WRF Model Methodology for Offshore Wind Energy Applications

Giannakopoulou, Evangelia-Maria; Nhili, Regis

doi:10.1155/2014/319819

Cited by 55 publications

(38 citation statements)

References 14 publications

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“…The negative wind direction bias represents an anticlockwise deviation. Other studies (Carvalho et al, 2014;Giannakopoulou and Nhili, 2014) have found similar wind direction biases. A possible reason for this systematic error is that WRF does not adequately resolve surface roughness, resulting in lower surface friction and leading to overly geostrophic winds (Mass and Ovens, 2011).…”

Section: Impact Of Nudging On Wind Statisticssupporting

confidence: 65%

“…A Galion4000 single-beam pulsed wind lidar from SgurrEnergy was used (Gottschall et al, 2009). Wind speed data were collected using the Doppler beam swinging (DBS) method (opening angle of 62 • ), which averaged multiple line-ofsight measurements at a constant elevation angle and four azimuth angles to calculate the 10 min mean wind speed at 40 range gates up to an altitude of about 1100 m. Reference measurement found the mean lidar error to be around 1 % with a standard deviation of 5 % (Gottschall, 2013). The resulting wind speed is inherently spatially and temporally averaged.…”

Section: Measurement Campaignmentioning

confidence: 99%

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Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

et al. 2019

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Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes above conventional wind turbines where reliable high-resolution wind data are scarce. Wind light detection and ranging (lidar) measurements and mesoscale models both have their advantages and disadvantages when assessing the wind resource at such heights. This study investigates whether assimilating measurements into the mesoscale Weather Research and Forecasting (WRF) model using observation nudging generates a more accurate, complete data set. The impact of continuous observation nudging at multiple altitudes on simulated wind conditions is compared to an unnudged reference run and to the lidar measurements themselves. We compare the impact on wind speed and direction for individual days, average diurnal variability and long-term statistics. Finally, wind speed data are used to estimate the optimal traction power and operating altitudes of AWES. Observation nudging improves the WRF accuracy at the measurement location. Close to the surface the impact of nudging is limited as effects of the air–surface interaction dominate but becomes more prominent at mid-altitudes and decreases towards high altitudes. The wind speed frequency distribution shows a multi-modality caused by changing atmospheric stability conditions. Therefore, wind speed profiles are categorized into various stability conditions. Based on a simplified AWES model, the most probable optimal altitude is between 200 and 600 m. This wide range of heights emphasizes the benefit of such systems to dynamically adjust their operating altitude.

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Section: Impact Of Nudging On Wind Statisticssupporting

confidence: 65%

Section: Measurement Campaignmentioning

confidence: 99%

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

et al. 2019

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“…According to the studies [13,33,34], the surface roughness and the different atmospheric stability conditions have a great influence on the vertical profile of winds and must be taken into account in the estimation of the wind at altitude. e two methods of wind speed extrapolation taking into account both of these parameters and used by [17,18,24,35] are in the first place of the log-linear law which is a similarity model function and secondly the power law.…”

Section: Methods Of Wind Speed Vertical Extrapolationmentioning

confidence: 99%

“…e smaller its value is, the closer it is to zero and the better the model is [48]. Another test was used and considered as a little more reliable because less affected by the most important prediction errors is the Mean Absolute Error (MAE) used in the studies of [34,50]:…”

Section: Model Validation Testmentioning

confidence: 99%

Vertical Profile of Wind Diurnal Cycle in the Surface Boundary Layer over the Coast of Cotonou, Benin, under a Convective Atmosphere

Donnou

Akpo

Kouchade

et al. 2019

Advances in Meteorology

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The characteristics of the wind vertical profile over the coast of Cotonou during wind convective diurnal cycle were explored in this study. Wind data at 10 m above the ground and the radiosonde data in the lower 60 m of the surface boundary layer were used over the period from January 2013 to December 2016. Based on Monin–Obukhov theory, the logarithmic and power laws have allowed characterizing the wind profile. The error estimators of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) were, respectively, evaluated at 0.025; 0.016 (RMSE; MAE) and 0.018; 0.015. At the site of Cotonou, the atmosphere is generally unstable from 09:00 to 18:00 MST and stable for the remainder of the time. The annual mean value of the wind shear coefficient is estimated at 0.20 and that of the ground surface roughness length and friction velocity are, respectively, of 0.007 m, 0.38 m·s−1. A comparative study between the wind extrapolation models and the data was carried out in order to test their reliability on our study site. The result of this is that whatever the time of the year is, only the models proposed (best fitting equation) are always in good agreement with the data unlike the other models evaluated. Finally, from the models suitable for our site, the profile of wind convective diurnal cycle was obtained by extrapolation of the wind data measured at 10 m from the ground. The average wind speed during this cycle is therefore evaluated to 8.07 m·s−1 for August which is the windiest month and to 4.98 m·s−1 for the least windy month (November) at 60 m of the ground. Considering these results, we can so consider that the site of Cotonou coastal could be suitable for the installation of wind turbines.

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“…However, wind power is also random and volatile, and any serious power disturbances can affect the safety and stability of wind-powered grids. As such, accurate wind power forecasting is necessary for creating reasonable generation plans and system backup arrangements [7][8][9]. Ultimately, the key to increasing the number of wind-powered grids is to improve the wind power penetration limit of power grids.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

Zang

Mian

et al. 2016

Advances in Meteorology

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Accurate short-term wind power forecasting is important for improving the security and economic success of power grids. Existing wind power forecasting methods are mostly types of deterministic point forecasting. Deterministic point forecasting is vulnerable to forecasting errors and cannot effectively deal with the random nature of wind power. In order to solve the above problems, we propose a short-term wind power interval forecasting model based on ensemble empirical mode decomposition (EEMD), runs test (RT), and relevance vector machine (RVM). First, in order to reduce the complexity of data, the original wind power sequence is decomposed into a plurality of intrinsic mode function (IMF) components and residual (RES) component by using EEMD. Next, we use the RT method to reconstruct the components and obtain three new components characterized by the fine-to-coarse order. Finally, we obtain the overall forecasting results (with preestablished confidence levels) by superimposing the forecasting results of each new component. Our results show that, compared with existing methods, our proposed short-term interval forecasting method has less forecasting errors, narrower interval widths, and larger interval coverage percentages. Ultimately, our forecasting model is more suitable for engineering applications and other forecasting methods for new energy.

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WRF Model Methodology for Offshore Wind Energy Applications

Cited by 55 publications

References 14 publications

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

Improving mesoscale wind speed forecasts using lidar-based observation nudging for airborne wind energy systems

Vertical Profile of Wind Diurnal Cycle in the Surface Boundary Layer over the Coast of Cotonou, Benin, under a Convective Atmosphere

Short-Term Wind Power Interval Forecasting Based on an EEMD-RT-RVM Model

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