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Scenario-based model predictive control for energy scheduling in a parabolic trough concentrating solar plant with thermal storage

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  • Velarde, Pablo
  • Gallego, Antonio J.
  • Bordons, Carlos
  • Camacho, Eduardo F.

Abstract

Optimal energy planning is a key topic in thermal solar trough plants. Obtaining a profitable energy schedule is difficult due to the stochastic nature of solar irradiance and electricity prices. This article focuses on optimal energy planning for thermal solar trough plants, particularly by developing a model predictive control algorithm based on multiple scenarios to deal with uncertainties. The results obtained using the proposed scheme have been tested and compared to other well-known approaches to energy scheduling through a realistic and reliable comparison to evaluate their performances and establish their advantages and weaknesses. Simulations were carried out for a 50 MW parabolic trough concentrating solar plant with a thermal energy storage system, considering different types of days classified according to their solar irradiance, meteorological forecast, and electrical market. Simulation results show that the proposed method outperforms other scheduling methods in dealing with uncertainties by selling energy to the grid at the right times, generating the highest income of about 7.58%.

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  • Velarde, Pablo & Gallego, Antonio J. & Bordons, Carlos & Camacho, Eduardo F., 2023. "Scenario-based model predictive control for energy scheduling in a parabolic trough concentrating solar plant with thermal storage," Renewable Energy, Elsevier, vol. 206(C), pages 1228-1238.
    • Handle: RePEc:eee:renene:v:206:y:2023:i:c:p:1228-1238
    DOI: 10.1016/j.renene.2023.02.114
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    References listed on IDEAS

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    2. Behzadi, Amirmohammad & Sadrizadeh, Sasan, 2023. "Grid-tied solar and biomass hybridization for multi-family houses in Sweden: An optimal rule-based control framework through machine learning approach," Renewable Energy, Elsevier, vol. 218(C).

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