Current issues in wind energy meteorology

Emeis, Stefan

doi:10.1002/met.1472

Cited by 71 publications

(51 citation statements)

References 77 publications

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“…Using this expression causes estimation errors, often an underestimation of the actual power produced, as seen in the time series in Figures 1-3 above, and as documented in the other studies highlighted, especially when considering hourly averaged wind power. These errors have been attributed to some energy meteorology parameters such as shear [37], turbine age [11], atmospheric conditions [9], ambient turbulence [38], thermal effects and surface roughness [39], although there are limited scholarly articles to validate and quantify these impacts.…”

Section: Estimations Using the Effective Power Curvementioning

confidence: 99%

Assessment and Performance Evaluation of a Wind Turbine Power Output

Abolude

Zhou

2018

Energies

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Estimation errors have constantly been a technology bother for wind power management, often time with deviations of actual power curve (APC) from the turbine power curve (TPC). Power output dispersion for an operational 800 kW turbine was analyzed using three averaging tine steps of 1-min, 5-min, and 15-min. The error between the APC and TPC in kWh was about 25% on average, irrespective of the time of the day, although higher magnitudes of error were observed during low wind speeds and poor wind conditions. The 15-min averaged time series proved more suitable for grid management and energy load scheduling, but the error margin was still a major concern. An effective power curve (EPC) based on the polynomial parametric wind turbine power curve modeling technique was calibrated using turbine and site-specific performance data. The EPC reduced estimation error to about 3% in the aforementioned time series during very good wind conditions. By integrating statistical wind speed forecasting methods and site-specific EPCs, wind power forecasting and management can be significantly improved without compromising grid stability.

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Section: Estimations Using the Effective Power Curvementioning

confidence: 99%

Assessment and Performance Evaluation of a Wind Turbine Power Output

Abolude

Zhou

2018

Energies

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“…The application of the wind shear log-law assumes neutral atmospheric stability and is designed up to heights of 100 m, with evidence of inaccuracies above [13] and proposed methods to overcome this limitation [14]. The latter method requires empirical knowledge of a site, presenting challenges to adequately address in this paper, thus for simplicity the shear log-law is applied above 100 m. The annual energy production, AEP , of a wind farm is estimated by:…”

Section: Yield Modellingmentioning

confidence: 99%

“…The detailed yield modelling methodology of P Sim (v a ) can be accessed in the literature [3]. Wake losses depend on multiple factors, such as terrain topology, wind distribution, atmospheric stability, turbine thrust coefficient, C t , spacing and the array layout [11,13,[16][17][18]; however, in this paper the wind farm with the most turbines (36 x 500 kW) is assumed to experience a medium wake loss of 10%. Each of the modelled wind farm wake losses are scaled linearly with the number of turbines deployed in the respective wind farm.…”

Section: Yield Modellingmentioning

confidence: 99%

The Effect of Upscaling and Performance Degradation on Onshore Wind Turbine Lifetime Extension Decision Making

Rubert

McMillan

Niewczas

2017

J. Phys.: Conf. Ser.

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Abstract. Ever greater rated wind turbine generators (WTGs) are reaching their end of design life in the near future. In addition, first research approaches quantified the impact of long-term performance degradation of WTGs. As a consequence, this work is aimed at discussing and analysing the impact of upscaling and performance degradation on the economics of wind turbine lifetime extension. Findings reveal that the lifetime extension levelised cost of energy (LCOE 2) of an 18 MW wind farm comprising of 0.5 MW rated WTGs are within the order of £23.52 per MWh. Alternatively, if the same wind farm consists of fewer 2 or 3 MW WTGs, the LCOE 2 reduces to £16.56 or £15.49 per MWh, respectively. Further, findings reveal that an annual performance degradation of 1.6% (0.2%) increases LCOE2 by 34-41% (3.6-4.3%). IntroductionLifetime extension (LTE) is financially attractive in comparison to new investment and repowering due to the following identified root causes: the ageing of the current European wind fleet as illustrated in Figure 1; the more competitive allocation of onshore subsidies for new investments [1]; the trend of terminating repowering subsidies as observed in Germany, Spain, Denmark, and the UK [2]; the demonstration of competitive lifetime extension levelised cost of energy (LCOE 2 ) [3]; and finally the comparatively little due diligence for the lifetime extension analysis (LTEA) as well as the hard-won, long-term stakeholder relationships with local communities. As a consequence, LTE of wind turbine generators (WTGs) has become a widespread objective within todays' onshore wind energy industry.As greater rated turbines have been commercially developed and installed in the past, there is an observable trend of greater WTG classes reaching their end of design lifetime as illustrated in Figure 2, depicting the current and future distribution of turbine classes reaching their 20th year of operation. In 2016, the distribution is predominantly composed of WTG below 0.5 MW and to a lesser impact of WTG in between 0.5 and 1 MW; however, this distribution is changing significantly over the next decade with the pre-dominant WTG class shifting towards installations in between 0.5 and 1 MW. Given this observable trend of greater scale turbines reaching their end of design lifetime, this paper is aimed at analysing the development of lifetime extension LCOE with varying turbine classes.

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“…According to e.g. Emeis (2014), the turbulence length-scale is small close to the surface and increases with height. Therefore, in eddy covariance calculations, at higher altitudes a longer sampling period is typically required compared with lower altitudes, where more (small) eddies pass the instrument within a given period.…”

Section: Determination Of Gust Length-scalesmentioning

confidence: 99%

Gust factor based on research aircraft measurements: a new methodology applied to the Arctic marine boundary layer

Suomi

Lüpkes

Hartmann

et al. 2016

Quart J Royal Meteoro Soc

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There is as yet no standard methodology for measuring wind gusts from a moving platform. To address this, we have developed a method to derive gusts from research aircraft data. First we evaluated four different approaches, including Taylor's hypothesis of frozen turbulence, to derive the gust length‐scales that correspond to the gust time‐scales, namely the gust duration (s) and the sample period (typically 10 min). The novelty of our method lies in using peak factors (deviation of the gust from the mean wind speed normalized by the local turbulence) to convert between the scales. After devising a way to derive the gust length‐scales, we calculated the gust factors from aircraft observations and tested them against those from four parametrizations originally developed for weather stations. Three of them performed well (R2=0.66 or higher), while the fourth overestimated the gust factors in unstable conditions (R2=0.52). The mean errors for all methods were low, from −0.02 to 0.05, indicating that wind gust factors can indeed be measured from research aircraft. Moreover, we showed that aircraft can provide gust measurements within the whole boundary layer, if horizontal legs are flown at multiple levels over the same track. This is a significant advance, as gust measurements are usually limited to heights reached by weather masts. In unstable conditions over the open ocean, the gust factor was nearly constant with height throughout the boundary layer, the near‐surface values only slightly exceeding those at upper levels. Furthermore, we found gust factors to be strongly dependent on surface roughness conditions, which differed between the open ocean and sea ice in the Arctic marine environment. The roughness effect on the gust factor was stronger than the effect of boundary‐layer stability.

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Current issues in wind energy meteorology

Cited by 71 publications

References 77 publications

Assessment and Performance Evaluation of a Wind Turbine Power Output

Assessment and Performance Evaluation of a Wind Turbine Power Output

The Effect of Upscaling and Performance Degradation on Onshore Wind Turbine Lifetime Extension Decision Making

Gust factor based on research aircraft measurements: a new methodology applied to the Arctic marine boundary layer

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