Wind Energy in the UK: Progress and Future Expectations

Estimation of Weibull parameters for wind energy analysis across the UK

Journal of Renewable and Sustainable Energy ◽

10.1063/5.0038001 ◽

2021 ◽

Vol 13 (2) ◽

pp. 023303

Author(s):

Z. R. Shu ◽

Mike Jesson

Keyword(s):

Wind Energy ◽

Energy Analysis ◽

Weibull Parameters ◽

The Uk

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Evolution of trauma care in the UK: Current developments and future expectations

Injury ◽

10.1016/j.injury.2011.05.014 ◽

2011 ◽

Vol 42 (8) ◽

pp. 838-840 ◽

Cited By ~ 15

Author(s):

Graham Sleat ◽

Keith Willett

Keyword(s):

Trauma Care ◽

Future Expectations ◽

The Uk

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A Framework for the Selection of Optimum Offshore Wind Farm Locations for Deployment

Energies ◽

10.3390/en11071855 ◽

2018 ◽

Vol 11 (7) ◽

pp. 1855 ◽

Cited By ~ 7

Author(s):

Varvara Mytilinou ◽

Estivaliz Lozano-Minguez ◽

Athanasios Kolios

Keyword(s):

Decision Making ◽

Wind Energy ◽

Wind Farm ◽

Cost Effective ◽

Optimization Techniques ◽

Offshore Wind ◽

Moray Firth ◽

The Uk ◽

The Given ◽

Nondominated Sorting

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

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The challenges of high wind energy penetration in the UK power system

2014 International Conference on Power System Technology ◽

10.1109/powercon.2014.6994004 ◽

2014 ◽

Cited By ~ 4

Author(s):

David Infield ◽

Lei Wu

Keyword(s):

Power System ◽

Wind Energy ◽

High Wind ◽

The Uk

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Wind energy

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2006.1955 ◽

2007 ◽

Vol 365 (1853) ◽

pp. 957-970 ◽

Cited By ~ 12

Author(s):

W.E Leithead

Keyword(s):

Wind Energy ◽

Current Drive ◽

Department Of Energy ◽

The Us ◽

Technical Issues ◽

The Uk ◽

Renewable Energy Generation ◽

Expected Increase ◽

Eu Commission ◽

The Eu

From its rebirth in the early 1980s, the rate of development of wind energy has been dramatic. Today, other than hydropower, it is the most important of the renewable sources of power. The UK Government and the EU Commission have adopted targets for renewable energy generation of 10 and 12% of consumption, respectively. Much of this, by necessity, must be met by wind energy. The US Department of Energy has set a goal of 6% of electricity supply from wind energy by 2020. For this potential to be fully realized, several aspects, related to public acceptance, and technical issues, related to the expected increase in penetration on the electricity network and the current drive towards larger wind turbines, need to be resolved. Nevertheless, these challenges will be met and wind energy will, very likely, become increasingly important over the next two decades. An overview of the technology is presented.

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Discourses of on-farm wind energy generation in the UK farming press

Journal of Environmental Policy & Planning ◽

10.1080/1523908x.2016.1224157 ◽

2016 ◽

Vol 19 (4) ◽

pp. 391-407 ◽

Cited By ~ 3

Author(s):

Kirsty L. Holstead ◽

Carlos Galán-Díaz ◽

Lee-Ann Sutherland

Keyword(s):

Wind Energy ◽

Energy Generation ◽

Wind Energy Generation ◽

The Uk ◽

On Farm

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Impact of weather regimes on wind power variability in western Europe

10.5194/egusphere-egu2020-18797 ◽

2020 ◽

Author(s):

Ricardo García-Herrera ◽

Jose M. Garrido-Perez ◽

Carlos Ordóñez ◽

David Barriopedro ◽

Daniel Paredes

Keyword(s):

High Resolution ◽

Wind Speed ◽

Wind Energy ◽

Wind Power ◽

Large Scale ◽

Western Europe ◽

Power Production ◽

Large Scale Circulation ◽

Weather Regimes ◽

The Uk

We have examined the applicability of a new set of 8 tailored weather regimes (WRs) to reproduce wind power variability in Western Europe. These WRs have been defined using a substantially smaller domain than those traditionally used to derive WRs for the North Atlantic-European sector, in order to maximize the large-scale circulation signal on wind power in the region of study. Wind power is characterized here by wind capacity factors (CFs) from a meteorological reanalysis dataset and from high-resolution data simulated by the Weather Research and Forecasting (WRF) model. We first show that WRs capture effectively year-round onshore wind power production variability across Europe, especially over northwestern / central Europe and Iberia. Since the influence of the large-scale circulation on wind energy production is regionally dependent, we have then examined the high-resolution CF data interpolated to the location of more than 100 wind farms in two regions with different orography and climatological features, the UK and the Iberian Peninsula. The use of WRs allows discriminating situations with varied wind speed distributions and power production in both regions. In addition, the use of their monthly frequencies of occurrence as predictors in a multi-linear regression model allows explaining up to two thirds of the month-to-month CF variability for most seasons and sub-regions. These results outperform those previously reported based on Euro-Atlantic modes of atmospheric circulation. The improvement achieved by the spatial adaptation of WRs to a relatively small domain seems to compensate for the reduction in explained variance that may occur when using yearly as compared to monthly or seasonal WR classifications. In addition, our annual WR classification has the advantage that it allows applying a consistent group of WRs to reproduce day-to-day wind speed variability during extreme events regardless of the time of the year. As an illustration, we have applied these WRs to two recent periods such as the wind energy deficit of summer 2018 in the UK and the surplus of March 2018 in Iberia, which can be explained consistently by the different combinations of WRs.

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