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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%.

Suggested Citation

  • 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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    1. Hemmati, Reza & Saboori, Hedayat & Jirdehi, Mehdi Ahmadi, 2017. "Stochastic planning and scheduling of energy storage systems for congestion management in electric power systems including renewable energy resources," Energy, Elsevier, vol. 133(C), pages 380-387.
    2. Mazzoni, Stefano & Ooi, Sean & Nastasi, Benedetto & Romagnoli, Alessandro, 2019. "Energy storage technologies as techno-economic parameters for master-planning and optimal dispatch in smart multi energy systems," Applied Energy, Elsevier, vol. 254(C).
    3. Ruiz-Moreno, Sara & Frejo, José Ramón D. & Camacho, Eduardo F., 2021. "Model predictive control based on deep learning for solar parabolic-trough plants," Renewable Energy, Elsevier, vol. 180(C), pages 193-202.
    4. Xiangjie Liu & Le Feng & Xiaobing Kong, 2022. "A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System," Energies, MDPI, vol. 15(13), pages 1-22, June.
    5. Deetjen, Thomas A. & Vitter, J. Scott & Reimers, Andrew S. & Webber, Michael E., 2018. "Optimal dispatch and equipment sizing of a residential central utility plant for improving rooftop solar integration," Energy, Elsevier, vol. 147(C), pages 1044-1059.
    6. P. Velarde & X. Tian & A. D. Sadowska & J. M. Maestre, 2019. "Scenario-Based Hierarchical and Distributed MPC for Water Resources Management with Dynamical Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 677-696, January.
    7. Masero, Eva & Maestre, José M. & Camacho, Eduardo F., 2022. "Market-based clustering of model predictive controllers for maximizing collected energy by parabolic-trough solar collector fields," Applied Energy, Elsevier, vol. 306(PA).
    8. Li, Rong & Guo, Su & Yang, Yong & Liu, Deyou, 2020. "Optimal sizing of wind/ concentrated solar plant/ electric heater hybrid renewable energy system based on two-stage stochastic programming," Energy, Elsevier, vol. 209(C).
    9. Francis A. Longstaff & Ashley W. Wang, 2004. "Electricity Forward Prices: A High-Frequency Empirical Analysis," Journal of Finance, American Finance Association, vol. 59(4), pages 1877-1900, August.
    10. Xin Tian & Yuxue Guo & Rudy R. Negenborn & Lingna Wei & Nay Myo Lin & José María Maestre, 2019. "Multi-Scenario Model Predictive Control Based on Genetic Algorithms for Level Regulation of Open Water Systems under Ensemble Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3025-3040, July.
    11. Herrmann, Ulf & Kelly, Bruce & Price, Henry, 2004. "Two-tank molten salt storage for parabolic trough solar power plants," Energy, Elsevier, vol. 29(5), pages 883-893.
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