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The Economic Value of Wind Energy Nowcasting

Author

Listed:
  • Aurore Dupré

    (LMD/IPSL, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL, Research University, Sorbonne Université, CNRS, 91120 Palaiseau, France
    Current address: Inria Sophia-Antipolis Méditerrannée, 2004 Route des Lucioles, 06902 Valbonne, France.)

  • Philippe Drobinski

    (LMD/IPSL, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL, Research University, Sorbonne Université, CNRS, 91120 Palaiseau, France)

  • Jordi Badosa

    (LMD/IPSL, École Polytechnique, Institut Polytechnique de Paris, ENS, PSL, Research University, Sorbonne Université, CNRS, 91120 Palaiseau, France)

  • Christian Briard

    (Zephyr ENR, 37550 Saint-Avertin, France)

  • Peter Tankov

    (CREST, ENSAE, Institut polytechnique de Paris, 91120 Palaiseau, France)

Abstract

In recent years, environmental concerns resulted in an increase in the use of renewable resources such as wind energy. However, high penetration of the wind power is a challenge due to the intermittency of this resource. In this context, the wind energy forecasting has become a major issue. In particular, for the end users of wind energy forecasts, a critical but often neglected issue is the economic value of the forecast. In this work, we investigate the economic value of forecasting from 30 min to 3 h ahead, also known as nowcasting. Nowcasting is mainly used to inform trading decisions in the intraday market. Two sources of uncertainty affecting wind farm revenues are investigated, namely forecasting errors and price variations. The impact of these uncertainties is assessed for six wind farms and several balancing strategies using market data. Results are compared with the baseline case of no nowcasting and with the idealized case of perfect nowcast. The three settings show significant differences while the impact of the choice of a specific balancing strategy appears minor.

Suggested Citation

  • Aurore Dupré & Philippe Drobinski & Jordi Badosa & Christian Briard & Peter Tankov, 2020. "The Economic Value of Wind Energy Nowcasting," Energies, MDPI, vol. 13(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:20:p:5266-:d:425951
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    References listed on IDEAS

    as
    1. Dupré, Aurore & Drobinski, Philippe & Alonzo, Bastien & Badosa, Jordi & Briard, Christian & Plougonven, Riwal, 2020. "Sub-hourly forecasting of wind speed and wind energy," Renewable Energy, Elsevier, vol. 145(C), pages 2373-2379.
    2. Olivier F'eron & Peter Tankov & Laura Tinsi, 2020. "Price formation and optimal trading in intraday electricity markets," Papers 2009.04786, arXiv.org, revised Jun 2021.
    3. Roulston, M.S. & Kaplan, D.T. & Hardenberg, J. & Smith, L.A., 2003. "Using medium-range weather forcasts to improve the value of wind energy production," Renewable Energy, Elsevier, vol. 28(4), pages 585-602.
    4. Olivier F'eron & Peter Tankov & Laura Tinsi, 2020. "Price formation and optimal trading in intraday electricity markets with a major player," Papers 2011.07655, arXiv.org.
    5. Barthelmie, R.J. & Murray, F. & Pryor, S.C., 2008. "The economic benefit of short-term forecasting for wind energy in the UK electricity market," Energy Policy, Elsevier, vol. 36(5), pages 1687-1696, May.
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    Cited by:

    1. Sameer Al-Dahidi & Piero Baraldi & Enrico Zio & Lorenzo Montelatici, 2021. "Bootstrapped Ensemble of Artificial Neural Networks Technique for Quantifying Uncertainty in Prediction of Wind Energy Production," Sustainability, MDPI, vol. 13(11), pages 1-19, June.
    2. Bouche, Dimitri & Flamary, Rémi & d’Alché-Buc, Florence & Plougonven, Riwal & Clausel, Marianne & Badosa, Jordi & Drobinski, Philippe, 2023. "Wind power predictions from nowcasts to 4-hour forecasts: A learning approach with variable selection," Renewable Energy, Elsevier, vol. 211(C), pages 938-947.

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