Parc éolien

Résumé

thumb|Parc éolien de Neuenkirchen (Allemagne). thumb|Parc éolien de Tauern (Autriche). Une centrale éolienne, parc éolien, ou ferme éolienne, est un site regroupant plusieurs éoliennes produisant de l'électricité. Il se trouve dans un lieu où le vent est fort et/ou régulier. Parcs les plus importants vignette|centre|Parc de Guazhou (Chine).|upright=3

La ferme éolienne de Gansu, province du Gansu, en Chine, est le plus important parc du monde avec de puissance en novembre 2010. Il devrait atteindre une puissance totale de en 2020 Le parc de Guazhou intégré à ce projet comporte déjà, à lui seul, plus de 400 éoliennes, produisait en 2010 et devrait atteindre en 2015.
La centrale éolienne terrestre la plus importante d'Europe est celle de Sintfeld en Rhénanie-du-Nord-Westphalie (Allemagne), qui pourrait être supplantée par le projet luso-irlandais au Portugal qui prévoit une installation d'éoliennes près de Bragance d'une capacité de 400 à pouvant fournir 15 à 20 vi

Source officielle

https://fr.wikipedia.org/wiki/Parc_%C3%A9olien

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A new wake model and comparison of eight algorithms for layout optimization of wind farms in complex terrain

Roberto Brogna, Fernando Porté Agel

Layout optimization of wind farms constitutes an important and challenging task in complex terrain. This is especially due to the complex interactions of the boundary layer flows in complex terrain and wind turbine wakes, which renders wake modelling in complex terrain difficult. This study tackles this challenge with a new engineering wake model, which is developed by superposing a Gaussian shape wake model on top of the background flow field, assuming that the centerlines of wind turbine wakes follow the streamlines of the background flow field. The model is found to predict wind turbine wakes in complex terrain with good accuracy and at the same time it is computationally cheap to run for optimization applications. Comparisons with high fidelity simulations and field measurements for a real wind farm with 25 turbines in complex terrain demonstrate its effectiveness. A systematic comparison of eight optimization algorithms, which includes two gradient-based and six gradient-free algorithms, is also carried out for the layout optimization problem in complex terrain. To accelerate the optimization process, a double-stage approach is proposed, which optimizes the objective function first neglecting wake effects and then, in the second stage, including them. While all the tested optimization algorithms can improve the original wind farm layout, random search, local search, and pattern search are found to be the top three algorithms in terms of optimization results and computational cost.

ELSEVIER SCI LTD2020

Chargement

Switzerland has committed itself to an ambitious energy strategy. It aims to replace the exist- ing nuclear generation capacity with predominantly indigenous renewable resources. Wind power could play a significant role in this transition, yet the wind resource in the mountainous terrain that makes up most of the country is poorly understood. There are indications that this resource could be significant, but studies undertaken so far acknowledge large uncertain- ties. This is because the complex topography of the mountains influences the flow patterns significantly, and these can become partially decoupled from the synoptic flow aloft. This thesis aims to improve the understanding of the wind resource in highly complex terrain, and thereby contribute to a well informed energy transition in Switzerland. We start out by investigating the characteristics of the Swiss wind resource based on data from two meteorological measurement networks. From the pair-wise correlation between stations, it is concluded that wind farms across the country can be combined to produce a stable power output. It is also shown that elevation plays an important role in the wind resource, with the likelihood of sustained low wind speeds decreasing as a function of elevation, while mean speeds tend to increase with elevation. Next, a state of the art Numerical Weather Prediction model is assessed in its ability to simulate wind speeds over the Alps, and is shown to improve drastically upon existing mean wind speed estimates. This same model is then used to calculate the wind turbine capacity that is required to produce significant amounts of wind power, and it is found that the required capacity can be significantly reduced by allowing for wind turbines to be built at high elevations. In the last part of this thesis, smaller areas of the Alpine domain are simulated at high resolu- tions, to investigate the effect of increased model resolution on the accuracy and height of resource assessments. While it is found that optimal model parameterization is dependent on weather and terrain, strong indications of higher wind power potential are found with high resolution models compared to a model at lower resolution. This is explained by the fact that high resolutions are required to properly resolve the complex topography, which has a significant influence on the flow patterns and therefore, on the potential energy production.

EPFL2019

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The atmospheric boundary layer (ABL) undergoes substantial changes in its structure and dynamics in the course of a day due to the transient nature of forcing factors such as the surface fluxes of heat and momentum. The non- stationary nature of the mean wind and turbulence in the ABL, associated with the diurnal cycle, can in turn affect the structure of wind turbine wakes and their effects on power losses within wind farms. In this study, large-eddy simulation (LES) is used to study the evolution of the turbine wakes and their effects on power losses inside an idealized finite-size wind farm in the course of two full diurnal cycles. In the LES, turbulent subgrid-scale stresses are modeled using tuning-free Lagrangian scale-dependent dynamic models, while the turbine-induced forces are parameterized using a dynamic actuator disk model with rotation. To minimize the effects of the initial conditions on the results, our analysis is focused on the second diurnal cycle. The simulation results show a strong effect of atmospheric stability on the wind farm wakes and associated power losses. During the night, the relatively low turbulence intensity of the ambient ABL flow results in a relatively slow rate of entrainment of ambient flow into the wake and, consequently, a slow wake recovery. In contrast, during the day the positive buoyancy flux and associated turbulence production lead to a relatively high turbulence level in the background ABL flow, which enhances turbulent mixing and wake recovery. As a result, the averaged power deficit in the wind farm is found to increase with increasing thermal stability. In particular for that day, it increased from 27% under the most convective condition to about 61% under the most stable condition.

2014

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EPFL2019