Evaluation of a semi-empirical model for predicting the wind energy resource relevant to small-scale wind turbines

Weekes, S.M.; Tomlin, Alison S.

doi:10.1016/j.renene.2012.06.053

Cited by 24 publications

(42 citation statements)

References 11 publications

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“…This study has used a Weibull distribution to approximate the annual wind speed distribution since it is representative of a large number of wind regimes [28].…”

Section: Resource At Installation Sitementioning

confidence: 99%

“…For simplicity of analysis, a general shape factor representative of potential UK installation sites was determined as k=1.89 and scale factor or c = 1.124, based upon a study of a number of UK sites in rural, coastal, suburban and urban settings [28]. The expected annual generation for a variety of average annual wind speeds has been calculated using the power curves published by the manufacturers (Table 4).…”

Section: Resource At Installation Sitementioning

confidence: 99%

“…It should also be noted that empirical studies from across the UK have shown that average annual wind speeds are unlikely to reach the higher end of this range apart from in the most ideal locations at over 10 m above canopy height [28,31]. Therefore, most installation sites would have average wind speeds of 6 m/s or less.…”

Section: Current Carbon Intensity Of Micro-wind Turbinesmentioning

confidence: 99%

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The carbon payback of micro-generation: An integrated hybrid input–output approach

Bush¹,

Jacques²,

Scott³

et al. 2014

Applied Energy

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Feed-in tariffs (FiTs) in the UK have been introduced to stimulate growth in small-scale renewables such as photovoltaics and micro-wind. They form one of the UK's key policies to decarbonise electricity by 2030. However, the evidence used to inform the policy was predominantly related to costs, capacity and deployment; not contribution to meeting decarbonisation targets. This paper employs an integrated hybrid lifecycle assessment method, which overcomes boundary limitations of traditional process-based assessments, to measure the full lifecycle emissions of solar PV and micro-wind technologies eligible under FiTs. Environmental assessments of policies often don't take account of the lifecycle emissions of technologies, therefore underestimating their emissions contribution and overestimating the success of policies towards decarbonisation targets.Considering the full lifecycle emissions, the paper assesses the effectiveness of FiTs for driving the UK's low carbon transition. The results demonstrate that, while there is still significant variation and uncertainty with such estimates, even with the most conservative figures, both the technologies can offer substantial emission savings compared to fossil fuel alternatives when installed in suitable locations. However, the renewable resource of installation sites is critical to the carbon intensity that the technologies can offer. Under a poor renewable resource their impacts can be as high as fossil fuels alternatives. As FiTs makes no distinction between installation sites this should form part of the assessment of funding. Finally, despite their potential for carbon reduction, with the full lifecycle of the considered technologies taken into account, a target of 50 gCO 2 e/kWh is not possible with the current technology generation efficiencies. The paper concludes that a complete re-assessment of the role of technologies in the decarbonisation of electricity is required to take into account the full lifecycle impacts to gain a more realistic picture of the mitigation potential. Highlights We apply novel lifecycle analysis methodology to solar PV and micro-wind technologies  We explore the implications of lifecycle emissions for meeting decarbonisation targets  A 50 gCO 2 e/kWh target cannot be achieved for the case studies considered  Availability of renewable resource is critical to the carbon intensity of electricity generation 2  The effectiveness of feed-in tariffs for driving decarbonisation in the UK is evaluated

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“…This study has used a Weibull distribution to approximate the annual wind speed distribution since it is representative of a large number of wind regimes [28].…”

Section: Resource At Installation Sitementioning

confidence: 99%

Section: Resource At Installation Sitementioning

confidence: 99%

Section: Current Carbon Intensity Of Micro-wind Turbinesmentioning

confidence: 99%

See 1 more Smart Citation

The carbon payback of micro-generation: An integrated hybrid input–output approach

Bush¹,

Jacques²,

Scott³

et al. 2014

Applied Energy

View full text Add to dashboard Cite

show abstract

“…These were used to calculate the error metrics described in Section 2.6. Since the angular dependent upwind roughness can affect the scaling between the reference and target site wind speeds [8], the BW approach was implemented using wind data binned into 90 0 angular sectors with respect to the reference site wind direction, except when investigating the convergence efficiency (Section 3.1) where no binning was applied. This sector width was chosen based on the performance of the BW approach for sector widths of 30 0 -360 0 .…”

Section: Application Of the Bivariate Weibull Probability Approachmentioning

confidence: 99%

“…For small-scale installations, a long-term measurement campaign may not be practical or financially viable and developers may rely on wind maps, empirical correction factors [6] or boundary layer scaling approaches [7,8]. MCP applied to very short-term measurement periods may also be a viable approach [9] providing the performance of the techniques as a function of the measurement period has been investigated.…”

Section: Introductionmentioning

confidence: 99%

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

Weekes

Tomlin

2014

Renewable Energy

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Highlights Measure-correlate-predict approach based on bivariate Weibull probability tested at 22 sites  Deviations from ideal bivariate Weibull behaviour investigated using observed and artificial data  Error metrics calculated using 120 test periods over an 11 year data record  Performance compared to existing regression methods using variable onsite measurement periods Keywords: measure-correlate-predict, wind resource assessment, bivariate Weibull distribution Abstract A detailed investigation of a measure-correlate-predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites has been conducted. Since wind speeds are typically assumed to follow Weibull distributions, this approach has a stronger theoretical basis than widely used regression MCP techniques. Building on previous work that applied the technique to artificially generated wind data, we have used long-term (11 year) wind observations at 22 pairs of correlated UK sites. Additionally, 22 artificial wind data sets were generated from ideal BW distributions modelled on the observed data at the 22 site pairs. Comparison of the fitting efficiency revealed that significantly longer data periods were required to accurately extract the BW distribution parameters from the observed data, compared to artificial wind data, due to seasonal variations. The overall performance of the BW approach was compared to standard regression MCP techniques for the prediction of the 10 year wind resource using both observed and artificially generated wind data at the 22 site pairs for multiple short-term measurement periods of 1-12 months. Prediction errors were quantified by comparing the predicted and observed values of mean wind speed, mean wind power density, Weibull shape factor and standard deviation of wind speeds at each site. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods.When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods. This suggests that real wind observations at correlated sites may differ from ideal BW distributions and hence regression approaches, which require less fitting parameters, may be more appropriate, particularly when using short measurement periods.

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Wind Energy Potential (Measurements, Evaluation, Forecasting)

Axaopoulos¹,

Tzanes²

2022

Comprehensive Renewable Energy

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Evaluation of a semi-empirical model for predicting the wind energy resource relevant to small-scale wind turbines

Cited by 24 publications

References 11 publications

The carbon payback of micro-generation: An integrated hybrid input–output approach

The carbon payback of micro-generation: An integrated hybrid input–output approach

Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP

Wind Energy Potential (Measurements, Evaluation, Forecasting)

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