211028,
https://doi.org/10.1371/journal.pone.0211028, 2019.
Gillenwater, D., Masson, C., and Perron, J.: Wind Turbine Performance During
Icing Events, in: 46th AIAA Aerospace Sciences Meeting and Exhibit, American
Institute of Aeronautics and Astronautics, Reston, Virigina, 2008.
Gkarakis, K. and Orfanaki, G.: Historical wind speed trends and impact on
long-term adjustment and interannual variability in Cyprus, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Graves, A., Harman, K., Wilkinson, M., and Walker, R.: Understanding Availiability Trends of Operating Wind Farms, in: AWEA WINDPOWER 2008, AWEA,
Houston, TX, 2008.
Halberg, E.: A Monetary Comparison of Remote Sensing and Tall Towers, in: AWEA Wind Resource and Project Energy Assessment Workshop 2017, AWEA, Snowbird, UT, 2017.
Halberg, E. and Breakey, M.: On-Shore Wake Validation Study: Wake Analysis
Based on Production Data, in: AWEA Wind Resource and Project Energy Assessment Workshop 2013, AWEA, Las Vegas, NV, 2013.
Hale, E.: External Perspectives: Estimate Accuracy and Plant Operations, in:
AWEA Wind Resource and Project Energy Assessment Workshop 2017, AWEA, Snowbird, UT, 2017.
Hamel, M.: Estimating 50-yr Extreme Wind Speeds from Short Datasets, in: AWEA
Wind Resource and Project Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014.
Hamilton, S. D., Millstein, D., Bolinger, M., Wiser, R., and Jeong, S.: How
Does Wind Project Performance Change with Age in the United States?, Joule,
4, 1004–1020, https://doi.org/10.1016/j.joule.2020.04.005, 2020.
Hasager, C., Bech, J. I., Bak, C., Vejen, F., Madsen, M. B., Bayar, M., Skrzypinski, W. R., Kusano, Y., Saldern, M., Tilg, A.-M., Fæster, S., and
Johansen, N. F.-J.: Solution to minimize leading edge erosion on turbine blades, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Hatlee, S.: Measurement Uncertainty in Wind Resource Asessment, in: AWEA Wind
Resource and Project Energy Assessment Workshop 2011, AWEA, Seattle, WA, 2011.
Hatlee, S.: Operational Performance vs. Precon Estimate, in: AWEA Wind Resource and Project Energy Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
Healer, B.: Liquid Power Markets 201, in: AWEA Wind Resource and Project Energy Assessment Workshop 2018, AWEA, Austin, TX, 2018.
Hendrickson, M.: 2009 AWEA Wind Resource & Project Energy Assessment Workshop – Introduction, in: AWEA Wind Resource and Project Energy Assessment Workshop 2009, AWEA, Minneapolis, MN, 2009.
Hendrickson, M.: Extending Data – by whatever means necessary, in: AWEA Wind
Resource and Project Energy Assessment Workshop 2010, AWEA, Oklahoma City, OK, 2010.
Hendrickson, M.: Industry Survey of Wind Energy Assessment Techniques, in:
AWEA Wind Resource and Project Energy Assessment Workshop 2011, AWEA, Seattle, WA, 2011.
Hendrickson, M.: Extreme Winds in the Suitability Context: Should we be
Concerned?, in: AWEA Wind Resource and Project Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014.
Hendrickson, M.: P50 Bias Update: Are we there yet?, in: AWEA Wind Resource
and Project Energy Assessment Workshop 2019, AWEA, Renton, WA, 2019.
Hill, N., Pullinger, D., Zhang, M., and Crutchley, T.: Validation of windfarm
downtime modelling and impact on grid-constrained projects, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Holtslag, E.: Improved Bankability: The Ecofys position on LiDAR use, Utrecht, the Netherlands, 2013.
Horn, B.: Achieving Measurable Financial Results in Operational Assessments,
in: AWEA Wind Resource and Project Energy Assessment Workshop 2009, AWEA,
Minneapolis, MN, 2009.
Istchenko, R.: WRA Uncertainty Validation, in: AWEA Wind Resource and Project
Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014.
Istchenko, R.: Re-examining Uncertainty and Bias, in: AWEA Wind Resource and
Project Energy Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
Jaynes, D.: The Vestas Operating Fleet: Real-World Experience in Wind Turbine Siting and Power Curve Verification, in: AWEA Wind Resource and Project Energy Assessment Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Jog, C.: Benchmark: Wind flow, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2017, AWEA, Snowbird, UT, 2017.
Johnson, C.: Actual vs. Predicted performance – Validating pre construction
energy estimates, in: AWEA Wind Resource and Project Energy Assessment
Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Johnson, C., White, E., and Jones, S.: Summary of Actual vs. Predicted Wind
Farm Performance: Recap of WINDPOWER 2008, in: AWEA Wind Resource and Project
Energy Assessment Workshop 2008, AWEA, Portland, OR, 2008.
Johnson, J.: Typical Availability Losses and Categorization: Observations from an Operating Project Portfolio, in: AWEA Wind Resource and Project Energy Assessment Workshop 2011, AWEA, Seattle, WA, 2011.
Jones, S.: Project Underperformance: 2008 Update, in: AWEA WINDPOWER 2008,
AWEA, Houston, TX, 2008.
Kassebaum, J.: Power Curve Testing with Remote Sensing Devices, in: AWEA Wind
Resource and Project Energy Assessment Workshop 2013, AWEA, Las Vegas, NV, 2013.
Kassebaum, J. L.: What p-level is your p-ower curve?, in: AWEA Wind Resource
and Project Energy Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
Keck, R.-E., Sondell, N., and Håkansson, M.: Validation of a fully numerical approach for early stage wind resource assessment in absence of
on-site measurements, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Kelly, M.: Uncertainty in vertical extrapolation of wind statistics: shear-exponent and WAsP/EWA methods, No. 0121, DTU Wind Energy, Roskilde, Denmark, 2016.
Kelly, M., Kersting, G., Mazoyer, P., Yang, C., Hernández Fillols, F.,
Clark, S., and Matos, J. C.: Uncertainty in vertical extrapolation of measured wind speed via shear, No. E-0195, DTU Wind Energy, Roskilde, Denmark, 2019.
Kim, K. and Shin, P.: Analysis on the Parameters Under the Power Measurement
Uncertainty for a Small Wind Turbine, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Kline, J.: Wind Farm Wake Analysis: Summary of Past & Current Work, in: AWEA Wind Resource and Project Energy Assessment Workshop 2013, AWEA, Las Vegas, NV, 2013.
Kline, J.: Wake Model Validation Test, in: AWEA Wind Resource and Project
Energy Assessment Workshop 2016, AWEA, Minneapolis, MN, 2016.
Kline, J.: Detecting and Correcting for Bias in Long-Term Wind Speed Estimates, in: AWEA Wind Resource and Project Energy Assessment Workshop 2019, AWEA, Renton, WA, 2019.
Lackner, M. A., Rogers, A. L., and Manwell, J. F.: Uncertainty Analysis in
MCP-Based Wind Resource Assessment and Energy Production Estimation, J. Sol.
Energ. Eng., 130, 31006–31010, https://doi.org/10.1115/1.2931499, 2008.
Langel, C. M., Chow, R., Hurley, O. F., van Dam, C. P., Ehrmann, R. S., White, E. B., and Maniaci, D.: Analysis of the Impact of Leading Edge Surface
Degradation on Wind Turbine Performance, in: AIAA SciTech 33rd Wind Energy
Symposium, American Institute of Aeronautics and Astronautics, Kissimmee, FL, p. 13, 2015.
Langreder, W.: Uncertainty of Vertical Wind Speed Extrapolation, in: AWEA Wind Resource and Project Energy Assessment Workshop 2017, AWEA, Snowbird, UT, 2017.
Latoufis, K., Riziotis, V., Voutsinas, S., and Hatziargyriou, N.: Effects of
leading edge erosion on the power performance and acoustic noise emissions of locally manufactured small wind turbines blades, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Lee, J.: Banter on Blockage, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2019, AWEA, Renton, WA, 2019.
Lee, J. C. Y., Fields, M. J., and Lundquist, J. K.: Assessing variability of
wind speed: comparison and validation of 27 methodologies, Wind Energ. Sci.,
3, 845–868, https://doi.org/10.5194/wes-3-845-2018, 2018.
Liew, J., Urbán, A. M., Dellwick, E., and Larsen, G. C.: The effect of
wake position and yaw misalignment on power loss in wind turbines, in:
WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Lunacek, M., Fields, M. J., Craig, A., Lee, J. C. Y., Meissner, J., Philips,
C., Sheng, S., and King, R.: Understanding Biases in Pre-Construction Estimates, J. Phys. Conf. Ser., 1037, 062009,
https://doi.org/10.1088/1742-6596/1037/6/062009, 2018.
Maniaci, D. C., White, E. B., Wilcox, B., Langel, C. M., van Dam, C. P., and
Paquette, J. A.: Experimental Measurement and CFD Model Development of Thick
Wind Turbine Airfoils with Leading Edge Erosion, J. Phys. Conf. Ser., 753, 022013, https://doi.org/10.1088/1742-6596/753/2/022013, 2016.
McAloon, C.: Wind Assessment: Raw Data to Hub Height Winds, in: AWEA Wind
Resource and Project Energy Assessment Workshop 2010, AWEA, Oklahoma City, OK, 2010.
McCaa, J.: Wake modeling at 3TIER, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2013, AWEA, Las Vegas, NV, 2013.
Medley, J. and Smith, M.: The “Why?”, “What?” and “How?” of lidar type
classification, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Mibus, M.: Conservatism in Shadow Flicker Assessment and Wind Farm Design, in: AWEA Wind Resource and Project Energy Assessment Workshop 2018, AWEA,
Austin, TX, 2018.
Mönnich, K., Horodyvskyy, S., and Krüger, F.: Comparison of Pre-Construction Energy Yield Assessments and Operating Wind Farm's Energy Yields, UL International GmbH – DEWI, Oldenburg, Germany, 2016.
Mortensen, N. G.: Planning and Development of Wind Farms: Wind Resource
Assessment and Siting, report no. 0045(EN), DTU Wind Energy, Roskilde, Denmark, 2013.
Mortensen, N. G. and Ejsing Jørgensen, H.: Comparative Resource and
Energy Yield Assessment Procedures (CREYAP) Pt. II, in: EWEA Technology Workshop: Resource Assessment 2013, Dublin, Ireland, 2013.
Mortensen, N. G., Ejsing Jørgensen, H., Anderson, M., and Hutton, K.-A.:
Comparison of resource and energy yield assessment procedures, in: Proceedings of EWEA 2012 – European Wind Energy Conference & Exhibition
European Wind Energy Association (EWEA), Technical Universtiy of Denmark, Copenhagen, Denmark, p. 10, 2012.
Mortensen, N. G., Nielsen, M., and Ejsing Jørgensen, H.: Comparison of
Resource and Energy Yield Assessment Procedures 2011–2015: What have we learned and what needs to be done?, in: Proceedings of the European Wind
Energy Association Annual Event and Exhibition 2015, European Wind Energy
Association, Paris, France, 2015a.
Mortensen, N. G., Nielsen, M., and Ejsing Jørgensen, H.: EWEA CREYAP benchmark exercises: summary for offshore wind farm cases, Technical Universtiy of Denmark, Roskilde, Denmark, 2015b.
Murphy, O.: Blade Erosion Performance Impact, in: 21st Meeting of the Power
Curve Working Group, PCWG, Glasgow, Scotland, 2016.
Neubert, A.: WindFarmer White Paper, DNV GL, Oldenburg, Germany, 2016.
Nielsen, P., Villadsen, J., Kobberup, J., Madsen, P., Jacobsen, T., Thøgersen, M. L., Sørensen, M. V., Sørensen, T., Svenningsen, L.,
Motta, M., Bredelle, K., Funk, R., Chun, S., and Ritter, P.: WindPRO 2.7 User
Guide, 3rd Edn., Aalborg, Denmark, 2010.
Olauson, J., Edström, P., and Rydén, J.: Wind turbine performance
decline in Sweden, Wind Energy, 20, 2049–2053, https://doi.org/10.1002/we.2132, 2017.
Osler, E.: Yaw Error Detection and Mitigation with Nacelle Mounted Lidar, in:
AWEA Wind Resource and Project Energy Assessment Workshop 2013, AWEA, Las
Vegas, NV, 2013.
Ostridge, C.: Understanding & Predicting Turbine Performance, in: AWEA Wind Resource and Project Energy Assessment Workshop 2014, AWEA, Orlando, FL, 2014.
Ostridge, C.: Using Pattern of Production to Validate Wind Flow, Wakes, and
Uncertainty: Using Pattern of Production to Validate Wind Flow, Wakes, and
Uncertainty, in: AWEA Wind Resource and Project Energy Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
Ostridge, C.: Wind Power Project Performance White Paper 2017 Update, DNV GL, Seattle, WA, 2017.
Ostridge, C. and Rodney, M.: Modeling Wind Farm Energy, Revenue and Uncertainty on a Time Series Basis, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2016, AWEA, Minneapolis, MN, 2016.
Papadopoulos, I.: DNV GL Energy Production Assessment Validation 2019, report no. L2C183006-UKBR-R-01, DNV GL – Energy, Bristol, England, 2019.
Pedersen, H. S. and Langreder, W.: Hack the Error Codes of a Wind Turbine,
in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Perry, A.: Cross Validation of Operational Energy Assessments, in: AWEA Wind
Resource and Project Energy Assessment Workshop 2017, AWEA, Snowbird, UT, 2017.
Peyre, N.: How can drones improve topography inspections, terrain modelling and energy yield assessment?, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Poulos, G. S.: Complex Terrain Mesoscale Wind Flow Modeling: Successes, Failures and Practical Advice, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2017, AWEA, Snowbird, UT, 2017.
Pram, M.: Analysis of Vestas Turbine Performance, in: AWEA Wind Resource and
Project Energy Assessment Workshop 2018, AWEA, Austin, TX, 2018.
Pryor, S. C., Barthelmie, R. J., and Schoof, J. T.: Inter-annual variability
of wind indices across Europe, Wind Energy, 9, 27–38, https://doi.org/10.1002/we.178, 2006.
Pullinger, D., Ali, A., Zhang, M., Hill, M., and Crutchley, T.: Improving
accuracy of wind resource assessment through feedback loops of operational
performance data: a South African case study, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Randall, G.: Energy Assessment Uncertainty Analysis, in: AWEA Wind Resource
and Project Energy Assessment Workshop 2009, AWEA, Minneapolis, MN, 2009.
Redouane, A.: Analysis of pre- and post construction wind farm energy yields
with focus on uncertainties, Universität Kassel, Kassel, 2014.
Rezzoug, M.: Innovative system for performance optimization: Independent data to increase AEP and preserve turbine lifetime, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Rindeskär, E.: Modelling of icing for wind farms in cold climate: A comparison between measured and modelled data for reproducing and predicting
ice accretion, Examensarbete vid Institutionen för geovetenskaper,
MS thesis, Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL, Uppsala, Sweden, ISSN 1650-6553, 2010.
Robinson, N.: Blockage Effect Update, in: AWEA Wind Resource and Project
Energy Assessment Workshop 2019, AWEA, Renton, WA, 2019.
Rogers, A. L., Rogers, J. W., and Manwell, J. F.: Uncertainties in Results of
Measure-Correlate-Predict Analyses, in: European Wind Energy Conference 2006,
Athens, Greece, p. 10, 2006.
Rogers, T.: Effective Utilization of Remote Sensing, in: AWEA Wind Resource
and Project Energy Assessment Workshop 2010, AWEA, Oklahoma City, OK, 2010.
Rogers, T.: Estimating Long-Term Wind Speeds, in: AWEA Wind Resource and
Project Energy Assessment Workshop 2011, AWEA, Seattle, WA, 2011.
Sareen, A., Sapre, C. A., and Selig, M. S.: Effects of leading edge erosion on wind turbine blade performance, Wind Energy, 17, 1531–1542,
https://doi.org/10.1002/we.1649, 2014.
Schramm, M., Rahimi, H., Stoevesandt, B., and Tangager, K.: The Influence of
Eroded Blades on Wind Turbine Performance Using Numerical Simulations,
Energies, 10, 1420, https://doi.org/10.3390/en10091420, 2017.
Shihavuddin, A., Chen, X., Fedorov, V., Nymark Christensen, A., Andre Brogaard Riis, N., Branner, K., Bjorholm Dahl, A., and Reinhold Paulsen, R.: Wind Turbine Surface Damage Detection by Deep Learning Aided Drone Inspection Analysis, Energies, 12, 676, https://doi.org/10.3390/en12040676, 2019.
Sieg, C.: Validation Through Variation: Using Pattern of Production to Validate Wind Flow, Wakes, and Uncertainty, in: AWEA Wind Resource and Project Energy Assessment Workshop 2015, AWEA, New Orleans, LA, 2015.
Simon, R. L.: Long-term Inter-annual Resource Variations in California, in:
Wind Power, Palm Springs, California, 236–243, 1991.
Slater, J.: Floating lidar uncertainty reduction for use on operational wind
farms, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Slinger, C. W., Harris, M., Ratti, C., Sivamani, G., and Smith, M.: Nacelle
lidars for wake detection and waked inflow energy loss estimation, in:
WindEurope 2019, WindEurope, Bilbao, Spain, 2019a.
Slinger, C. W., Sivamani, G., Harris, M., Ratti, C., and Smith, M.: Wind yaw
misalignment measurements and energy loss projections from a multi-lidar
instrumented wind farm, in: WindEurope 2019, WindEurope, Bilbao, Spain, 2019b.
Smith, M., Wylie, S., Woodward, A., and Harris, M.: Turning the Tides on Wind
Measurements: The Use of Lidar to Verify the Performance of A Meteorological
Mast, in: WindEurope 2016, WindEurope, 2016.
Spalding, T.: Wind Farm Blockage Modeling Summary, in: AWEA Wind Resource and
Project Energy Assessment Workshop 2019, AWEA, Renton, WA, 2019.
Spengemann, P. and Borget, V.: Review and analysis of wind farm operational
data validation of the predicted energy yield of wind farms based on real
energy production data, DEWI Group, Wilhelmshaven, Germany and Lyon, France, 2008.
Spruce, C. J. and Turner, J. K.: Pitch Control for Eliminating Tower Vibration Events on Active Stall Wind Turbines, Surrey, UK, 2006.
Staffell, I. and Green, R.: How does wind farm performance decline with age?, Renew. Energy, 66, 775–786, https://doi.org/10.1016/j.renene.2013.10.041, 2014.
Standish, K., Rimmington, P., Laursen, J., Paulsen, H., and Nielsen, D.:
Computational Predictions of Airfoil Roughness Sensitivity, in: 48th AIAA
Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace
Exposition, American Institute of Aeronautics and Astronautics, Reston,
Virigina, 2010.
Stehly, T., Beiter, P., Heimiller, D., and Scott, G.: 2017 Cost of Wind
Energy Review, NREL/TP-6A20-72167, National Renewable Energy Laboratory, Golden, CO, 2018.
Stoelinga, M.: A Multi-Project Validation Study of a Time Series-Based Wake
Model, in WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Stoelinga, M. and Hendrickson, M.: A Validation Study of Vaisala's Wind
Energy Assessment Methods, Vaisala, Seattle, WA, 2015.
Tchou, J.: Successfully Transitioning Pre-Construction Measurements to Post-Construction Operations, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Tindal, A.: Wake modelling and validation, in: AWEA Wind Resource and Project
Energy Assessment Workshop 2009, AWEA, Minneapolis, MN, 2009.
Trudel, S.: Icing Losses Estimate Vadliation: From Development To Operation,
in: AWEA Wind Resource and Project Energy Assessment Workshop 2016, AWEA,
Minneapolis, MN, 2016.
VanLuvanee, D., Rogers, T., Randall, G., Williamson, A., and Miller, T.:
Comparison of WAsP, MS-Micro/3, CFD, NWP, and Analytical Methods for Estimating Site-Wide Wind Speeds, in: AWEA Wind Resource and Project Energy
Assessment Workshop 2009, AWEA, Minneapolis, MN, 2009.
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., Sanz Rodrigo, J., Sempreviva, A. M., Smith, J. C., Tuohy, A.,
and Wiser, R.: Grand challenges in the science of wind energy, Science, 366, 6464, https://doi.org/10.1126/science.aau2027, 2019.
Walls, L.: A New Method to Assess Wind Farm Performance and Quantify Model
Uncertainty, in: AWEA Wind Resource and Project Energy Assessment Workshop 2018, AWEA, Austin, TX, 2018.
Walter, K.: Wind Assessment: Raw Data to Hub Height Winds, in: AWEA Wind Resource and Project Energy Assessment Workshop 2010, AWEA, Oklahoma City, OK, 2010.
Waskom, M., Botvinnik, O., Ostblom, J., Lukauskas, S., Hobson, P., MaozGelbart, Gemperline, D. C., Augspurger, T., Halchenko, Y., Cole, J. B.,
Warmenhoven, J., Ruiter, J. de, Pye, C., Hoyer, S., Vanderplas, J., Villalba, S., Kunter, G., Quintero, E., Bachant, P., Martin, M., Meyer, K., Swain, C., Miles, A., Brunner, T., O'Kane, D., Yarkoni, T., Williams, M. L., and Evans, C.: mwaskom/seaborn: v0.10.0, Zenodo, https://doi.org/10.5281/zenodo.3629446, 2020.
White, E.: Continuing Work on Improving Plant Performance Estimates, in: AWEA
Wind Resource and Project Energy Assessment Workshop 2008, AWEA, Portland, OR, 2008a.
White, E.: Understanding and Closing the Gap on Plant Performance, in: AWEA
WINDPOWER 2008, AWEA, Houston, TX, 2008b.
White, E.: Operational Performance: Closing the Loop on Pre-Construction
Estimates, in: AWEA Wind Resource and Project Energy Assessment Workshop 2009, AWEA, Minneapolis, MN, 2009.
Wilcox, B. J., White, E. B., and Maniaci, D. C.: Roughness Sensitivity Comparisons of Wind Turbine Blade Sections, Albuquerque, NM, 2017.
Wilkinson, L., Kay, E., and Lawless, M.: Braced for the Storm? Startling
Insights into the Impact of Climate Change on Offshore Wind Operations, in:
WindEurope 2019, WindEurope, Bilbao, Spain, 2019.
Wilks, D. S.: Statistical methods in the atmospheric sciences, Academic Press, Amsterdam, the Netherlands, 2011.
Winslow, G.: Secondary Losses: Using Operational Data to Evaluate Losses and
Revisit Estimates, in: AWEA Wind Resource and Project Energy Assessment
Workshop 2012, AWEA, Pittsburgh, PA, 2012.
Wiser, R., Bolinger, M., Barbose, G., Barghouth, N., Hoen, B., Mills, A.,
Rand, J., Millstein, D., Jeong, S., Porter, K., Disanti, N., and Oteri, F.:
2018 Wind Technologies Market Report, US Department of Energy, Office of Energy Efficiency & Renewable Energy, Washington, D.C., 2019.
Wolfe, J.: Deep Array Wake Loss in Large Onshore Wind Farms (A Model Validation), in: AWEA Wind Resource and Project Energy Assessment Workshop 2010, AWEA, Oklahoma City, OK, 2010.
Žagar, M.: Wind Resource from an OEM perspective, in: WindEurope 2019,
WindEurope, Bilbao, Spain, 2019.
Zhang, M., Pullinger, D., Hill, N., and Crutchley, T.: Validating wind flow
model uncertainty using operational data, in: WindEurope 2019, AWEA, Renton,
WA, 2019.
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.
This review paper evaluates the energy prediction bias in the wind resource assessment process,...
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