Énergie éolienne au Royaume-Uni

Résumé

thumb|Petite ferme éolienne offshore (Burbo Bank), dans la lumière d'un coucher de soleil automnal. L'énergie éolienne est devenue une source d'énergie électrique de premier plan au Royaume-Uni, qui dispose d'un potentiel éolien parmi les meilleurs au monde : l'éolien fournissait 24,2 % de la production électrique britannique en 2020 et 20,8 % en 2021, et sa progression est rapide. Le rythme d'installation s'accélère pour les éoliennes en mer (55 % de la production éolienne de 2021) alors qu'il chute pour les éoliennes terrestres. Le Royaume-Uni était en 2021 au européen pour la production d'électricité éolienne, après l'Allemagne, et au mondial (3,5 % du total mondial) derrière la Chine, les États-Unis, l'Allemagne et l'Inde. Depuis 2019, la production en mer l'emporte sur la production à terre. Pour la puissance installée, il était en 2022 au mondial avec 3,1 % du total mondial ; sur le segment offshore, il tient le européen et le mondial (21,6 % du total mondial), derrière

Source officielle

https://fr.wikipedia.org/wiki/%C3%89nergie_%C3%A9olienne_au_Royaume-Uni

À propos de ce résultat

Cette page est générée automatiquement et peut contenir des informations qui ne sont pas correctes, complètes, à jour ou pertinentes par rapport à votre recherche. Il en va de même pour toutes les autres pages de ce site. Veillez à vérifier les informations auprès des sources officielles de l'EPFL.

Publications associées (5)

Publications associées

Chargement

Personnes associées (1)

Personnes associées

Chargement

Unités associées (2)

Unités associées

Chargement

Concepts associés (10)

Concepts associés

Chargement

Cours associés (3)

Les étudiants comprennent les problématiques principales du développement territorial. Les outils d'aménagement du territoire (du niveau fédéral au niveau communal) leur sont familiers. Ils connaissent les démarches foncières ainsi que les aspects financiers relatifs à un projet territorial.

Interactive course addressing bulk and thin-film magnetic materials that provide application-specific functionalities in different modern technologies such as e.g. wind energy harvesting, electric article surveillance, spintronics, sensing, and data storage.

L'analyse critique de stratégies énergétiques nécessite de comprendre les différentes filières énergétiques impliquées dans l'approvisionnement en énergie et leurs impacts sur l'économie, l'environnement et la société. Le cours introduit à la formalisation de problèmes de stratégies énergétiques.

Cours associés

Chargement

Séances de cours associées (3)

Séances de cours associées

Chargement

A momentum-conserving wake superposition method for wind farm power prediction

Fernando Porté Agel, Haohua Zong

Analytical wind turbine wake models and wake superposition methods are prevailing tools widely adopted by the wind energy community to predict the power production of wind farms. However, none of the existing wake superposition methods conserve the streamwise momentum. In this study, a novel wake superposition method capable of conserving the total momentum deficit in the streamwise direction is derived theoretically, and its performance is validated with both particle imaging velocimetry measurements and large-eddy simulation results. Detailed inter-method comparisons show that the novel wake superposition method outperforms all the existing methods by delivering an accurate prediction of the power production and the centreline wake velocity deficit, with a typical error of less than 5% (excluding the near-wake region). Additionally, the momentum-conserving wake superposition method is extended to combine the transverse velocities induced by yawed wind turbines, and the secondary wake steering effect crucial to the power optimization in active wake control is well reproduced.

CAMBRIDGE UNIV PRESS2020

Chargement

, ,

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

2021

Chargement

A new analytical model for wind farm power prediction

Amin Niayifar, Fernando Porté Agel

In this study, a new analytical approach is presented and validated to predict wind farm power production. The new model is an extension of the recently proposed by Bastankhah and Porte-Agel for a single wake. It assumes a self-similar Gaussian shape of the velocity deficit and satisfies conservation of mass and momentum. To estimate the velocity deficit in the wake, this model needs the local wake growth rate parameter which is calculated based on the local turbulence intensity in the wind farm. The interaction of the wakes is modeled by use of the velocity deficit superposition principle Finally, the power curve is used to estimate the power production from the wind turbines. The wind farm model is compared to large-eddy simulation (LES) data and measurments of Horns Rev wind farm for a wide range of wind directions. Reasonable agreement between the proposed analytical model, LES data and measurments is obtained. This prediction is also found to be substantially better than the one obtained with a commonly used wind farm wake model.

Iop Publishing Ltd2015

Chargement