Wind farm flow control: prospects and challenges

Meyers, Johan; Bottasso, Carlo L.; Dykes, Katherine; Fleming, Paul; Gebraad, Pieter; Giebel, Gregor; Göçmen, Tuhfe; Wingerden, Jan‐Willem van

doi:10.5194/wes-7-2271-2022

Cited by 114 publications

(91 citation statements)

References 302 publications

(325 reference statements)

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“…Accurate wake modeling is essential for optimizing wind plant layouts and creating effective control strategies (Veers et al, 2022;Meyers et al, 2022). Hybrid wake models balance the accuracy of high-fidelity simulations with the computational efficiency of analytic models to facilitate wind plant design studies.…”

Section: Introductionmentioning

confidence: 99%

“…This allows hybrid wake models to include additional physics beyond the scope of typical engineering wake models without incurring substantial computational costs. While wake model development is an active area (Porté-Agel et al, 2020;Bastankhah et al, 2021Bastankhah et al, , 2022, in the context of wind plant design, the applicability of these models is largely dependent on their ability to predict wake recovery on the order of turbine row spacing (Meyers et al, 2022). Subtle differences in estimating wake losses at this scale have an outsized impact on assessing the effectiveness of control strategies (Bay et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Evolution of eddy viscosity in the wake of a wind turbine

et al. 2023

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Abstract. The eddy viscosity hypothesis is a popular method in wind turbine wake modeling for estimating turbulent Reynolds stresses. We document the downstream evolution of eddy viscosity in the wake of a wind turbine from experimental and large-eddy-simulation data. Wake eddy viscosity is isolated from its surroundings by subtracting the inflow profile, and the driving forces are identified in each wake region. Eddy viscosity varies in response to changes in turbine geometry and nacelle misalignment with larger turbines generating stronger velocity gradients and shear stresses. We propose a model for eddy viscosity based on a Rayleigh distribution. Model parameters are obtained from scaling the eddy viscosity hypothesis and demonstrate satisfactory agreement with the reference data. The model is implemented in the curled wake formulation in the FLOw Redirection and Induction in Steady State (FLORIS) framework and assessed through comparisons with the previous formulation. Our approach produced more accurate flow field estimates with lower total error for the majority of cases.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evolution of eddy viscosity in the wake of a wind turbine

et al. 2023

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“…However, in this study, we simply limit the yaw offset magnitude to 25° for all wind conditions to estimate potential power increases from wake steering. For each wind direction and wind speed bin, yaw offsets are optimized using the Serial-Refine (SR) optimization method in FLORIS (Fleming et al, 2022). As discussed by Fleming et al (2022), compared to the gradient-based sequential least-squares programming (SLSQP) optimization method implemented in FLORIS using the SciPy Python package (Virtanen et al, 2020)-which was used to estimate the AEP gain from wake steering for the representative set of 50 U.S. wind plants by Bensason et al (2021)-the SR method tends to find yaw offsets that yield slightly higher power production while requiring significantly less computation time.…”

Section: Wake Steering Optimization Using Florismentioning

confidence: 99%

“…For each wind direction and wind speed bin, yaw offsets are optimized using the Serial-Refine (SR) optimization method in FLORIS (Fleming et al, 2022). As discussed by Fleming et al (2022), compared to the gradient-based sequential least-squares programming (SLSQP) optimization method implemented in FLORIS using the SciPy Python package (Virtanen et al, 2020)-which was used to estimate the AEP gain from wake steering for the representative set of 50 U.S. wind plants by Bensason et al (2021)-the SR method tends to find yaw offsets that yield slightly higher power production while requiring significantly less computation time. The SR method begins by stepping serially through each wind turbine in a wind plant, from the most upstream to the farthest downstream turbine, and evaluating the power produced by the wind plant for a discrete set of NYaw yaw offsets evenly spaced between the lower and upper offset bounds (we used NYaw = 5 in this study, resulting in the set: {0°, ±12.5°, and ±25°}).…”

Section: Wake Steering Optimization Using Florismentioning

confidence: 99%

“…Wind farm flow control is a technology that coordinates the control actions of individual turbines to achieve wind plant-level objectives, such as increasing overall energy production or reducing structural loads, by influencing aerodynamic interactions between turbines (Boersma et al, 2017;Meyers et al, 2022). Wake steering is a type of wind farm flow control in which…”

Section: Introductionmentioning

confidence: 99%

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The value of wake steering wind farm control in U.S. energy markets

Simley¹,

Millstein²,

Jeong³

et al. 2023

Preprint

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Abstract. Wind farm flow control represents a category of control strategies for increasing wind plant power production and/or reducing structural loads by mitigating the impact of wake interactions between wind turbines. Wake steering is a wind farm flow control technology in which specific turbines are misaligned with the wind to deflect their wakes away from downstream turbines, thus increasing overall wind plant power production. In addition to promising results from simulation studies, wake steering has been shown to successfully increase energy production through several recent field trials. However, to better understand the benefits of wind farm flow control strategies such as wake steering, the value of the additional energy to the electrical grid should be evaluated—for example, by considering the price of electricity when the additional energy is produced. In this study, we investigate the potential for wake steering to increase the value of wind plant energy production by combining model predictions of power gains using the FLOw Redirection and Induction in Steady State (FLORIS) engineering wind farm control tool with historical electricity price data for 15 existing U.S. wind plants in four different electricity market regions. Specifically, for each wind plant, we use FLORIS to estimate power gains from wake steering for a time series of hourly wind speeds and wind directions spanning the years 2018–2020, obtained from the ERA5 reanalysis data set. The modeled power gains are then correlated with hourly electricity prices for the nearest transmission node. Through this process we find that wake steering increases annual energy production (AEP) between 0.5 % and 2 %, depending on the wind plant, with average increases in potential annual revenue (i.e., annual value production (AVP)) 10 % higher than the AEP gains. For all wind plants, AVP gain was found to exceed AEP gain. But the ratio between AVP gain and AEP gain is greater for wind plants in regions with high wind penetration because electricity prices tend to be relatively higher during periods with below-rated wind plant power production, when wake losses occur and wake steering is active; for wind plants in the Southwest Power Pool—the region with the highest wind penetration analyzed (31 %)—the increase in AVP from wake steering is 21 % higher than the AEP gain. Consequently, we expect the value of wake steering, and other types of wind farm flow control, to increase as wind penetration continues to grow.

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Numerical simulations for a parametric study of blockage effect on offshore wind farms

Flaszyński,

Wasilczuk,

Piotrowicz

et al. 2023

Wind Energy

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The paper presents a study of the upstream influence of wind farms on the wind speed, which is called blockage effect. A Reynolds Averaged Navier–Stokes (RANS) numerical model using an actuator disc method was devised and validated using the SCADA data from a Horns Rev 1 wind farm. The maximum difference between the average power in the first row for SCADA and the numerical model was 7.8%. The model was used to determine the impact of blockage effect on the wind farm parameters and the extent to which the wind speed and the power generation were reduced. A reference wind farm was defined, with a modified size, spacing, turbine height, and diameter that were used for comparison with other wind farm configurations. The results of the investigation of the wind farm parameter effects on the upstream wind speed reduction are presented in the paper. It has been established that increasing the turbine spacing from 5D to 6.7D reduces the power loss due to blockage by two. Blockage losses are almost eliminated when the spacing is increased two times. Similarly, the wind turbine thrust coefficient (CT) has a large impact on blockage, which is more pronounced, when CT is higher. In fact, the velocity deficit due to blockage is proportional to CT. The turbine tower height has small impact on blockage effect—the power reduction was changed by 0.3% due to blockage for the investigated range. The number of turbines in a row (with a constant number of turbines in a row) does not affect blockage significantly.

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Wind farm flow control: prospects and challenges

Cited by 114 publications

References 302 publications

Evolution of eddy viscosity in the wake of a wind turbine

Evolution of eddy viscosity in the wake of a wind turbine

The value of wake steering wind farm control in U.S. energy markets

Numerical simulations for a parametric study of blockage effect on offshore wind farms

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