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Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union

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  • Monforti, Fabio
  • Gonzalez-Aparicio, Iratxe

Abstract

The adequate modelling of power systems under heavily penetration of Renewable Energy sources crucially depends on the accurate representation of the spatial and temporal features of intermittent power resources supplying the system. In the case of wind energy, two main ingredients are needed for a suitable assessment of its temporal features: a detailed knowledge of the technical parameters of the power generators and the proper description of the physical parameters leading to actual power generation. In this paper we compare the relative weight of uncertainties in the wind power assessment arising from the limited knowledge of the wind farms technical parameters (namely hub height, turbine type and wind farm positioning) with uncertainties originated by the limited description of wind speed fields at hub height provided by meteorological reanalyses. The quantitative comparison of error sources has been achieved by means of a sensitivity analysis taking into consideration the most crucial parameters impacting wind power estimation. The analysis has shown the overwhelming importance of coupling an accurate data base of operating wind farms with a proper representation of the input wind fields at the most possibly detailed level. To our knowledge, this is the first time that the sources of uncertainties for wind power generation estimates have been compared on a continental scale.

Suggested Citation

  • Monforti, Fabio & Gonzalez-Aparicio, Iratxe, 2017. "Comparing the impact of uncertainties on technical and meteorological parameters in wind power time series modelling in the European Union," Applied Energy, Elsevier, vol. 206(C), pages 439-450.
    • Handle: RePEc:eee:appene:v:206:y:2017:i:c:p:439-450
    DOI: 10.1016/j.apenergy.2017.08.217
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    1. Martin, Rebecca & Lazakis, Iraklis & Barbouchi, Sami & Johanning, Lars, 2016. "Sensitivity analysis of offshore wind farm operation and maintenance cost and availability," Renewable Energy, Elsevier, vol. 85(C), pages 1226-1236.
    2. González-Aparicio, I. & Zucker, A., 2015. "Impact of wind power uncertainty forecasting on the market integration of wind energy in Spain," Applied Energy, Elsevier, vol. 159(C), pages 334-349.
    3. Engeland, Kolbjørn & Borga, Marco & Creutin, Jean-Dominique & François, Baptiste & Ramos, Maria-Helena & Vidal, Jean-Philippe, 2017. "Space-time variability of climate variables and intermittent renewable electricity production – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 600-617.
    4. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Nissen, J.N., 2015. "A novel application of an analog ensemble for short-term wind power forecasting," Renewable Energy, Elsevier, vol. 76(C), pages 768-781.
    5. Maria Grazia De Giorgi & Stefano Campilongo & Antonio Ficarella & Paolo Maria Congedo, 2014. "Comparison Between Wind Power Prediction Models Based on Wavelet Decomposition with Least-Squares Support Vector Machine (LS-SVM) and Artificial Neural Network (ANN)," Energies, MDPI, vol. 7(8), pages 1-22, August.
    6. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    7. Birgir Freyr Ragnarsson & Gudmundur V. Oddsson & Runar Unnthorsson & Birgir Hrafnkelsson, 2015. "Levelized Cost of Energy Analysis of a Wind Power Generation System at Búrfell in Iceland," Energies, MDPI, vol. 8(9), pages 1-22, September.
    8. Rohatgi, Janardan & Barbezier, Gil, 1999. "Wind turbulence and atmospheric stability — Their effect on wind turbine output," Renewable Energy, Elsevier, vol. 16(1), pages 908-911.
    9. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
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