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Impact of DNI forecasting on CSP tower plant power production

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  • Alonso-Montesinos, J.
  • Polo, Jesús
  • Ballestrín, Jesús
  • Batlles, F.J.
  • Portillo, C.

Abstract

In the context of energy policies focusing on minimizing power plant emissions, concentrating solar power (CSP) technology plays an important role in the energy mix. These plants require a high level of direct normal irradiance to work properly and profitably. Over-sizing of plant capacity is frequently employed in order to store part of the energy produced, to extend the operating time throughout the day, and also to manage cloud transients. Forecasting the energy delivered by the plant is very important in plant operational strategies to ensure dispatchability as much as possible. This work presents an analysis of energy forecasting in solar tower plants by combining a short-term solar irradiation forecasting scheme with a solar tower plant model using the System Advisor Model (SAM), as the modeling tool for computing plant production throughout the year. Satellite images were used to predict Direct Normal Irradiance (DNI) on an intra-hour time-scale (up to three hours). The predictions were introduced into SAM to simulate the behavior of the Gemasolar and Crescent Dunes plants, placed on Spain and Nevada, respectively). The results show that the best outcomes appear for the 90-mins horizon, where the Mean Bias was about −10% and the RMSE near to 23%.

Suggested Citation

  • Alonso-Montesinos, J. & Polo, Jesús & Ballestrín, Jesús & Batlles, F.J. & Portillo, C., 2019. "Impact of DNI forecasting on CSP tower plant power production," Renewable Energy, Elsevier, vol. 138(C), pages 368-377.
    • Handle: RePEc:eee:renene:v:138:y:2019:i:c:p:368-377
    DOI: 10.1016/j.renene.2019.01.095
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    References listed on IDEAS

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    1. Cornejo, Lorena & Martín-Pomares, Luis & Alarcon, Diego & Blanco, Julián & Polo, Jesús, 2017. "A through analysis of solar irradiation measurements in the region of Arica Parinacota, Chile," Renewable Energy, Elsevier, vol. 112(C), pages 197-208.
    2. Tsai, Sang-Bing & Xue, Youzhi & Zhang, Jianyu & Chen, Quan & Liu, Yubin & Zhou, Jie & Dong, Weiwei, 2017. "Models for forecasting growth trends in renewable energy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 1169-1178.
    3. Aguiar, L. Mazorra & Pereira, B. & Lauret, P. & Díaz, F. & David, M., 2016. "Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting," Renewable Energy, Elsevier, vol. 97(C), pages 599-610.
    4. Royo, Alberto & García, Ignacio & Torres, José Luis, 2018. "Generation of the site-adapted clearest-sky year of direct normal irradiance for solar concentrating technologies," Renewable Energy, Elsevier, vol. 128(PA), pages 250-264.
    5. Karimi, Reza & Gheinani, Touraj Tavakoli & Madadi Avargani, Vahid, 2018. "A detailed mathematical model for thermal performance analysis of a cylindrical cavity receiver in a solar parabolic dish collector system," Renewable Energy, Elsevier, vol. 125(C), pages 768-782.
    6. Alonso-Montesinos, J. & Martínez-Durbán, M. & del Sagrado, J. & del Águila, I.M. & Batlles, F.J., 2016. "The application of Bayesian network classifiers to cloud classification in satellite images," Renewable Energy, Elsevier, vol. 97(C), pages 155-161.
    7. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    8. Marzo, A. & Trigo-Gonzalez, M. & Alonso-Montesinos, J. & Martínez-Durbán, M. & López, G. & Ferrada, P. & Fuentealba, E. & Cortés, M. & Batlles, F.J., 2017. "Daily global solar radiation estimation in desert areas using daily extreme temperatures and extraterrestrial radiation," Renewable Energy, Elsevier, vol. 113(C), pages 303-311.
    9. Escrig, H. & Batlles, F.J. & Alonso, J. & Baena, F.M. & Bosch, J.L. & Salbidegoitia, I.B. & Burgaleta, J.I., 2013. "Cloud detection, classification and motion estimation using geostationary satellite imagery for cloud cover forecast," Energy, Elsevier, vol. 55(C), pages 853-859.
    10. Rabehi, Rayan & Chaker, Abla & Ming, Tingzhen & Gong, Tingrui, 2018. "Numerical simulation of solar chimney power plant adopting the fan model," Renewable Energy, Elsevier, vol. 126(C), pages 1093-1101.
    11. Alonso, J. & Batlles, F.J., 2014. "Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery," Energy, Elsevier, vol. 73(C), pages 890-897.
    12. Alonso-Montesinos, J. & Batlles, F.J., 2015. "Solar radiation forecasting in the short- and medium-term under all sky conditions," Energy, Elsevier, vol. 83(C), pages 387-393.
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    1. Alonso-Montesinos, J. & Monterreal, R. & Fernández-Reche, J. & Ballestrín, J. & Carra, E. & Polo, J. & Barbero, J. & Batlles, F.J. & López, G. & Enrique, R. & Martínez-Durbán, M. & Marzo, A., 2019. "Intra-hour energy potential forecasting in a central solar power plant receiver combining Meteosat images and atmospheric extinction," Energy, Elsevier, vol. 188(C).
    2. Mathieu David & Joaquín Alonso-Montesinos & Josselin Le Gal La Salle & Philippe Lauret, 2023. "Probabilistic Solar Forecasts as a Binary Event Using a Sky Camera," Energies, MDPI, vol. 16(20), pages 1-18, October.
    3. Salmon, Aloïs & Marzo, Aitor & Polo, Jesús & Ballestrín, Jesús & Carra, Elena & Alonso-Montesinos, Joaquín, 2022. "World map of low-layer atmospheric extinction values for solar power tower plants projects," Renewable Energy, Elsevier, vol. 201(P1), pages 876-888.
    4. Arias, I. & Cardemil, J. & Zarza, E. & Valenzuela, L. & Escobar, R., 2022. "Latest developments, assessments and research trends for next generation of concentrated solar power plants using liquid heat transfer fluids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    5. Rodríguez-Benítez, Francisco J. & López-Cuesta, Miguel & Arbizu-Barrena, Clara & Fernández-León, María M. & Pamos-Ureña, Miguel Á. & Tovar-Pescador, Joaquín & Santos-Alamillos, Francisco J. & Pozo-Váz, 2021. "Assessment of new solar radiation nowcasting methods based on sky-camera and satellite imagery," Applied Energy, Elsevier, vol. 292(C).
    6. Lin, Xiaoxia & He, Caitou & Huang, Wenjun & Zhao, Yuhong & Feng, Jieqing, 2022. "GPU-based Monte Carlo ray tracing simulation considering refraction for central receiver system," Renewable Energy, Elsevier, vol. 193(C), pages 367-382.

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