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Fast-Running Engineering Models of Wind Farm Flows

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A3: Wind, Wave and Tidal Energy".

Deadline for manuscript submissions: closed (10 April 2023) | Viewed by 14559

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Special Issue Editors

Dr. Majid Bastankhah

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Guest Editor

Department of Engineering, Durham University, Durham DH1 3LE, UK
Interests: wind energy aerodynamics; physics-based engineering wake models; experimental fluid mechanics; turbulent flows

Dr. Ervin Bossanyi

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Guest Editor

1. Faculty of Engineering, Bristol University, Bristol BS8 1TS, UK
2. DNV, One Linear Park, Avon Street, Bristol BS2 0PS, UK
Interests: renewable energy technologies; grid integration; wind engineering; wind energy; wind turbines; wind farms; control; simulation; wind power and power systems

Dr. Dries Allaerts

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Guest Editor

Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS Delft, The Netherlands
Interests: wind farm aerodynamics; wake aerodynamics; boundary-layer meteorology; atmospheric gravity waves; computational fluid dynamics; engineering models

Special Issue Information

Dear Colleagues,

Despite the rapid growth of flow measurement technologies and numerical simulation techniques over the last few decades, fast-running engineering models are still the most popular tools in industry to characterise and predict wind farm flows. This is mainly due to their low computational costs and ease of use. These models, which can be empirical or physics-based, cover a wide range of topics including but not limited to:

Turbine wake flows: mean flow (steady or dynamic) and turbulence characteristics, wake meandering, wake recovery, near-wake to far-wake transition, wake deflection, etc.;
Cumulative wake effects: wake superposition techniques;
Load estimation: steady and unsteady distribution of loads on wind turbine blades and other turbine components, blade element momentum theory (BEM), fatigue due to turbulence, and wake immersion, etc.;
Flow blockage: velocity reduction upwind of wind turbines and wind farms, and flow speed-up (i.e., jetting) between adjacent wind turbine columns in a wind farm;
Topography and wind farms: impact of hills, forests, and human-made objects on the performance of wind turbines and wind farms;
Wind farm power production: effect of atmospheric turbulence or layout configuration on power generation, and power unsteadiness caused by turbulence, etc.;
Wind farm control: influencing wake effects and blockage by adjusting the control of individual turbines;
Wind farm interaction with the atmospheric boundary layer: top-down models, growth of the internal boundary layer, concept of infinite (i.e., very large) wind farms, wind-farm-induced atmospheric gravity waves, wind farm wakes and farm–farm interactions, etc.;
Thermal stability and Coriolis force: effect of thermal stratification on performance of wind farms, low-level jets, wind veer, and wind shear, etc.

The aim of this Special Issue is to gather new original research either on the development of new fast-running engineering models or the application of existing models in different fields of wind energy research mentioned above, and beyond.

Dr. Majid Bastankhah
Dr. Ervin Bossanyi
Dr. Dries Allaerts
Guest Editors

Manuscript Submission Information

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Keywords

Fast-running model
Analytical model
Physics-based model
Control-oriented model
Wind turbine
Wind farm
Wake
Active wake control
Wind farm flow control
Turbine power generation
Wake superposition
Cumulative wake effect
Wake meandering
Wake turbulence
Flow blockage
Thermal stability
Atmospheric boundary layer
Coriolis force
Wind veer
Wind shear
Topography
Internal boundary layer
Infinite wind farm
Atmospheric gravity waves

Published Papers (7 papers)

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Research

17 pages, 3682 KiB

Open AccessArticle

Prediction and Mitigation of Wind Farm Blockage Losses Considering Mesoscale Atmospheric Response

by Leila Legris, Morten Lindholt Pahus, Takafumi Nishino and Edgar Perez-Campos

Energies 2023, 16(1), 386; https://0-doi-org.brum.beds.ac.uk/10.3390/en16010386 - 29 Dec 2022

Cited by 1 | Viewed by 1405

Abstract

The engineering wind farm models currently used in industry can assess power losses due to turbine wake effects, but the prediction of power losses due to farm blockage is still a challenge. In this study we demonstrate a new prediction method of farm blockage losses and a possible strategy to mitigate them for a large offshore wind farm in the North Sea, by combining a common engineering wind farm model ’FLORIS’ with the ’two-scale momentum theory’ of Nishino and Dunstan (2020). Results show that the farm blockage losses depend significantly on the ’wind extractability’ factor, which reflects the strength of mesoscale atmospheric response. For a typical range of the extractability factor (assessed using a numerical weather prediction model) the farm blockage losses are shown to vary between about 5% and 15% of the annual energy production (AEP). However, these losses may be mitigated by adjusting turbine operating points taking into account the wind extractability. It is shown that a simple adjustment of the blade pitch angle and tip-speed ratio used below the rated wind speed may increase the AEP by up to about 2%. Full article

(This article belongs to the Special Issue Fast-Running Engineering Models of Wind Farm Flows)

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18 pages, 1775 KiB

Open AccessArticle

A New Streamwise Scaling for Wind Turbine Wake Modeling in the Atmospheric Boundary Layer

by Dara Vahidi and Fernando Porté-Agel

Energies 2022, 15(24), 9477; https://0-doi-org.brum.beds.ac.uk/10.3390/en15249477 - 14 Dec 2022

Cited by 4 | Viewed by 1501

Abstract

In this study, we aim to investigate if there is a scaling of the streamwise distance from a wind turbine that leads to a collapse of the mean wake velocity deficit under different ambient turbulence levels. For this purpose, we perform large-eddy simulations of the wake of a wind turbine under neutral atmospheric conditions with various turbulence levels. Based on the observation that a higher atmospheric turbulence level leads to faster wake recovery and shorter near-wake length, we propose the use of the near-wake length as an appropriate normalization length scale. By normalizing the streamwise distance by the near-wake length, we obtain a collapse of the normalized wake velocity deficit profiles for different turbulence levels. We then explore the possibility of using the relationship obtained for the normalized maximum wake velocity deficit as a function of the normalized streamwise distance in the context of analytical wake modeling. Specifically, we investigate two approaches: (a) using the new relationship as a stand-alone model to calculate the maximum wake velocity deficit, and (b) using the new relationship to calculate the wake advection velocity within a physics-based wake expansion model. Large-eddy simulation of the wake of a wind turbine under neutral atmospheric conditions is used to evaluate the performance of both approaches. Overall, we observe good agreement between the simulation data and the model predictions, along with considerable savings in terms of the models’ computational costs. Full article

(This article belongs to the Special Issue Fast-Running Engineering Models of Wind Farm Flows)

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16 pages, 4333 KiB

Open AccessFeature PaperArticle

Curled-Skewed Wakes behind Yawed Wind Turbines Subject to Veered Inflow

by Mohammadreza Mohammadi, Majid Bastankhah, Paul Fleming, Matthew Churchfield, Ervin Bossanyi, Lars Landberg and Renzo Ruisi

Energies 2022, 15(23), 9135; https://0-doi-org.brum.beds.ac.uk/10.3390/en15239135 - 02 Dec 2022

Cited by 4 | Viewed by 1961

Abstract

This work presents a new engineering analytical model that predicts the effect of both the turbine yaw misalignment and the inflow wind veer on the wake flow distribution downwind of a wind turbine. To consider the veered inflow, two methods were examined. In the first method, the curled shape of the wake due to the yaw offset is initially modelled. The wake shape is then laterally skewed at each height due to the wind veer based on the assumption that the turbine wake is transported downstream by the incoming flow. The second method is a more realistic approach that accounts for the effect of wind veer on the wind velocity direction and the yaw angle seen by the wind turbine. This models the wake region in a local coordinate system defined based on the wind direction at each height. A coordinate transformation is then performed to represent the wake flow distribution in the global coordinate system attached to the ground. The results show that while the two methods provide similar outputs for small variations in the wind direction across the rotor, the difference becomes more evident with an increase in wind veer. High-fidelity simulations for a turbine subject to a neutral atmospheric boundary layer were employed to validate model predictions for different operating conditions. Full article

(This article belongs to the Special Issue Fast-Running Engineering Models of Wind Farm Flows)

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15 pages, 5515 KiB

Open AccessArticle

Wind Tunnel Study on the Tip Speed Ratio’s Impact on a Wind Turbine Wake Development

by Ingrid Neunaber, Michael Hölling and Martin Obligado

Energies 2022, 15(22), 8607; https://0-doi-org.brum.beds.ac.uk/10.3390/en15228607 - 17 Nov 2022

Cited by 1 | Viewed by 1163

Abstract

We propose an experimental study on the influence of the tip speed ratio on the spatial development of a wind turbine wake. To accomplish this, a scaled wind turbine is tested in a wind tunnel, and its turbulent wake measured for streamwise distances between 1 and 30 diameters. Two different tip speed ratios (5.3 and 4.5) are tested by varying the pitch angle of the rotor blades between the optimal setting and one with an offset of

+ 6^{\circ}

. In addition, we test two Reynolds numbers for the optimal tip speed ratio,

R e_{D} = 1.9 \times 10^{5}

and

R e_{D} = 2.9 \times 10^{5}

(based on the turbine diameter and the freestream velocity). For all cases, the mean streamwise velocity deficit at the centerline evolves close to a power law in the far wake, and we check the validity of the Jensen and Bastankhah-Porté-Agel engineering wind turbine wake models and the Townsend-George wake model for free shear flows for this region. Lastly, we present radial profiles of the mean streamwise velocity and test different radial models. Our results show that the lateral profile of the wake is properly fitted by a super-Gaussian curve close to the rotor, while Gaussian-like profiles adapt better in the far wake. Full article

(This article belongs to the Special Issue Fast-Running Engineering Models of Wind Farm Flows)

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23 pages, 5113 KiB

Open AccessArticle

Ensemble-Based Flow Field Estimation Using the Dynamic Wind Farm Model FLORIDyn

by Marcus Becker, Dries Allaerts and Jan-Willem van Wingerden

Energies 2022, 15(22), 8589; https://0-doi-org.brum.beds.ac.uk/10.3390/en15228589 - 16 Nov 2022

Cited by 2 | Viewed by 1159

Abstract

Wind farm control methods allow for a more flexible use of wind power plants over the baseline operation. They can be used to increase the power generated, to track a reference power signal or to reduce structural loads on a farm-wide level. Model-based control strategies have the advantage that prior knowledge can be included, for instance by simulating the current flow field state into the near future to take adequate control actions. This state needs to describe the real system as accurately as possible. This paper discusses what state estimation methods are suitable for wind farm flow field estimation and how they can be applied to the dynamic engineering model FLORIDyn. In particular, we derive an Ensemble Kalman Filter framework which can identify heterogeneous and changing wind speeds and wind directions across a wind farm. It does so based on the power generated by the turbines and wind direction measurements at the turbine locations. Next to the states, this framework quantifies uncertainty for the resulting state estimates. We also highlight challenges that arise when ensemble methods are applied to particle-based flow field simulations. The development of a flow field estimation framework for dynamic low-fidelity wind farm models is an essential step toward real-time dynamic model-based closed-loop wind farm control. Full article

(This article belongs to the Special Issue Fast-Running Engineering Models of Wind Farm Flows)

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Graphical abstract

13 pages, 1576 KiB

Open AccessArticle

An Analytical Model for Wind Turbine Wakes under Pressure Gradient

by Arslan Salim Dar and Fernando Porté-Agel

Energies 2022, 15(15), 5345; https://0-doi-org.brum.beds.ac.uk/10.3390/en15155345 - 23 Jul 2022

Cited by 12 | Viewed by 1776

Abstract

In this study, we present an analytical modeling framework for wind turbine wakes under an arbitrary pressure gradient imposed by the base flow. The model is based on the conservation of the streamwise momentum and self-similarity of the wake velocity deficit. It builds on the model proposed by Shamsoddin and Porté-Agel, which only accounted for the imposed pressure gradient in the far wake. The effect of the imposed pressure gradient on the near wake velocity is estimated by using Bernoulli’s equation. Using the estimated near wake velocity as the starting point, the model then solves an ordinary differential equation to compute the streamwise evolution of the maximum velocity deficit in the turbine far wake. The model is validated against experimental data of wind turbine wakes on escarpments of varying geometries. In addition, a comparison is performed with a pressure gradient model which only accounts for the imposed pressure gradient in the far wake, and with a model that does not account for any imposed pressure gradient. The new model is observed to agree well with the experimental data, and it outperforms the other two models tested in the study for all escarpment cases. Full article

(This article belongs to the Special Issue Fast-Running Engineering Models of Wind Farm Flows)

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23 pages, 2951 KiB

Open AccessEditor’s ChoiceArticle

Comparison of the Gaussian Wind Farm Model with Historical Data of Three Offshore Wind Farms

by Bart Matthijs Doekemeijer, Eric Simley and Paul Fleming

Energies 2022, 15(6), 1964; https://0-doi-org.brum.beds.ac.uk/10.3390/en15061964 - 08 Mar 2022

Cited by 12 | Viewed by 3498

Abstract

A recent expert elicitation showed that model validation remains one of the largest barriers for commercial wind farm control deployment. The Gaussian-shaped wake deficit model has grown in popularity in wind farm field experiments, yet its validation for larger farms and throughout annual operation remains limited. This article addresses this scientific gap, providing a model comparison of the Gaussian wind farm model with historical data of three offshore wind farms. The energy ratio is used to quantify the model’s accuracy. We assume a fixed turbulence intensity of

I_{\infty} = 6 %

and a standard deviation on the inflow wind direction of

σ_{w d} = 3 °

in our Gaussian model. First, we demonstrate the non-uniqueness issue of

I_{\infty}

and

σ_{w d}

, which display a waterbed effect when considering the energy ratios. Second, we show excellent agreement between the Gaussian model and historical data for most wind directions in the Offshore Windpark Egmond aan Zee (OWEZ) and Westermost Rough wind farms (36 and 35 wind turbines, respectively) and wind turbines on the outer edges of the Anholt wind farm (110 turbines). Turbines centrally positioned in the Anholt wind farm show larger model discrepancies, likely due to deep-array effects that are not captured in the model. A second source of discrepancy is hypothesized to be inflow heterogeneity. In future work, the Gaussian wind farm model will be adapted to address those weaknesses. Full article

(This article belongs to the Special Issue Fast-Running Engineering Models of Wind Farm Flows)

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