Énergie éolienne aux États-Unis

Résumé

L’énergie éolienne aux États-Unis progresse rapidement dans le contexte de hausse des prix du pétrole et de réchauffement climatique, qui a poussé le gouvernement fédéral et de nombreux États à accorder des subventions à l'éolien, sous forme de crédits d'impôt et de certificats verts associés à des « normes de portefeuille d'énergie renouvelable » (Renewable portfolio standard - RPS). Les éoliennes ont produit 10,1 % de l'électricité du pays en 2022. Les États-Unis se classaient en 2020 au mondial derrière la Chine pour la production éolienne avec 21,4 % du total mondial. En 2022, le pays est au également pour la puissance installée éolienne avec 15,9 % du total mondial. Sa part de marché a été de 13,6 % en 2021 et de 11,1 % en 2022. La plupart des installations éoliennes sont construites dans le centre et le nord des États-Unis. En 2022, sept États ont tiré du vent plus de 30 % de leur électricité : l'Iowa (62,6 %), le Dakota du Sud (54,8 %), le Kansas (47,1 %), l'Oklahoma (43,6 %), le Dakota du Nord (36,7 %), le Nouveau-Mexique (35,4 %) et le Nebraska (31,0 %) ; six autres dépassent 20 % : le Colorado, le Maine, le Minnesota, le Texas, le Wyoming et le Vermont. 18 États ont dépassé le seuil de 10 % de production éolienne. Le gouvernement Biden a fixé un objectif de d'éolien en mer en 2030. Un total de est attendu sur cinq ans, faisant des États-Unis le troisième marché de l'offshore après la Chine et le Royaume-Uni. Le territoire américain offre un bon potentiel éolien, surtout sur la côte du Pacifique au nord de San Francisco, dans les Grands Lacs, dans les Appalaches, dans les îles Aléoutiennes et surtout sur le piémont des Montagnes Rocheuses où souffle le chinook. Le NREL fournit des cartes plus précises, selon l'altitude et la région et des cartes des ressources éoliennes offshore. Cependant, les ouragans peuvent représenter une menace pour les éoliennes, notamment dans les états du sud. La production d'électricité éolienne aux États-Unis atteint en 2022 (+15 %), soit 10,1 % de la production nationale d'électricité.

Source officielle

https://fr.wikipedia.org/wiki/Énergie_éolienne_aux_États-Unis

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In this study, large-eddy simulations (LESs) were performed to investigate the effects of changing wind direction on the turbine wakes and associated power losses in the Horns Rev offshore wind farm. In the LES framework, the turbulent subgrid-scale stresses are parameterized using a tuning-free Lagrangian scale-dependent dynamic model, and the turbine-induced forces are computed using a dynamic actuator-disk model with rotation (ADM-R). This dynamic ADM-R couples blade-element theory with a turbine-specific relation between the blade angular velocity and the shaft torque to compute simultaneously turbine angular velocity and power output. A total of 67 simulations were performed for a wide range of wind direction angles. Results from the simulations show a strong impact of wind direction on the spatial distribution of turbine-wake characteristics, such as velocity deficit and turbulence intensity. This can be explained by the fact that changing the wind angle can be viewed as changing the wind farm layout relative to the incoming wind, while keeping the same wind turbine density. Of particular importance is the effect of wind direction on the distance available for the wakes to recover and expand before encountering other downwind turbines (in full-wake or partial-wake interactions), which affects the power losses from those turbines. As a result, even small changes in wind direction angle can have strong impacts on the total wind farm power output. For example, a change in wind direction of just 10 degrees from the worst-case full-wake condition is found to increase the total power output by as much as 43%. This has important implications for the design of wind farms and the management of the temporal variability of their power output.

Mdpi Ag2013

Simulation of atmospheric flows over complex terrain for wind power potential assessment

When assessing the economic viability of a wind farm, the estimation of the on-site wind power potential is perhaps the most important step. The most common way of evaluating the wind power potential of an area of interest consists of making on-site measurements for a period of one year. In order to take account of the inter-annual variation of wind speed, the one year of data are normally correlated with data recorded at a reference site where long-term data (typically > 10 years) are available. A correlation analysis is formulated for the concurrent data sets at the reference and prediction sites. This correlation is then used to transform the long-term wind speed at the reference site to the long-term wind speed that would have been expected at the prediction site had long-term measurements been made at this site. An alternative approach is also used, which consists of establishing site-to-site relationships using a numerical model to simulate meteorological situations which are typical for the area of interest. These relationships are then used to transpose the known long-term wind statistics of the reference site to the prediction site. Such an approach is applied in this work to the region of Chasseral & Mt-Crosin. The wind data available for a period of 16 years at Chasseral are transposed to the Mt-Crosin site where they are then compared to the data measured at the location of the installed wind farm. Over complex terrain, the linearised models traditionally used for wind power potential assessment fail to reproduce accurate wind fields. Therefore, to be applied to mountainous terrain such as that found in Switzerland, the approach relying on numerical simulation requires the development and validation of a numerical tool capable of simulating wind fields over complex topography. As the numerical model would have to deal with relatively steep slopes requiring a fine horizontal (50-100 m) and vertical resolution (∼5-10 m in the lowest levels), a fluid dynamics model was used which solves the complete set of Navier-Stokes equations with κ-ε turbulence closure. The standard version of the model used (CFX4) is modified in a novel way to extend its field of application so that atmospheric phenomena could be simulated which are typical of the meso-scale. The modified version solves the flow equations with the anelastic approximation (deep Boussinesq) and assuming a background rotation of the wind field (with the high altitude wind field following the geostrophic approximation). In the first part of this work, the numerical model is validated. The results obtained in this phase show that for meteorological situations for which the wind at the ground is coupled to the high altitude wind, the numerical model is able to satisfactorily reproduce: the flow in the surface layer, reproducing the effects associated with the ground roughness, roughness change, or heat flux through the ground; the flow in the Ekman layer together with the interaction between the free flow thermal stability conditions and the boundary layer; the linear and non-linear effects associated with the perturbation induced by a mountain in a stably stratified flow. In the second stage of this work, an extension of the standard Measure-Correlate-Predict method is presented to calculate the wind speed distribution at the prediction site, from transposition relationships and from the wind statistics at the reference site. The validity of the underlying assumptions is confirmed using concurrent data sets that were collected at both the reference and prediction sites. To evaluate the accuracy that can be achieved with the transposition assumptions, a back-prediction is performed using the transposition relationships obtained using the observations. Different types of transposition relationships have been investigated. Finally, the transposition methodology is applied to calculate the wind speed conditions at Mt-Crosin from the Chassera1 data, using the transposition relationships calculated by the numerical model for a range of meteorological situations typical for the area considered. The Mt-Crosin to Chassera1 sector wind speed ratios calculated by the numerical model tend to slightly underestimate those observed. The mean wind speeds obtained from the transposition are underestimated by 7% to 18% at the three measuring mast locations on Mt-Crosin. The yearly energy output that can be produced by a wind turbine in these conditions is underestimated by 8% to 36%. For a further period, the actual energy production of the three installed wind turbines has been compared with the model prediction at hub height, which showed that the transposition results underestimate the actual yearly production by 22% to 24%. From the transposition of the long-term data at Chassera1 (16 years), with the relationships obtained by the numerical model, a wind power potential of between 470 MWh/year (Côte Est) and 596 MWh/year (Côte Nord) is predicted using the characteristics of a Vestas-V44 wind turbine. From the work presented here, it appears that for well-exposed sites such as those located along the Jura Crest, the methods developed are able to give a wind power potential prediction with a similar accuracy as a one year measurement campaign performed on site.

EPFL1998