Wind power in the United Kingdom

Summary

The United Kingdom is the best location for wind power in Europe and one of the best in the world. By 2023, the UK had over 11 thousand wind turbines with a total installed capacity of 28 gigawatts (GW): 14 GW onshore and 14 GW offshore, the sixth largest capacity of any country. Wind power generated about 25% of UK electricity, having surpassed coal in 2016 and nuclear in 2018. It is the largest source of renewable electricity in the UK. Polling of public opinion consistently shows strong support for wind power in the UK, with nearly three-quarters of the population agreeing with its use, even for people living near onshore wind turbines. The government has committed to a major expansion of offshore capacity to 50 GW by 2030, with 5GW from floating wind. One reason for this is to improve energy security. History The world's first electricity generating wind turbine was a battery

Official source

https://en.wikipedia.org/wiki/Wind_power_in_the_United_Kingdom

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (5)

Related publications

Related people (1)

Related people

Related units (2)

Related units

Related concepts (10)

Related concepts

Related courses (3)

Related courses

Related lectures (3)

Related lectures

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

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Lorenzo Donadio, Jiannong Fang, Fernando Porté Agel

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

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

Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Lorenzo Donadio, Jiannong Fang, Fernando Porté Agel

2021