Simulating European wind power generation applying statistical downscaling to reanalysis data

González-Aparicio, Iratxe; Monforti, F.; Volker, Patrick; Zucker, Andreas; Careri, F.; Huld, Thomas; Badger, Jake

doi:10.1016/j.apenergy.2017.04.066

Cited by 115 publications

(86 citation statements)

References 23 publications

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“…As has been shown in González‐Aparicio et al (), Nuño et al () and Staffell and Pfenninger (), using meteorological reanalysis wind speeds directly can cause significant errors in capacity factors. To correct for this, historical capacity factor data can be used to calibrate wind speeds in CorRES.…”

Section: Combination Of Meteorological Data and Stochastic Simulationsmentioning

confidence: 92%

“…For the study of future scenarios, the models need to be applicable to a changing geographical distribution and technological development of installations. VRE generation time series for future scenarios can be generated using stochastic time series simulation (Ekström, Koivisto, Mellin, Millar, & Lehtonen, ; Klöckl & Papaefthymiou, ; Koivisto et al, ), or meteorological reanalysis techniques (i.e., using historical weather data) (González‐Aparicio et al, ; Marinelli et al, ; Nuño et al, ; Staffell & Pfenninger, ). Such VRE generation simulation tools can be used, for example, in the estimation of adequacy of reserves in power systems (Das et al, ), in stability analyses (Flynn et al, ), in long‐term transmission system planning (Marinelli et al, ), and in electricity market studies (Traber, Koduvere, & Koivisto, ).…”

Section: Introductionmentioning

confidence: 99%

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Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

Koivisto

Das

Guo

et al. 2018

WIREs Energy & Environment

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The increasing share of variable renewable energy (VRE) generation poses challenges to power systems. Possible challenges include adequacy of reserves, planning and operation of power systems, and interconnection expansion studies in future power systems with very different generation patterns compared to today. To meet these challenges, there is a need to develop models and tools to analyze the variability and uncertainty in VRE generation. To address the varied needs, the tools should be versatile and applicable to different geographical and temporal scales. Time series simulation tools can be used to model both today and future scenarios with varying VRE installations. Correlations in Renewable Energy Sources (CorRES) is a simulation tool developed at Technical University of Denmark, Department of Wind Energy capable of simulating both wind and solar generation. It uses a unique combination of meteorological time series and stochastic simulations to provide consistent VRE generation and forecast error time series with temporal resolution in the minute scale. Such simulated VRE time series can be used in addressing the challenges posed by the increasing share of VRE generation. These capabilities will be demonstrated through three case studies: one about the use of large-scale VRE generation simulations in energy system analysis, and two about the use of the simulations in power system operation, planning, and analysis.

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Section: Combination Of Meteorological Data and Stochastic Simulationsmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

Koivisto

Das

Guo

et al. 2018

WIREs Energy & Environment

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“…Over the decades, new reanalyses have gradually become more high-resolved in time and space [2]. During the last years, MERRA [3] and MERRA-2 [1] (both produced by NASA) have been very popular for modelling wind power generation [4][5][6][7][8][9][10][11][12][13][14] due to, e.g., the hourly temporal resolution and adequate height for wind speeds (50 m). A general conclusion from the studies cited above is that MERRA gives good results in terms of country-wise wind power generation (relatively low errors when comparing to measurements).…”

Section: Introductionmentioning

confidence: 99%

ERA5: The new champion of wind power modelling?

2018

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“…Results from these analyses suggest that resolutions larger than of ∼25 km do not accurately represent the variability of the surface wind field over the UK, especially in coastal regions and regions with complex topography. This suggests a need to revisit the conclusions reached in studies that use spatial resolutions lower than this threshold, such as Cannon et al (2015) and González-Aparicio et al (2017), to name a few. It was also determined that southeastern England and offshore of southwest England are possible candidates for wind farm placement, supporting the historical locations of wind farm development in the UK.…”

Section: Discussionmentioning

confidence: 99%

Impact of model resolution on the representation of the wind speed field: An example from the United Kingdom

Benedetti

Moore

2019

Quart J Royal Meteoro Soc

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The United Kingdom (UK) is experiencing rapid growth in wind farm development, primarily in the south as well as in offshore regions. As such, reliable long‐term wind statistics are integral in planning future development. Given the scarcity of in situ data, atmospheric reanalyses are beginning to be used for this purpose. However, most reanalyses have resolutions of ∼50 km or lower – scales too coarse to capture many topographic and coastal effects. To address this, dynamical downscaling has been used to obtain higher‐resolution data. However, it is unclear how the downscaling process impacts the temporal and spatial representation of the wind field. Here a set of reanalysis and analysis datasets with a common lineage and with horizontal resolutions ranging from ∼75 to ∼9 km are used to investigate the impact of model resolution on the representation of the spatial and temporal variability in the wind field in the UK. To assess this impact, the decorrelation length and temporal scale – DCLS and DCTS, respectively – are used to characterize spatial and temporal variability. The results can be classified into two categories – resolution‐dependent and resolution‐independent features. Resolution‐dependent results suggest that even resolutions of ∼30 km do not capture the wind field's variability, especially in coastal and mountainous regions. However, resolution‐independent results suggest that there are regions, such as central Ireland, over the oceans, and southeastern England, where the 30 km resolution captures the wind fields' variability. Results also support the historic choice of wind farm development in the UK, namely, the southern/southwestern tip of England as well as off the southern shore.

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Simulating European wind power generation applying statistical downscaling to reanalysis data

Cited by 115 publications

References 23 publications

Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

Using time series simulation tools for assessing the effects of variable renewable energy generation on power and energy systems

ERA5: The new champion of wind power modelling?

Impact of model resolution on the representation of the wind speed field: An example from the United Kingdom

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