scholarly journals Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

2017 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wrf Model ◽  
Power Production ◽  
Weather Research And Forecasting ◽  
Model Version ◽  
The Impact ◽  
Weather Research

Abstract. Forecasts of wind power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate that a vertical grid with nominally 12-m vertical resolution is necessary for reproducing the observed power production, with statistical significance. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed and low turbulence conditions. We also find the WFP performance is independent of atmospheric stability, the number of wind turbines per model grid cell, and the upwind-downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

scholarly journals Evaluation of the wind farm parameterization in the Weather Research and Forecasting model (version 3.8.1) with meteorological and turbine power data

2017 ◽  
Vol 10 (11) ◽  
pp. 4229-4244 ◽  
Author(s):  
Joseph C. Y. Lee ◽  
Julie K. Lundquist
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Statistical Significance ◽  
Wrf Model ◽  
Grid Cell ◽  
Power Production ◽  
Weather Research And Forecasting ◽  
Model Version ◽  
The Impact ◽  
Weather Research

Abstract. Forecasts of wind-power production are necessary to facilitate the integration of wind energy into power grids, and these forecasts should incorporate the impact of wind-turbine wakes. This paper focuses on a case study of four diurnal cycles with significant power production, and assesses the skill of the wind farm parameterization (WFP) distributed with the Weather Research and Forecasting (WRF) model version 3.8.1, as well as its sensitivity to model configuration. After validating the simulated ambient flow with observations, we quantify the value of the WFP as it accounts for wake impacts on power production of downwind turbines. We also illustrate with statistical significance that a vertical grid with approximately 12 m vertical resolution is necessary for reproducing the observed power production. Further, the WFP overestimates wake effects and hence underestimates downwind power production during high wind speed, highly stable, and low turbulence conditions. We also find the WFP performance is independent of the number of wind turbines per model grid cell and the upwind–downwind position of turbines. Rather, the ability of the WFP to predict power production is most dependent on the skill of the WRF model in simulating the ambient wind speed.

A study of the impact of very large wind farms on regional weather using the WRF model in high resolution

2020 ◽  
Author(s):  
Simon Jacobsen ◽  
Aksel Walløe Hansen
Keyword(s):  
Wind Speed ◽  
Wind Turbines ◽  
Wind Farm ◽  
Wrf Model ◽  
Wind Farms ◽  
Real Data ◽  
Power Production ◽  
Wake Effects ◽  
The Impact ◽  
Large Wind

<p>The Weather Research and Forecasting (WRF) model fitted with the Fitch et al. (2012) scheme for parameterization of the effect of wind energy extraction is used to study the effects of very large wind farms on regional weather. Two real data cases have been run in a high spatial resolution (grid size 500 m). Both cases are characterized by a convective westerly flow. The inner model domain covers the North Sea and Denmark. The largest windfarm consists of 200.000 wind turbines each with a capacity of 8MW. The model is run for up to 12 hours with and without the wind farm. The impact on the regional weather of these very large wind farms are studied and presented. Furthermore, the effect of horizontal spacing between wind turbines is investigated. Significant impact on the regional weather from the very large wind farms was found. Horizontal wind speed changes occur up to 3500m above the surface. The precipitation pattern is greatly affected by the very large wind farms due to the enhanced mixing in the boundary layer. Increased precipitation occurs at the front? within the wind farm, thus leaving the airmass relatively dry downstream when it reaches the Danish coast, resulting in a decrease in precipitation here compared to the control run. The formation of a small low level jet is found above the very large wind farm. Furthermore, wake effects from individual wind turbines decrease the total power production. The wind speed in the real data cases are well above the speed of maximum power production of the wind turbines. Yet most of the 200.000 wind turbines are producing only 1MW due the wake effects. A simulation run with a wind farm of 50.000 8MW wind turbines was also run. This windfarm covers the same area as the previous one, but horizontal distance between wind turbines are 1000m instead of 500m. This configuration was found to produce a similar amount of power as the 200.000 configuration. However, the atmospheric impact on regional weather is smaller but still large with 50.000 wind turbines.</p>

scholarly journals Spatiotemporal Correlations in the Power Output of Wind Farms: On the Impact of Atmospheric Stability

Energies ◽  
2019 ◽  
Vol 12 (8) ◽  
pp. 1486 ◽  
Author(s):  
Nicolas Tobin ◽  
Adam Lavely ◽  
Sven Schmitz ◽  
Leonardo P. Chamorro
Keyword(s):  
Power Output ◽  
Wind Farm ◽  
Statistical Significance ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Temporal Correlations ◽  
Theoretical Predictions ◽  
Power Variance ◽  
Ambient Turbulence ◽  
The Impact

The dependence of temporal correlations in the power output of wind-turbine pairs on atmospheric stability is explored using theoretical arguments and wind-farm large-eddy simulations. For this purpose, a range of five distinct stability regimes, ranging from weakly stable to moderately convective, were investigated with the same aligned wind-farm layout used among simulations. The coherence spectrum between turbine pairs in each simulation was compared to theoretical predictions. We found with high statistical significance (p < 0.01) that higher levels of atmospheric instability lead to higher coherence between turbines, with wake motions reducing correlations up to 40%. This is attributed to higher dominance of atmospheric motions over wakes in strongly unstable flows. Good agreement resulted with the use of an empirical model for wake-added turbulence to predict the variation of turbine power coherence with ambient turbulence intensity (R 2 = 0.82), though other empirical relations may be applicable. It was shown that improperly accounting for turbine–turbine correlations can substantially impact power variance estimates on the order of a factor of 4.

Influence of Atmospheric Stability on Wind Turbine Power Performance Curves

2006 ◽  
Vol 128 (4) ◽  
pp. 531-538 ◽  
Author(s):  
Jonathon Sumner ◽  
Christian Masson
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Similarity Theory ◽  
Atmospheric Stability ◽  
Performance Testing ◽  
System Of Nonlinear Equations ◽  
Power Performance ◽  
Turbine Efficiency ◽  
The Impact ◽  
Power Curves

The impact of atmospheric stability on vertical wind profiles is reviewed and the implications for power performance testing and site evaluation are investigated. Velocity, temperature, and turbulence intensity profiles are generated using the model presented by Sumner and Masson. This technique couples Monin-Obukhov similarity theory with an algebraic turbulence equation derived from the k-ϵ turbulence model to resolve atmospheric parameters u*, L, T*, and z0. The resulting system of nonlinear equations is solved with a Newton-Raphson algorithm. The disk-averaged wind speed u¯disk is then evaluated by numerically integrating the resulting velocity profile over the swept area of the rotor. Power performance and annual energy production (AEP) calculations for a Vestas Windane-34 turbine from a wind farm in Delabole, England, are carried out using both disk-averaged and hub height wind speeds. Although the power curves generated with each wind speed definition show only slight differences, there is an appreciable impact on the measured maximum turbine efficiency. Furthermore, when the Weibull parameters for the site are recalculated using u¯disk, the AEP prediction using the modified parameters falls by nearly 5% compared to current methods. The IEC assumption that the hub height wind speed can be considered representative tends to underestimate maximum turbine efficiency. When this assumption is further applied to energy predictions, it appears that the tendency is to overestimate the site potential.

scholarly journals How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

2020 ◽  
Vol 5 (3) ◽  
pp. 1169-1190
Author(s):  
Patrick Murphy ◽  
Julie K. Lundquist ◽  
Paul Fleming
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Turbine Blades ◽  
Atmospheric Stability ◽  
Power Production ◽  
High Plains ◽  
Power Curve ◽  
Mean Power ◽  
Rotor Disk ◽  
Utility Scale

Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, also known as shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer), and rotor-equivalent wind speed (REWS; a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured by either the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW or up to 5 % of the rated power for a single 1.5 MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

scholarly journals How wind speed shear and directional veer affect the power production of a megawatt-scale operational wind turbine

2019 ◽  
Author(s):  
Patrick Murphy ◽  
Julie K. Lundquist ◽  
Paul Fleming
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Turbine Blades ◽  
Atmospheric Stability ◽  
Power Production ◽  
High Plains ◽  
Power Curve ◽  
Mean Power ◽  
Rotor Disk ◽  
Utility Scale

Abstract. Most megawatt-scale wind turbines align themselves into the wind as defined by the wind speed at or near the center of the rotor (hub height). However, both wind speed and wind direction can change with height across the area swept by the turbine blades. A turbine aligned to hub-height winds might experience suboptimal or superoptimal power production, depending on the changes in the vertical profile of wind, or shear. Using observed winds and power production over 6 months at a site in the high plains of North America, we quantify the sensitivity of a wind turbine's power production to wind speed shear and directional veer as well as atmospheric stability. We measure shear using metrics such as α (the log-law wind shear exponent), βbulk (a measure of bulk rotor-disk-layer veer), βtotal (a measure of total rotor-disk-layer veer) and rotor-equivalent wind speed (REWS), a measure of actual momentum encountered by the turbine by accounting for shear). We also consider the REWS with the inclusion of directional veer, REWSθ, although statistically significant differences in power production do not occur between REWS and REWSθ at our site. When REWS differs from the hub-height wind speed (as measured either by the lidar or a transfer function-corrected nacelle anemometer), the turbine power generation also differs from the mean power curve in a statistically significant way. This change in power can be more than 70 kW, or up to 5 % of the rated power for a single 1.5-MW utility-scale turbine. Over a theoretical 100-turbine wind farm, these changes could lead to instantaneous power prediction gains or losses equivalent to the addition or loss of multiple utility-scale turbines. At this site, REWS is the most useful metric for segregating the turbine's power curve into high and low cases of power production when compared to the other shear or stability metrics. Therefore, REWS enables improved forecasts of power production.

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