How does wind farm performance decline with age?

Staffell, Iain; Green, Richard

doi:10.1016/j.renene.2013.10.041

Cited by 369 publications

(263 citation statements)

References 14 publications

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“…A similar approach as the Virtual Wind Farm (VWF) model developed in [51,52] was used to simulate wind power production from wind speeds at 100 m. height, provided by MERRA2 reanalysis and WRF simulations.…”

Section: Wind Power Productionmentioning

confidence: 99%

Compatibility of wind and solar energy with electricity demand in Argentina

Томас¹,

Bianchi²,

Solarte³

et al. 2018

Preprint

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One of the greatest obstacles in the exploitation of wind and solar resources is the uncertainty in their availability, usually known as intermittency effects. These effects can be greatly diminished by combining wind and solar resources from different locations. In this article we study the temporal variability of solar irradiance and wind speeds in Argentina, focusing on current projects and those included in GENREN and RENOVAR tenders. We also converted two year wind speeds and irradiance time series into power production, analyzing its compatibility with demand. Finally we numerically search the best distribution of additional capacity that minimizes a basic measure of the intermittency effect. ResumenLa falta de certeza en la disponibilidad es uno de los mayores obstáculos que se presentan a la hora de aprovechar las energías eólica y solar. Esta característica, usualmente conocida como efectos de intermitencia, puede ser mitigada integrando ambas fuentes y en distintos sitios. En este trabajo estudiamos la variabilidad temporal del recurso eólico y solar en Argentina, enfocándonos en los proyectos ofrecidos en las licitaciones GENREN y REN-OVAR. Luego convertimos las series temporales horarias de viento y sol en potencia, durante el perído 2015-2016, * tguozden@unrn.edu.ar † ebianchi@unrn.edu.ar estudiando como hubiera sido el recurso de los parques eólicos y solares operativos y próximos a entrar en servicio. Porúltimo analizamos la mejor distribución de potencia adicional a la proyectada tal que su impacto sobre la intermitencia sea mínimo.

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Section: Wind Power Productionmentioning

confidence: 99%

Compatibility of wind and solar energy with electricity demand in Argentina

Томас¹,

Bianchi²,

Solarte³

et al. 2018

Preprint

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“…Its effects are inevitable for all kinds of machinery, reducing the efficiency, output and availability of steam and gas turbines, solar PV modules, batteries and automobiles alike" [4]. Essentially, if ageing effects are observable on a wind turbine, its effects will naturally impact the overall efficiency, thus reducing the AEP and capacity factor while increasing LCOE as published in the literature [1].…”

Section: Performance Degradationmentioning

confidence: 99%

“…what sources of PD are included and what are excluded from consideration. As mentioned previously, studies have aimed to quantify long-term performance degradation with different methodologies such as based on the UK's renewables obligation certificates (ROC) register [4] or a supervisory control and data acquisition (SCADA) system respectively [5]. Naturally, both approaches have advantages and disadvantages while considering a different combination of PD sources in the analysis, as contrasted in Table 3.…”

Section: Performance Degradationmentioning

confidence: 99%

“…Furthermore, performance degradation (PD) can be observed in all different types of machinery, with its effect mostly attributed to wear and tear of components. In 2014, Staffell and Green [4] as well as Wilkinson [5] focused on the long-term impact of PD on WTGs, with initial attempts to quantify its impact on the annual energy production (AEP) over an asset's lifetime. Given this recently published performance degradation, current LCOE models appear to omit this reduction in AEP when estimating project costs [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…It is now common to encounter wind farms that have been in operation for more than ten years. As a wind farm ages the performance of its WT degrades with time as reported in [3].…”

Section: Introductionmentioning

confidence: 99%

Comparison of power curve monitoring methods

et al. 2017

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Abstract. Performance monitoring is an important aspect of operating wind farms. This can be done through the power curve monitoring (PCM) of wind turbines (WT). In the past years, important work has been conducted on PCM. Various methodologies have been proposed, each one with interesting results. However, it is difficult to compare these methods because they have been developed using their respective data sets. The objective of this actual work is to compare some of the proposed PCM methods using common data sets. The metric used to compare the PCM methods is the time needed to detect a change in the power curve. Two power curve models will be covered to establish the effect the model type has on the monitoring outcomes. Each model was tested with two control charts. Other methodologies and metrics proposed in the literature for power curve monitoring such as areas under the power curve and the use of statistical copulas have also been covered. Results demonstrate that modelbased PCM methods are more reliable at the detecting a performance change than other methodologies and that the effectiveness of the control chart depends on the types of shift observed.

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How does wind farm performance decline with age?

Cited by 369 publications

References 14 publications

Compatibility of wind and solar energy with electricity demand in Argentina

Compatibility of wind and solar energy with electricity demand in Argentina

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

Comparison of power curve monitoring methods

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