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Seasonal forecasts of wind power generation

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  • Lledó, Ll.
  • Torralba, V.
  • Soret, A.
  • Ramon, J.
  • Doblas-Reyes, F.J.

Abstract

The energy sector is highly dependent on climate variability for electricity generation, maintenance activities and demand. In recent years, a few climate services have appeared that provide tailored information for the energy sector. In particular, seasonal climate predictions of wind speed have proven useful to the wind power industry. However, most of the service users are ultimately interested in forecasts of electricity generation instead of wind. Although power generation depends on many factors other than wind conditions, the capacity factor is a suitable indicator to quantify the impact of wind variability on production. In this paper a methodology to produce seasonal predictions of capacity factor for a range of turbine classes is proposed for the first time. The strengths and weaknesses of the method are discussed and the forecast quality is evaluated for an application example over Europe.

Suggested Citation

  • Lledó, Ll. & Torralba, V. & Soret, A. & Ramon, J. & Doblas-Reyes, F.J., 2019. "Seasonal forecasts of wind power generation," Renewable Energy, Elsevier, vol. 143(C), pages 91-100.
    • Handle: RePEc:eee:renene:v:143:y:2019:i:c:p:91-100
    DOI: 10.1016/j.renene.2019.04.135
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    References listed on IDEAS

    as
    1. Draxl, Caroline & Clifton, Andrew & Hodge, Bri-Mathias & McCaa, Jim, 2015. "The Wind Integration National Dataset (WIND) Toolkit," Applied Energy, Elsevier, vol. 151(C), pages 355-366.
    2. Staffell, Iain & Pfenninger, Stefan, 2018. "The increasing impact of weather on electricity supply and demand," Energy, Elsevier, vol. 145(C), pages 65-78.
    3. Chris Hewitt & Simon Mason & David Walland, 2012. "The Global Framework for Climate Services," Nature Climate Change, Nature, vol. 2(12), pages 831-832, December.
    4. Ritter, Matthias & Shen, Zhiwei & López Cabrera, Brenda & Odening, Martin & Deckert, Lars, 2015. "Designing an index for assessing wind energy potential," Renewable Energy, Elsevier, vol. 83(C), pages 416-424.
    5. Lydia, M. & Kumar, S. Suresh & Selvakumar, A. Immanuel & Prem Kumar, G. Edwin, 2014. "A comprehensive review on wind turbine power curve modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 452-460.
    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. Matthias Themeßl & Andreas Gobiet & Georg Heinrich, 2012. "Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal," Climatic Change, Springer, vol. 112(2), pages 449-468, May.
    8. Staffell, Iain & Pfenninger, Stefan, 2016. "Using bias-corrected reanalysis to simulate current and future wind power output," Energy, Elsevier, vol. 114(C), pages 1224-1239.
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    Cited by:

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    5. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
    6. Bastien Alonzo & Philippe Drobinski & Riwal Plougonven & Peter Tankov, 2020. "Measuring the Risk of Supply and Demand Imbalance at the Monthly to Seasonal Scale in France," Energies, MDPI, vol. 13(18), pages 1-21, September.
    7. Katopodis, Theodoros & Markantonis, Iason & Vlachogiannis, Diamando & Politi, Nadia & Sfetsos, Athanasios, 2021. "Assessing climate change impacts on wind characteristics in Greece through high resolution regional climate modelling," Renewable Energy, Elsevier, vol. 179(C), pages 427-444.
    8. Lledó, Llorenç & Ramon, Jaume & Soret, Albert & Doblas-Reyes, Francisco-Javier, 2022. "Seasonal prediction of renewable energy generation in Europe based on four teleconnection indices," Renewable Energy, Elsevier, vol. 186(C), pages 420-430.
    9. Zhang, Yi & Cheng, Chuntian & Yang, Tiantian & Jin, Xiaoyu & Jia, Zebin & Shen, Jianjian & Wu, Xinyu, 2022. "Assessment of climate change impacts on the hydro-wind-solar energy supply system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
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    12. Olaofe, Z.O., 2019. "Quantification of the near-surface wind conditions of the African coast: A comparative approach (satellite, NCEP CFSR and WRF-based)," Energy, Elsevier, vol. 189(C).

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