Hub-height Wind Research Articles

The future of wind energy in California: Future projections with the Variable-Resolution CESM

Shifting wind patterns are an expected consequence of global climate change, with direct implications for wind energy production. However, wind is notoriously difficult to predict, and significant uncertainty remains in our understanding of climate change impacts on existing wind generation capacity. In this study, historical and future wind climatology and associated capacity factors at five wind turbine sites in California are examined. Historical (1980–2000) and mid-century (2030–2050) simulations were produced using the Variable-Resolution Community Earth System Model (VR-CESM) to understand how these wind generation sites are expected to be impacted by climate change. A high-resolution statistically downscaled WRF product provided by DNV GL, reanalysis datasets MERRA-2, CFSR, NARR, and observational data were used for model validation and comparison. These projections suggest that wind power generation capacity throughout the state is expected to increase during the summer, and decrease during fall and winter, based on significant changes at several wind farm sites. This study improves the characterization of uncertainty around the magnitude and variability in space and time of California's wind resources in the near future, and also enhances our understanding of the physical mechanisms related to the trends in wind resource variability. • The state-of-the-art global climate model Variable-Resolution Community Earth System Model (VR-CESM) was used. • Model validation and comparison of hub-height wind speed with industry-approved datasets. • Significant wind energy resources trend were detected at all major wind farm locations in future climate. • Analyzed synoptic-scale patterns associated with the wind energy resources change under future climate.

Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data

Abstract This study presents a hybrid numerical weather prediction model (NWP) and a Gaussian process regression (GPR) model for near surface wind speed prediction up to 72 h ahead using data partitioned on atmospheric stability class to improve model performance. NWP wind speed data from the UK meteorological office was corrected using a GPR model, where the data was subdivided using the atmospheric stability class calculated using the Pasquill-Gifford-Turner method based on observations at the time of prediction. The method was validated using data from 15 UK MIDAS (Met office Integrated Data Archive System) sites with a 9 month training and 3 month test period. Results are also shown for hub height wind speed prediction at one turbine. Additionally, power output is predicted for this turbine by translating hub height wind speed to power using a turbine power curve. While various forecasting methods exist, limited methods consider the impact of atmospheric stability on prediction accuracy. Therefore the method presented in this study gives a new way to improve wind speed predictions. Outputs show the GPR model improves forecast accuracy over the original NWP data, and consideration of atmospheric stability further reduces prediction errors. Comparing predicted power output to measured output reveals power predictions are also enhanced, demonstrating the potential of this novel wind speed prediction technique.

Variability of wind turbine noise over a diurnal cycle

Abstract The diurnal variation of atmospheric conditions over land has a significant effect on the wind and temperature distributions which greatly influence the generation and propagation of wind turbine aerodynamic sound. In this paper, a fully consistent unsteady approach is used to study wind turbine noise such that large eddy simulation with a rotational actuator disk wind turbine model is used to model the wind and temperature around a mega-watt scale wind turbine over a diurnal cycle, and time dependent flow and temperature fields are used as input to the coupled wind turbine noise generation-propagation model. Computations are carried out for four different 10 min datasets selected at certain periods of a day for a same hub height wind speed. It is observed that the time dependent as well as the time averaged sound pressure levels in near field do not show large variations during the day. However, as we move away from the turbine, the propagation effects take over and downwind of the turbine the night time levels exceed the day time levels (at 3600 m the averaged difference reaches 6.5 dBA).

Atmospheric stability and topography effects on wind turbine performance and wake properties in complex terrain

Abstract This paper evaluates the influence of atmospheric stability and topography on wind turbine performance and wake properties in complex terrain. To assess atmospheric stability effects on wind turbine performance, an equivalent wind speed calculated with the power output and the manufacture power curve is proposed and calibrated with the mast hub-height wind speed. After estimating the thrust coefficient and turbulence dissipation, this paper examines wind turbine performance curves and wake profiles segregated by atmospheric stability. Results show that the equivalent wind speed at a given mast wind speed can increase by 2% under stable conditions and decrease by 5% under unstable conditions as compared with that under neutral conditions, yielding about 16% reductions of power output and thrust coefficient from stable conditions to unstable conditions. Due to the lower thrust coefficient and the enhanced turbulence, the wind turbine wakes are found to recover faster under unstable conditions than under other stability conditions. Differences in wind turbine performance and asymmetric wake profiles due to topographic effects are also observed. Results suggest that atmospheric stability and topography have significant influences on wind turbine performance and wake properties. Considering effects of atmospheric stability and topography will benefit the wind resource assessment in complex terrain.

Evaluation of Wind Energy Potential in a University Campus

Today, zero energy campus concept is a popular topic. Zero energy attempts are important for reducing global warming effect and greenhouse gas emissions. In the current study, wind characteristics and wind energy potential of Yildiz Technical University Davupasa Campus were investigated. Data from wind measurement station (10 m height) in campus area was examined. Due to the gaps in the measured data, one year long data was lumped by using all the measured data (25 months). The lumped data was used in all energy analyses. Mean wind speed was calculated as 3.45 m/s at 10 m and north northeast (30°) was defined as prevailing wind direction. Annual wind energy production and capacity factor for Fuhrlander FL 30 (30 kW-27 m hub height) wind turbine were estimated as 28.87 MWh and 10.9%, respectively. According to these results, an economically feasible wind energy project seems not possible. However, a possible wind energy project in Davutpasa campus will help to raise awareness of people. Therefore, it will help and promote to catch the aimed future wind energy capacity of Turkey.

Design Wind Speed Evaluation Technique in Wind Turbine Installation Point by Using the Meteorological and CFD Models

It is highly important in Japan to choose a good site for wind turbines, because the spatial distribution of wind speed is quite complicated over steep complex terrain. We have been developing the unsteady numerical model called the RIAM-COMPACT (Research Institute for Applied Mechanics, Kyushu University, Computational Prediction of Airflow over Complex Terrain). The RIAM-COMPACT is based on the LES (Large-Eddy Simulation). The object domain of the RIAM-COMPACT is from several m to several km, and can predict the airflow and gas diffusion over complex terrain with high precision. In the present paper, the design wind speed evaluation technique in wind turbine installation point by using the mesoscale meteorological model and RIAM-COMPACT CFD model was proposed. The design wind speed to be used for designing WTGs can be calculated by multiplying the ratio of the mean wind speed at the hub-height to the mean upper-air wind speed at the inflow boundary, i.e., the fractional increase of the mean hub-height wind speed, by the reduction ratio, R. The fractional increase of the mean hub-height wind speed was evaluated using the CFD simulation results. This method was proposed as Approach 1 in the present paper. A value of 61.9 m/s was obtained for the final design wind speed, Uh, in Approach 1. In the evaluation procedure of the design wind speed in Approach 2, neither the above-mentioned reduction rate, R, nor an upper-air wind speed of 1.7 Vo, where Vo is the reference wind speed, was used. Instead, the value of the maximum wind speed which was obtained from the typhoon simulation for each of the investigated wind directions was adopted. When the design wind speed was evaluated using the 50-year recurrence value, the design wind speed was 48.3 m/s. When a somewhat conservative safety factor was applied, that is, when the 100 year recurrence value was used instead, the design wind speed was 52.9 m/s.

Using Large-Eddy Simulations to Define Spectral and Coherence Characteristics of the Hurricane Boundary Layer for Wind-Energy Applications

Offshore wind-energy development is planned for regions where hurricanes commonly occur, such as the USA Atlantic Coast. Even the most robust wind-turbine design (IEC Class I) may be unable to withstand a Category-2 hurricane (hub-height wind speeds >50 m s^{-1}). Characteristics of the hurricane boundary layer that affect the structural integrity of turbines, especially in major hurricanes, are poorly understood, primarily due to a lack of adequate observations that span typical turbine heights (<200 m above sea level). To provide these data, we use large-eddy simulations to produce wind profiles of an idealized Category-5 hurricane at high spatial (10 m) and temporal (0.1 s) resolution. By comparison with unique flight-level observations from a field project, we find that a relatively simple configuration of the Cloud Model I model accurately represents the properties of Hurricane Isabel (2003) in terms of mean wind speeds, wind-speed variances, and power spectra. Comparisons of power spectra and coherence curves derived from our hurricane simulations to those used in current turbine design standards suggest that adjustments to these standards may be needed to capture characteristics of turbulence seen within the simulated hurricane boundary layer. To enable improved design standards for wind turbines to withstand hurricanes, we suggest modifications to account for shifts in peak power to higher frequencies and greater spectral coherence at large separations.

Technical and economic evaluation of energy production from wind in Istanbul and surrounds

Istanbul is located in the northwest part of Turkey and has the greatest population of any city in the country. Istanbul and its surroundings were researched for wind potential. Energy production from wind was evaluated technically and economically in this study. Çorlu, Kumköy and Şile sites were examined. Other sites in the same region (Göztepe, Kireçburnu, Bahçeköy, Florya and Kartal) were also evaluated but the results were not satisfactory, and for this reason, these sites were not examined in this study. When we checked daily, monthly and yearly wind speed values and frequency distributions, the wind energy potential of Çorlu, Kumköy and Şile were greater than other sites. Çorlu, Kumköy and especially Şile’s wind speed data are statistically decreasing. A Nordex N117 91m hub height wind turbine can produce 6099 MWh in Çorlu, 6459 MWh in Şile, 7265 MWh in Kumköy. A Nordex N117 140 m hub height wind turbine can produce 6471 MWh in Çorlu, 7439 MWh in Şile and 8175 MWh in Kumköy. The energy costs were calculated by the average of 36 years of wind measurement data using Nordex N117 turbines. Energy costs with Nordex N117 91 m and 140 m hub height in Çorlu, Şile and Kumköy are 0.025-0,027 US$/KWh, 0.022-0,025 US$/KWh, 0.020-0,023 US$/KWh, respectively.

Technical and economic evaluation of energy production from wind in Istanbul and surrounds

Istanbul is located in the northwest part of Turkey and has the greatest population of any city in the country. Istanbul and its surroundings were researched for wind potential. Energy production from wind was evaluated technically and economically in this study. Corlu, Kumkoy and Sile sites were examined. Other sites in the same region (Goztepe, Kirecburnu, Bahcekoy, Florya and Kartal) were also evaluated but the results were not satisfactory, and for this reason, these sites were not examined in this study. When we checked daily, monthly and yearly wind speed values and frequency distributions, the wind energy potential of Corlu, Kumkoy and Sile were greater than other sites. Corlu, Kumkoy and especially Sile’s wind speed data are statistically decreasing. A Nordex N117 91m hub height wind turbine can produce 6099 MWh in Corlu, 6459 MWh in Sile, 7265 MWh in Kumkoy. A Nordex N117 140 m hub height wind turbine can produce 6471 MWh in Corlu, 7439 MWh in Sile and 8175 MWh in Kumkoy. The energy costs were calculated by the average of 36 years of wind measurement data using Nordex N117 turbines. Energy costs with Nordex N117 91 m and 140 m hub height in Corlu, Sile and Kumkoy are 0.025-0,027 US$/KWh, 0.022-0,025 US$/KWh, 0.020-0,023 US$/KWh, respectively.

Calibrated Probabilistic Hub-Height Wind Forecasts in Complex Terrain

Abstract This work evaluates the use of a WRF ensemble for short-term, probabilistic, hub-height wind speed forecasts in complex terrain. Testing for probabilistic-forecast improvements is conducted by increasing the number of planetary boundary layer schemes used in the ensemble. Additionally, several prescribed uncertainty models used to derive forecast probabilities based on knowledge of the error within a past training period are evaluated. A Gaussian uncertainty model provided calibrated wind speed forecasts at all wind farms tested. Attempts to scale the Gaussian distribution based on the ensemble mean or variance values did not result in further improvement of the probabilistic forecast performance. When using the Gaussian uncertainty model, a small-sized six-member ensemble showed equal skill to that of the full 48-member ensemble. A new uncertainty model called the pq distribution that better fits the ensemble wind forecast error distribution is introduced. Results indicate that the gross attributes (central tendency, spread, and symmetry) of the prescribed uncertainty model are more important than its exact shape.

WRF Hub-Height Wind Forecast Sensitivity to PBL Scheme, Grid Length, and Initial Condition Choice in Complex Terrain

Abstract This study evaluates the sensitivity of wind turbine hub-height wind speed forecasts to the planetary boundary layer (PBL) scheme, grid length, and initial condition selection in the Weather Research and Forecasting (WRF) Model over complex terrain. Eight PBL schemes available for the WRF-ARW dynamical core were tested with initial conditions sources from the North American Mesoscale (NAM) model and Global Forecast System (GFS) to produce short-term wind speed forecasts. The largest improvements in forecast accuracy primarily depended on the grid length or PBL scheme choice, although the most important factor varied by location, season, time of day, and bias-correction application. Aggregated over all locations, the Asymmetric Convective Model, version 2 (ACM2) PBL scheme provided the best forecast accuracy, particularly for the 12-km grid length. Other PBL schemes and grid lengths, however, did perform better than the ACM2 scheme for individual seasons or locations.

A methodology for the design and testing of atmospheric boundary layer models for wind energy applications

Abstract. The GEWEX Atmospheric Boundary Layer Studies (GABLS) 1, 2 and 3 are used to develop a methodology for the design and testing of Reynolds-averaged Navier–Stokes (RANS) atmospheric boundary layer (ABL) models for wind energy applications. The first two GABLS cases are based on idealized boundary conditions and are suitable for verification purposes by comparing with results from higher-fidelity models based on large-eddy simulation. Results from three single-column RANS models, of 1st, 1.5th and 2nd turbulence closure order, show high consistency in predicting the mean flow. The third GABLS case is suitable for the study of these ABL models under realistic forcing such that validation versus observations from the Cabauw meteorological tower are possible. The case consists on a diurnal cycle that leads to a nocturnal low-level jet and addresses fundamental questions related to the definition of the large-scale forcing, the interaction of the ABL with the surface and the evaluation of model results with observations. The simulations are evaluated in terms of surface-layer fluxes and wind energy quantities of interest: rotor equivalent wind speed, hub-height wind direction, wind speed shear and wind direction veer. The characterization of mesoscale forcing is based on spatially and temporally averaged momentum budget terms from Weather Research and Forecasting (WRF) simulations. These mesoscale tendencies are used to drive single-column models, which were verified previously in the first two GABLS cases, to first demonstrate that they can produce similar wind profile characteristics to the WRF simulations even though the physics are more simplified. The added value of incorporating different forcing mechanisms into microscale models is quantified by systematically removing forcing terms in the momentum and heat equations. This mesoscale-to-microscale modeling approach is affected, to a large extent, by the input uncertainties of the mesoscale tendencies. Deviations from the profile observations are reduced by introducing observational nudging based on measurements that are typically available from wind energy campaigns. This allows the discussion of the added value of using remote sensing instruments versus tower measurements in the assessment of wind profiles for tall wind turbines reaching heights of 200 m.

Wind farm layout optimization under uncertainty

The optimal design of a wind farm with the aim of increasing the exploitation rate and power production is one of the challenging problems in the field of renewable energies. In this paper, the effect of using different hub height wind turbines in a farm on total power is studied. An exact mathematical formulation is presented in terms of the interaction matrix for multi-turbine wake effects considering different hub height wind turbines, and a new mixed-integer quadratic optimization model is developed. Then, the model is generalized to include the changes in wind characteristics and uncertain parameters. The model is solved using the linearization technique and an iterative method. The performance of the model and solution algorithm is evaluated by solving different problems borrowed from the literature. The computation results show the possibility of having a high-quality solution in a reasonable time in almost all cases.

Effects of atmospheric stability on wind resource characteristic parameters

The mean wind speed, wind speed distribution, wind shear exponent and wind power density would be affected by terrain, atmospheric stability and equipments, are important parameters in the process of wind resource assessment. However, Conventional methods have always ignored the effects of atmospheric stability, which might make results deviate from the actual situation. The main purpose of this work is to present effects of atmospheric stability on wind resource parameters. Taking 4 years’ measured data as case study, six parameters in wind resource assessing are calculated under different atmospheric stabilities; influential rules of above assessing parameters under varying atmospheric stability are analyzed respectively in order to obtain a systemic relationship between atmospheric stability and wind resource characteristic parameters which is tested by 5th year’s data. Based on these studies, a hub height wind speed extrapolation model is proposed. This model could use different wind shear exponent under various atmospheric stabilities during wind speed extrapolating, rather than an average wind shear for the whole wind farm. In such way, much accurate extrapolating hub wind speed in wind resource assessing is employed, and errors in wind resource assessment might decline. Results validate the simplicity, accuracy and practicality of the proposed method.

Prediction of Hub Height Winds over the Plateau Terrain by using WRF /YSU/Noah and Statistical Forecast

The forecast of wind energy is closely linked to the prediction of the variation of winds over very short time intervals. Four wind towers located in the Inner Mongolia were selected to understand wind power resources in the compound plateau region. The mesoscale weather research and forecasting combining Yonsei University scheme and Noah land surface model (WRF/YSU/Noah) with 1-km horizontal resolution and 10-min time resolution were used to be as the wind numerical weather prediction (NWP) model. Three statistical techniques, persistence, back-propagation artificial neural network (BP-ANN), and least square support vector machine (LS-SVM) were used to improve the wind speed forecasts at a typical wind turbine hub height (70 m) along with the WRF/YSU/Noah output. The current physical-statistical forecasting techniques exhibit good skill in three different time scales: (1) short-term (day-ahead); (2) immediate-short-term (6-h ahead); and (3) nowcasting (1-h ahead). The forecast method, which combined WRF/YSU/Noah outputs, persistence, and LS-SVM methods, increases the forecast skill by 26.3-49.4% compared to the direct outputs of numerical WRF/YSU/Noah model. Also, this approach captures well the diurnal cycle and seasonal variability of wind speeds, as well as wind direction. Predicción de vientos en una altiplanicie a la altura del eje con el esquema de la Universidad Yonsei/Modelo Superficie Terrestre Noah y la predicción estadísticaResumenLa estimación de la energía eólica está relacionada con la predicción en la variación de los vientos en pequeños intervalos de tiempo. Se seleccionaron cuatro torres eólicas ubicadas al interior de Mongolia para estudiar los recursos eólicos en la complejidad de un altiplano. Se utilizó la investigación climática a mesoscala y la combinación del esquema de la Universidad Yonsei con el Modelo de Superficie Terrestre Noah (WRF/YSU/Noah), con resolución de 1km horizontal y 10 minutos, como el modelo numérico de predicción meteorológica (NWP, del inglés Numerical Weather Prediction). Se utilizaron tres técnicas estadísticas, persistencia, propagación hacia atrás en redes neuronales artificiales y máquina de vectores de soporte-mínimos cuadrados (LS-SVM, del inglés Least Square Support Vector Machine), para mejorar la predicción de la velocidad del viento en una turbina con la altura del eje a 70 metros y se complementó con los resultados del WRF/YSU/Noah. Las técnicas de predicción físico-estadísticas actuales tienen un buen desempeo en tres escalas de tiempo: (1) corto plazo, un día en adelante; (2) mediano plazo, de seis días en adelante; (3) cercano, una hora en adelante. Este método de predicción, que combina los resultados WRF/YSU/Noah con los métodos de persistencia y LS-SVM incrementa la precisión de predicción entre 26,3 y 49,4 por ciento, comparado con los resultados directos del modelo numérico WRF/YSU/Noah. Además, este método diferencia la variabilidad de las estaciones y el ciclo diurno en la velocidad y la dirección del viento.

Improving the Accuracy of Wind Turbine Power Curve Validation by the Rotor Equivalent Wind Speed Concept

The measurement of the wind speed at hub height is part of the current IEC standard procedure for the power curve validation of wind turbines. The inherent assumption is thereby made that this measured hub height wind speed sufficiently represents the wind speed across the entire rotor area. It is very questionable, however, whether the hub height wind speed (HHWS) method is appropriate for rotor sizes of commercial state-of-the-art wind turbines. The rotor equivalent wind speed (REWS) concept, in which the wind velocities are measured at several different heights across the rotor area, is deemed to be better suited to represent the wind speed in power curve measurements and thus results in more accurate predictions of the annual energy production (AEP) of the turbine. The present paper compares the estimated AEP, based on HHWS power curves, of two different commercial wind turbines to the AEP that is based on REWS power curves. The REWS was determined by LiDAR measurements of the wind velocities at ten different heights across the rotor area. It is shown that a REWS power curve can, depending on the wind shear profile, result in higher, equal or lower AEP estimations compared to the AEP predicted by a HHWS power curve.

The Actuator Disc Concept in Phoenics

This study presents two models to simulate a wind turbine. This is done by employing the 1D momentum actuator disc theory in PHOENICS, a general purpose computational fluid dynamics software. To test the general applicability of these models, single wind turbine simulations are conducted using eight different wind turbine models from two manufacturers. The simulations are performed by imposing sheared inflow with hub height wind speeds ranging from 3 m/s up to 25 m/s. A range of computational parameters are investigated, including the resolution of the domain, the thickness of the actuator disc and the iterative convergence criteria. To investigate the wake development produced by these methods, a comparison study is performed with the more complex large-eddy simulation software EllipSys3D using an actuator disc approach for validation purposes. The resulting wind turbine thrust and power outputs from PHOENICS are compared with the experimental power curves and thrust values provided by the manufacturers for each wind turbine. The results show that actuator disc methods are able to provide a reasonable estimation of the conventional wind turbine power and thrust output with low computational effort. Moreover, the results from the preliminary comparison of the wake produced from these two rotor models compare well with the wake produced by the actuator disc in EllipSys3D.

An integrated wind-forecast system based on the weather research and forecasting model, Kalman filter, and data assimilation with nacelle-wind observation

To forecast wind speed at hub-height is a challenging task for wind energy application, especially in Japan where the terrain feature is very complex and large fluctuations are observed in surface wind field. In this study, an integrated system to predict the hub-height wind speed has been developed by combining data assimilation and Kalman filter with the high resolution Weather Research and Forecasting (WRF) model. Assimilating the nacelle wind data (quality-controlled) and the Kalman filter algorithm effectively improves accuracy of the WRF model forecast by optimizing initial condition and post-processing the model output, respectively. It is found that the WRF model forecasts can be markedly improved after assimilating the nacelle wind data through the Gridpoint Statistical Interpolation analysis system, with the relative improvements of 34.3%, 23.9%, and 8.8% in ME (mean error), RMSE (root mean square error), and IA (index of agreement), respectively. The implementation of the Kalman filter can significantly reduce ME and RMSE while increases the value of IA as well. Further improvement can be achieved if the Kalman filter and nacelle wind data assimilation are implemented simultaneously. It is observed that the role of the Kalman filter is more dominant for the wind band of rated out speeds, while data assimilation is effective in reducing the random errors and becomes more important in rare or extreme weather conditions. Both data assimilation and Kalman filter modules apply the nacelle wind data which is routinely available, so the system can be easily adopted in different wind farm sites for operational use.

Sensitivity of Turbine-Height Wind Speeds to Parameters in Planetary Boundary-Layer and Surface-Layer Schemes in the Weather Research and Forecasting Model

We evaluate the sensitivity of simulated turbine-height wind speeds to 26 parameters within the Mellor–Yamada–Nakanishi–Niino (MYNN) planetary boundary-layer scheme and MM5 surface-layer scheme of the Weather Research and Forecasting model over an area of complex terrain. An efficient sampling algorithm and generalized linear model are used to explore the multiple-dimensional parameter space and quantify the parametric sensitivity of simulated turbine-height wind speeds. The results indicate that most of the variability in the ensemble simulations is due to parameters related to the dissipation of turbulent kinetic energy (TKE), Prandtl number, turbulent length scales, surface roughness, and the von Karman constant. The parameter associated with the TKE dissipation rate is found to be most important, and a larger dissipation rate produces larger hub-height wind speeds. A larger Prandtl number results in smaller nighttime wind speeds. Increasing surface roughness reduces the frequencies of both extremely weak and strong airflows, implying a reduction in the variability of wind speed. All of the above parameters significantly affect the vertical profiles of wind speed and the magnitude of wind shear. The relative contributions of individual parameters are found to be dependent on both the terrain slope and atmospheric stability.

A simple hourly wind power simulation for the South-West region of Western Australia using MERRA data

A simple simulator capable of generating synthetic hourly values of wind power was developed for the South West region of Western Australia. The global Modern Era Retrospective Analysis for Research and Applications (MERRA) atmospheric database was used to calibrate the simulation with wind speeds 50 m above ground level. Analysis of the MERRA data indicated that the normalised residual of hourly wind speed had a double exponential distribution. A translated square-root transformation function yn=((1.96+ye)−1.4)/0.302 was used to convert this to a normal-like distribution so that autoregressive (AR) time series analysis could be used. There was a significant dependency in this time series on the last 3 h m so a third order AR model was used to generate hourly 50 m wind speed residuals. The MERRA daily average 50 m wind speed was found to have a Weibull-like distribution, so a square root conversion was used on the data to obtain a normal distribution. The time series for this distribution was found to have a significant dependency on the values for the last two days, so a second order AR model was also used in the simulation to generate synthetic time series values for the square root of the daily average wind speed. Seasonal, daily, diurnal, and hourly components were added to generate synthetic time series values of total 50 m wind speed. To scale this wind speed to turbine hub height, a time varying wind shear factor model was created and calibrated using measured data at a coastal and an inland site. Standard wind turbine power curves were modified to produce an estimate of wind farm power output from the hub-height wind speed. Comparison with measured grid supervisory control and data acquisition (SCADA) data indicated that the simulation generated conservative power output values. The simulation was compared to two other models: a Weibull distribution model, and an AR model with normally distributed residuals. The statistical fit with the SCADA data was found to be closer than these two models. Spatial correlation using only the MERRA data was found to be higher than the SCADA data, indicating that there is still a further source of variability to be accounted for. Hence the simulation spatial correlation was calibrated to previously reported findings, which were similar to the SCADA data.