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Predicted direct solar radiation (ECMWF) for optimized operational strategies of linear focus parabolic-trough systems

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

Abstract

Day-ahead forecasts of direct normal irradiance (DNI) from the Integrated Forecasting System (IFS), the global model of the European Centre for Medium-Range Weather Forecasts (ECMWF), are used to simulate a concentrating solar power (CSP) plant through the System Advisor Model (SAM) to assess the potential value of the IFS in the electricity market. Although DNI forecasting from the IFS still demands advances towards cloud and aerosol representation, present results show substantial improvements with the new operational radiative scheme ecRad (cycle 43R3). A relative difference of approximately 0.12% for the total annual energy availability is found between forecasts and local measurements, while approximately 10.6% is obtained for the previous version. Results of electric energy injection to the grid from a simulated linear focus parabolic-trough system shows correlations coefficients of approximately 0.87 between hourly values of electric energy based on forecasted and measured DNI, while 0.92 are obtained for the daily values. In the context of control strategy, four operational strategies are given for different weather scenarios to handle the energy management of a CSP plant, including the effect of thermal energy storage capacity. Charge and discharge operational strategies are applied accordingly to the predicted energy availability.

Suggested Citation

  • Lopes, Francis M. & Conceição, Ricardo & Fasquelle, Thomas & Silva, Hugo G. & Salgado, Rui & Canhoto, Paulo & Collares-Pereira, Manuel, 2020. "Predicted direct solar radiation (ECMWF) for optimized operational strategies of linear focus parabolic-trough systems," Renewable Energy, Elsevier, vol. 151(C), pages 378-391.
    • Handle: RePEc:eee:renene:v:151:y:2020:i:c:p:378-391
    DOI: 10.1016/j.renene.2019.11.020
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    References listed on IDEAS

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    1. Abreu, Edgar F.M. & Canhoto, Paulo & Prior, Victor & Melicio, R., 2018. "Solar resource assessment through long-term statistical analysis and typical data generation with different time resolutions using GHI measurements," Renewable Energy, Elsevier, vol. 127(C), pages 398-411.
    2. Voyant, Cyril & Notton, Gilles, 2018. "Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool," Renewable and Sustainable Energy Reviews, Elsevier, vol. 92(C), pages 343-352.
    3. Chu, Yinghao & Coimbra, Carlos F.M., 2017. "Short-term probabilistic forecasts for Direct Normal Irradiance," Renewable Energy, Elsevier, vol. 101(C), pages 526-536.
    4. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
    5. Nonnenmacher, Lukas & Kaur, Amanpreet & Coimbra, Carlos F.M., 2016. "Day-ahead resource forecasting for concentrated solar power integration," Renewable Energy, Elsevier, vol. 86(C), pages 866-876.
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    1. Ailton M. Tavares & Ricardo Conceição & Francisco M. Lopes & Hugo G. Silva, 2024. "Effect of Solar Irradiation Inter-Annual Variability on PV and CSP Power Plants Production Capacity: Portugal Case-Study," Energies, MDPI, vol. 17(21), pages 1-20, November.
    2. Lopes, Telma & Fasquelle, Thomas & Silva, Hugo G., 2021. "Pressure drops, heat transfer coefficient, costs and power block design for direct storage parabolic trough power plants running molten salts," Renewable Energy, Elsevier, vol. 163(C), pages 530-543.
    3. Vasallo, Manuel Jesús & Cojocaru, Emilian Gelu & Gegúndez, Manuel Emilio & Marín, Diego, 2021. "Application of data-based solar field models to optimal generation scheduling in concentrating solar power plants," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1130-1149.
    4. Alami Merrouni, Ahmed & Conceição, Ricardo & Mouaky, Ammar & Silva, Hugo Gonçalves & Ghennioui, Abdellatif, 2020. "CSP performance and yield analysis including soiling measurements for Morocco and Portugal," Renewable Energy, Elsevier, vol. 162(C), pages 1777-1792.
    5. 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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