A long-term perspective of wind power output variability

Kirchner-Bossi, Nicolas; García‐Herrera, Ricardo; Prieto, Luís; Trigo, Ricardo M.

doi:10.1002/joc.4161

Cited by 47 publications

(19 citation statements)

References 73 publications

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“…Annual variations in capacity factor are large (between 33.2% and 45% based on the 34-year time series). During the 20th century, significant inter-annual and inter-decadal variations in wind-speed were recorded [42,43], and such variations would have had a major impact on wind-power resources [44,45]. Future trends also have to be taken into consideration [46].…”

Section: Windmentioning

confidence: 99%

Variability Characteristics of European Wind and Solar Power Resources—A Review

Graabak

Korpås

2016

Energies

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This paper reviews the most recent and relevant research into the variability characteristics of wind and solar power resources in Europe. The background for this study is that wind and solar resources will probably constitute major components of the future European power system. Such resources are variable, and EU plans to balance the variability with more grids and demand response. Thus, planning for the future power system requires an in-depth understanding of the variability. Resource variability is a multi-faceted concept best described using a range of distinct characteristics, and this review is structured on the basis of seven of these: Distribution Long-Term (hours to years), Distribution Short-Term (less than one hour), Step Changes, Autocorrelation, Spatial Correlation, Cross Correlation and Predictable Patterns. The review presents simulations and empirical results related to resource variability for each of these characteristics. Results to date reveal that the variability characteristics of the future power system is limited understood. This study recommends the development of a scheme for greater systematic assessment of variability. Such a scheme will contribute to the understanding of the impacts of variability and will make it possible to compare alternative power production portfolios and impacts of grid expansions, demand response and storage technologies.

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Section: Windmentioning

confidence: 99%

Variability Characteristics of European Wind and Solar Power Resources—A Review

Graabak

Korpås

2016

Energies

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“…The main limitation for the assessment of wind speed trends is the unavailability of long enough, homogenous time series of historical data from observational measurements. These data products can also be affected by discontinuities associated with changes in the measuring equipment, its location or in the observing practices , Kirchner-Bossi et al 2015, which impacts the data quality. To overcome these limitations global meteorological reanalysis data sets, which are available for long periods and are relatively homogenous, have been recently considered for different wind energy applications (Cannon et al 2015, Rose and Apt 2015, Staffell and Pfenninger 2016.…”

Section: Introductionmentioning

confidence: 99%

Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison

Torralba

Doblas‐Reyes

González-Reviriego

2017

Environ. Res. Lett.

140

105

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Reanalysis products have become a tool for wind energy users requiring information about the wind speed long-term variability. These users are sensitive to many aspects of the observational references they employ to estimate the wind resource, such as the mean wind, its seasonality and long-term trends. However, the assessment of the ability of atmospheric reanalyses to reproduce wind speed trends has not been undertaken yet. The wind speed trends have been estimated using the ERA-Interim reanalysis (ERA-I), the second version of the Modern Era Retrospective-Analysis for Research and Applications (MERRA-2) and the Japanese 55-year Reanalysis (JRA-55) for the period 1980-2015. These trends show a strong spatial and seasonal variability with an overall increase of the wind speed over the ocean and a tendency to a decline over land, although important disagreements between the different reanalyses have been found. In particular, the JRA-55 reanalysis produces more intense trends over land than ERA-I and MERRA-2. This can be linked to the negative bias affecting the JRA-55 near-surface wind speeds over land. In all the reanalyses high wind speeds tend to change faster than both low and average wind speeds. The agreement of the wind speed trends at 850 hPa with those found close to the surface suggests that the main driver of the wind speed trends are the changes in large-scale circulation.

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“…It has been demonstrated that wind speed and direction are affected by large-scale climate oscillations. For instance, European and North American wind speeds are affected by the annual variation of NAO (Kirchner-Bossi et al, 2014;Scaife et al, 2014;Smith et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Teleconnections and analysis of long-term wind speed variability in the UAE

Naizghi¹,

Ouarda

2016

Int. J. Climatol.

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Wind energy accounts for a small share of the global energy consumption in spite of its widespread availability. One of the obstacles hindering exploitation of wind energy is the lack of proper wind speed assessment models. The wind energy field credibility has occasionally suffered from wind power potential estimation studies that were conducted based on very short wind speed records and which did not give consideration to inter‐annual wind variability. The objective of this paper is to examine the long‐term variability of wind speed in the United Arab Emirates (UAE) and its teleconnections with various global climate indices by using wind speed collected from six ground stations and a reanalysis dataset. Linear correlation analysis and wavelet analysis were used to characterize the interaction. The modified Mann–Kendall test and linear regression indicated that half of the stations show a significant wind speed trend at the 5% level. The cumulative sum and Bayesian change detection methods indicated that five of the stations present change points. Continuous wavelet transform of wind speed showed biannual periodicity in some stations, in addition to the annual one. Wavelet coherence analysis demonstrated that wind speed in the UAE is mainly associated with the North Atlantic Oscillation, East Atlantic Oscillation, El Niño Southern Oscillation and the Indian Ocean Dipole indices. The first two indices simultaneously modulate wind speed in the summer while the last two influence winter and autumn wind speeds. Step‐wise multiple linear regression models were developed to select appropriate predictors among the various climate indices.

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A long-term perspective of wind power output variability

Cited by 47 publications

References 73 publications

Variability Characteristics of European Wind and Solar Power Resources—A Review

Variability Characteristics of European Wind and Solar Power Resources—A Review

Uncertainty in recent near-surface wind speed trends: a global reanalysis intercomparison

Teleconnections and analysis of long-term wind speed variability in the UAE

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