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The influence of the main large-scale circulation patterns on wind power production in Portugal

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  • Correia, J.M.
  • Bastos, A.
  • Brito, M.C.
  • Trigo, R.M.

Abstract

Renewable energy production is known to present variability on several time-scales. The large-scale atmospheric circulation patterns influence the anomalies of relevant climate variables for energy production, such as wind speed and direction or solar radiation, from sub-seasonal to multi-decadal time-scales. This work aims at evaluating the link between large-scale atmospheric circulation patterns and monthly wind power resource and production in Portugal. The three climate modes under focus are the North Atlantic Oscillation (NAO), the East Atlantic Pattern (EA) and the Scandinavian Pattern (SCAND), which are considered the most relevant large-scale circulation patterns for the climate of Southwestern Europe. The impact of each of the three climate variability modes and their combined effect on wind power resource on monthly and annual wind energy production is assessed, using both meteorological station data and wind power generation in Continental Portugal.

Suggested Citation

  • Correia, J.M. & Bastos, A. & Brito, M.C. & Trigo, R.M., 2017. "The influence of the main large-scale circulation patterns on wind power production in Portugal," Renewable Energy, Elsevier, vol. 102(PA), pages 214-223.
    • Handle: RePEc:eee:renene:v:102:y:2017:i:pa:p:214-223
    DOI: 10.1016/j.renene.2016.10.002
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    1. Kubik, M.L. & Brayshaw, D.J. & Coker, P.J. & Barlow, J.F., 2013. "Exploring the role of reanalysis data in simulating regional wind generation variability over Northern Ireland," Renewable Energy, Elsevier, vol. 57(C), pages 558-561.
    2. Christophe Cassou, 2008. "Intraseasonal interaction between the Madden–Julian Oscillation and the North Atlantic Oscillation," Nature, Nature, vol. 455(7212), pages 523-527, September.
    3. Kubik, M.L. & Coker, P.J. & Barlow, J.F. & Hunt, C., 2013. "A study into the accuracy of using meteorological wind data to estimate turbine generation output," Renewable Energy, Elsevier, vol. 51(C), pages 153-158.
    4. Xinxin Zhu & Marc G. Genton, 2012. "Short‐Term Wind Speed Forecasting for Power System Operations," International Statistical Review, International Statistical Institute, vol. 80(1), pages 2-23, April.
    5. Brayshaw, David James & Troccoli, Alberto & Fordham, Rachael & Methven, John, 2011. "The impact of large scale atmospheric circulation patterns on wind power generation and its potential predictability: A case study over the UK," Renewable Energy, Elsevier, vol. 36(8), pages 2087-2096.
    6. Cannon, D.J. & Brayshaw, D.J. & Methven, J. & Coker, P.J. & Lenaghan, D., 2015. "Using reanalysis data to quantify extreme wind power generation statistics: A 33 year case study in Great Britain," Renewable Energy, Elsevier, vol. 75(C), pages 767-778.
    7. Gallego-Castillo, Cristobal & Cuerva-Tejero, Alvaro & Lopez-Garcia, Oscar, 2015. "A review on the recent history of wind power ramp forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 1148-1157.
    8. Santos-Alamillos, F.J. & Pozo-Vázquez, D. & Ruiz-Arias, J.A. & Lara-Fanego, V. & Tovar-Pescador, J., 2014. "A methodology for evaluating the spatial variability of wind energy resources: Application to assess the potential contribution of wind energy to baseload power," Renewable Energy, Elsevier, vol. 69(C), pages 147-156.
    9. Santos-Alamillos, F.J. & Pozo-Vázquez, D. & Ruiz-Arias, J.A. & Von Bremen, L. & Tovar-Pescador, J., 2015. "Combining wind farms with concentrating solar plants to provide stable renewable power," Renewable Energy, Elsevier, vol. 76(C), pages 539-550.
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    3. António Couto & Paula Costa & Teresa Simões, 2021. "Identification of Extreme Wind Events Using a Weather Type Classification," Energies, MDPI, vol. 14(13), pages 1-16, July.
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    5. Figueiredo, Raquel & Nunes, Pedro & Brito, Miguel C., 2018. "Multiyear calibration of simulations of energy systems," Energy, Elsevier, vol. 157(C), pages 932-939.
    6. Bedassa R. Cheneka & Simon J. Watson & Sukanta Basu, 2021. "Associating Synoptic-Scale Weather Patterns with Aggregated Offshore Wind Power Production and Ramps," Energies, MDPI, vol. 14(13), pages 1-14, June.

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