Bernhard Lange

Comparison of Wake Model Simulations with Offshore Wind Turbine Wake Profiles Measured by Sodar

Abstract This paper gives an evaluation of most of the commonly used models for predicting wind speed decrease (wake) downstream of a wind turbine. The evaluation is based on six experiments where free-stream and wake wind speed profiles were measured using a ship-mounted sodar at a small offshore wind farm. The experiments were conducted at varying distances between 1.7 and 7.4 rotor diameters downstream of the wind turbine. Evaluation of the models compares the predicted and observed velocity deficits at hub height. A new method of evaluation based on determining the cumulative momentum deficit over the profiles is described. Despite the apparent simplicity of the experiments, the models give a wide range of predictions. Overall, it is not possible to establish any of the models as having individually superior performance with respect to the measurements.

The influence of thermal effects on the wind speed profile of the coastal marine boundary layer

The wind speed profile in a coastal marine environment is investigated with observations from the measurement program Rødsand, where meteorological data are collected with a 50 m high mast in the Danish Baltic Sea, about 11 km from the coast. When compared with the standard Monin—Obukhov theory the measured wind speed increase between 10 m and 50 m height is found to be systematically larger than predicted for stable and near-neutral conditions. The data indicate that the deviation is smaller for short (10–20 km) distances to the coast than for larger (>30 km) distances.The theory of the planetary boundary layer with an inversion lid offers a qualitative explanation for these findings. When warm air is advected over colder water, a capping inversion typically develops. The air below is constantly cooled by the water and gradually develops into a well-mixed layer with near-neutral stratification. Typical examples as well as scatter plots of the data are consistent with this explanation. The deviation of measured and predicted wind speed profiles is shown to be correlated with the estimated height and strength of the inversion layer.

Comparison of Wake Models with Data for Offshore Windfarms

A major objective of the ENDOW project is to evaluate the performance of wake models in offshore windfarm environments in order to ascertain the improvements required to enhance the prediction of power output within large offshore wind farms [1]. The strategy for achieving this is to compare the performance of the models in a wide range of conditions which are expected to be encountered during turbine operation offshore. Six models of varying complexity have been evaluated initially against the Vindeby single wake data in [2] where it was found that almost all of them overestimate the wake effects and also significant inconsistencies between the model predictions appeared in the near wake and turbulence intensity results. Based on the conclusions of that study, the ENDOW wake modeling groups have already implemented a number of modifications to their original models. In the present paper, new single wake results are presented against experimental data at Vindeby and Bockstigen wind farms. Clearly, some of the model discrepancies previously observed in Vindeby cases have been smoothed and overall the performance is improved.

On Detection of a Wave Age Dependency for the Sea Surface Roughness

The wave age dependency of the nondimensional sea surface roughness (also called the Charnock parameter) is investigated with data from the new field measurement program at Rodsand in the Danish Baltic Sea. An increasing Charnock parameter with inverse wave age is found, which can be described by a power-law relation. Friction velocity is a common quantity in both the Charnock parameter and wave age. Thus self-correlation effects are unavoidable in the relation between them. The significance of self-correlation is investigated by employing an artificial “dataset” with randomized wave parameters. It is found that self-correlation severely influences the relation. For the Rodsand dataset the difference between real and randomized “data” was found to be within the measurement uncertainty. By using a small subset of the data it was found that the importance of self-correlation increases for a narrower range of wave age values. This supports the conclusion of Johnson et al. that because of the scatter and self-correlation problems the coefficients of the power-law relation can only be obtained from the analysis of an aggregated dataset with a wide wave age range combining measurements from several sites. The dependency between wave age and sea roughness has been discussed extensively in the literature with different and sometimes conflicting results. A wide range of coefficients has been found for the power-law relation between the Charnock parameter and wave age for different datasets. It is shown that self-correlation contributes to such differences, since it depends on the range of wave age values present in the datasets. Also, data are often selected for rough flow conditions with the Reynolds roughness number. It is shown that for datasets with large scatter this can lead to misleading results with regard to the relationship between wave age and Charnock parameter. Two different methods to overcome this problem are presented.

Application of wind power prediction tools for power system operations

The wide use of wind energy in Germany results in a lot of new power system operation problems corresponding especially to the stochastic character of the wind speed and to the not controllable production of energy. The significant amount of installed wind power in the German power system (currently more than 17 GW) make the traditional scheduling of the power generation for the next day very unsure. Consequently the costs of the power system operation are high because of a large scale provision of spinning reserve power coming from the traditional power plants. The decisive rule in the decreasing of these costs plays the exactness of the wind energy transformation modelling process which starts with the forecast of wind speed. In Germany since more than ten years the knowledge how to solve this problem is available. Based on more than 100 representative wind farm power measurements all over Germany very exact models for the determination of the current and expected wind power are developed. The models are in operation at the control stations of the Transmission System Operators

Improvement of the Power System Reliability by Prediction of Wind Power Generation

The integration of wind farms into the electricity grid has become an important challenge for the utilization and control of electric power systems, because of the fluctuating and intermittent behaviour of wind power generation. Wind power predictions improve the economical and technical integration of large capacities of wind energy into the existing electricity grid. Trading, balancing, grid operation and safety increase the importance of forecasting electrical outputs from wind farms. Thus wind power forecast systems have to be integrated into the control room of the transmission system operator (TSO). Very high requirements of reliability and safety make this integration especially challenging. The pooling of several large offshore wind farms into clusters in the GW range will make new options feasible for an optimized integration of wind power. The geographically distributed onshore wind farms will be aggregated to clusters, for the purpose of operating these wind farms as one large (virtual) wind power plant. For this purpose, a new structure, the wind farm cluster will be introduced. All wind farms, which are directly or indirectly connected to one transmission network node will be associated to one wind farm cluster. The wind farm cluster manager (WCM) assists the TSO by operating the cluster according to the requirements of the power transmission system. Non-controllable wind farms within a wind farm cluster are supported by controllable ones.

ENDOW: Improvement of Wake Models within Offshore Wind Farms

The partners in the ENDOW (Efficient Development of Offshore Windfarms) project are validating, testing, designing and improving wind farm design tools for the efficient design of offshore wind farms. The different meteorological conditions offshore constitute a challenge for the current design tools and models because they have been developed and tested primarily for the design of land based wind farms. Measured downwind of turbines, wake-affected wind speed profiles at Vindeby offshore wind farm have been compared with the model predictions for single, double and quintuple wake cases. The modelling groups have based on these results adjusted their wake models for offshore wind farm design. This paper presents the data, model comparisons and the improvements to the models.

ENDOW: Efficient Development of Offshore Windfarms

The objective of the ENDOW project is to evaluate, enhance and interface wake and boundary-layer models for utilisation in developing offshore windfarms. The model hierarchy will form the basis of design tools for use by wind energy developers and turbine manufacturers to optimise power output from offshore wind farms through minimised wake effects immediately downwind of wind turbines and optimal grid connections. The initial focus of the project was to use databases from existing offshore wind farms (Vindeby and Bockstigen) for the first comprehensive evaluation of offshore wake model performances. The six wake models vary in complexity from empirical solutions to the most advanced models based on solutions of the Navier-Stokes equations using eddy viscosity or k-epsilon turbulence closure. One of the wake models is also being coupled with a full aeroelastic model for the calculation of wind loads on the turbines. Parallel research includes comparison of a local-scale stability/roughness model with a mesoscale model focusing on boundary-layer development within and over a large offshore wind farm, and particularly the influence of large scale thermal flows. A new experiment was conducted using SODAR immediately downwind of offshore wind turbines to examine vertical wind speed profiles to hub-height and beyond in near-wake conditions and wake dispersion to assist in model development and evaluation.

Large Off-Shore Windfarms: Linking Wake Models with Atmospheric Boundary Layer Models

Gerard Schepers1, Rebecca Barthelmie2, Kostas Rados3, Bernhard Lange4 and Wolfgang Schlez5 1Energy Research Centre,Wind Energy, 1755 ZG Petten,The Netherlands. E-mail 2Wind Energy Dept., Riso National Laboratory, 40 0 0 Roskilde, Denmark 3The Robert Gordon University, School of Mechanical and Offshore Engineering,Aberdeen AB10 1FR, Scotland 4Dept. of Energy and Semiconductor Research EHF,University of Oldenburg,D-26111 Oldenburg,Germany 5Wolfgang Schlez, Garrad Hassan and Partners Ltd, Bristol BS2 0 QD, UK

Predicting Wind Speed for Wind Energy; Progress of the WINDENG Project:

The suitability of the computer model MM5 for predicting wind speed, and hence wind energy, is investigated by performing simulations for different geographical regions. The focus is on wind speed in the lowest 200 m of the planetary boundary layer (PBL). The dependency of the simulated wind speed on PBL parameterization and atmospheric stability is studied. The smallest deviation between measured and simulated wind speed, averaged over a three-day period, is 1% and occurs for an off-shore simulation with unstable stratification. The largest deviations of 31% and 20% occur with orographically structured terrain, stable stratification and weak synoptic forcing. The results suggest that unstable conditions are simulated with better accuracy by MM5. Changes of the PBL scheme cause wind speed variations between 9% and 40% of the average wind speed. None of the PBL schemes is clearly the best and their performance can strongly vary for different conditions. Nevertheless, the Mellor-Yamada-Janjic (ETA) and the Blackadar PBL parameterization (BLK) schemes seem to be the most suitable schemes for wind energy applications. Additionally, MM5 was successfully adapted for idealised, stationary simulations in order to calculate a wind-climatology for Sardinia using a statistical-dynamical downscaling approach.