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Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants

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  • Lopes, Francis M.
  • Conceição, Ricardo
  • Silva, Hugo G.
  • Salgado, Rui
  • Collares-Pereira, Manuel

Abstract

To contribute for improved operational strategies of concentrating solar power plants with accurate forecasts of direct normal irradiance, this work describes the use of several post-processing methods on numerical weather prediction. Focus is given to a multivariate regression model that uses measured irradiance values from previous hours to improve next-hour predictions, which can be used to refine daily strategies based on day-ahead predictions. Short-term forecasts provided by the Integrated Forecasting System, the global model from the European Centre for Medium-Range Weather Forecasts (ECMWF), are used together with measurements in southern Portugal. As a nowcasting tool, the proposed regression model significantly improves hourly predictions with a skill score of ≈0.84 (i.e. an increase of ≈27.29% towards the original hourly forecasts). Using previous-day measured availability to improve next-day forecasts, the model shows a skill score of ≈0.78 (i.e. an increase of ≈6% towards the original forecasts), being further improved if larger sets of data are used. Through a power plant simulator (i.e. the System Advisor Model), a preliminary economic analysis shows that using improved hourly predictions of electrical energy allows to enhance a power plant’s profit in ≈0.44 M€/year, as compared with the original forecasts. Operational strategies are proposed accordingly.

Suggested Citation

  • Lopes, Francis M. & Conceição, Ricardo & Silva, Hugo G. & Salgado, Rui & Collares-Pereira, Manuel, 2021. "Improved ECMWF forecasts of direct normal irradiance: A tool for better operational strategies in concentrating solar power plants," Renewable Energy, Elsevier, vol. 163(C), pages 755-771.
    • Handle: RePEc:eee:renene:v:163:y:2021:i:c:p:755-771
    DOI: 10.1016/j.renene.2020.08.140
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    References listed on IDEAS

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    Cited by:

    1. Zhang, Yi-Ming & Wang, Hao, 2023. "Multi-head attention-based probabilistic CNN-BiLSTM for day-ahead wind speed forecasting," Energy, Elsevier, vol. 278(PA).
    2. Yin, S. & Wang, J. & Li, Z. & Fang, X., 2021. "State-of-the-art short-term electricity market operation with solar generation: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    3. Ailton M. Tavares & Ricardo Conceição & Francisco M. Lopes & Hugo G. Silva, 2022. "Development of a Simple Methodology Using Meteorological Data to Evaluate Concentrating Solar Power Production Capacity," Energies, MDPI, vol. 15(20), pages 1-27, October.

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