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Analysis of the Suitability of the EOLO Wind-Predictor Model for the Spanish Electricity Markets

Author

Listed:
  • Saray Martínez-Lastras

    (Department of Cartographic and Land Engineering, EPS Ávila, University of Salamanca, 05003 Ávila, Spain)

  • Laura Frías-Paredes

    (Statistics, Computer Science and Mathematics, Public University of Navarra, 31006 Pamplona, Spain)

  • Diego Prieto-Herráez

    (Department of Cartographic and Land Engineering, EPS Ávila, University of Salamanca, 05003 Ávila, Spain
    Sciences and Arts Faculty, Catholic University of Ávila (UCAV), Calle Canteros s/n, 05005 Ávila, Spain)

  • Martín Gastón-Romeo

    (Statistics, Computer Science and Mathematics, Public University of Navarra, 31006 Pamplona, Spain)

  • Diego González-Aguilera

    (Department of Cartographic and Land Engineering, EPS Ávila, University of Salamanca, 05003 Ávila, Spain)

Abstract
Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity markets, focusing on the statistical analysis of its results. Results show how the wind energy forecast generated by EOLO anticipates real electricity generation with high accuracy and stability, providing a reduced forecast error when it is used to participate in successive sessions of the Spanish electricity market. The obtained error, in terms of RMAE, ranges from 8%, when it is applied to the Day-ahead market, to 6%, when it is applied to the last intraday market. In financial terms, the prediction achieves a financial performance near 99% once imbalance penalties have been discounted.

Suggested Citation

  • Saray Martínez-Lastras & Laura Frías-Paredes & Diego Prieto-Herráez & Martín Gastón-Romeo & Diego González-Aguilera, 2023. "Analysis of the Suitability of the EOLO Wind-Predictor Model for the Spanish Electricity Markets," Energies, MDPI, vol. 16(3), pages 1-16, January.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:3:p:1101-:d:1040870
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    References listed on IDEAS

    as
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