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Optimal sizing and control strategy of low temperature solar thermal utility systems

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  • Lizárraga-Morazán, Juan Ramón
  • Picón-Núñez, Martín

Abstract

This work describes the development of an integrated method for the design of networks of flat plate solar collectors and a temperature control strategy. The main stages of this methodology include the optimization of single collector geometry, size and structure of the network, energy storage system, and design of a temperature control system. Solar network inlet temperature and flow rate are maintained at its optimal value and outlet temperature is conditioned to meet the process specifications. Comparison with common temperature control based on the manipulation of network inlet flow rate is presented. A case study to supply 480 l/min of water at 70 °C is analysed. Results shows that the network operates efficiently throughout the year using the alternative control strategy. In terms of the solar network design, the results indicate a reduction of 52.3% in payback time compared to networks that use commercial collectors, reduction of 21.4% in payback compared to the purchase and installation of a natural gas boiler, and reduction in CO2 equivalent gas emissions of 759.6 t/y. In terms of the operation, the results show a reduction in pumping costs of about 24,584 USD/y, compared to the control strategy based on flow rate manipulation.

Suggested Citation

  • Lizárraga-Morazán, Juan Ramón & Picón-Núñez, Martín, 2023. "Optimal sizing and control strategy of low temperature solar thermal utility systems," Energy, Elsevier, vol. 263(PC).
  • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027219
    DOI: 10.1016/j.energy.2022.125835
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    1. 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).
    2. Correa-Jullian, Camila & Cardemil, José Miguel & López Droguett, Enrique & Behzad, Masoud, 2020. "Assessment of Deep Learning techniques for Prognosis of solar thermal systems," Renewable Energy, Elsevier, vol. 145(C), pages 2178-2191.
    3. Saloux, Etienne & Candanedo, José A., 2021. "Model-based predictive control to minimize primary energy use in a solar district heating system with seasonal thermal energy storage," Applied Energy, Elsevier, vol. 291(C).
    4. Silva, R. & Pérez, M. & Fernández-Garcia, A., 2013. "Modeling and co-simulation of a parabolic trough solar plant for industrial process heat," Applied Energy, Elsevier, vol. 106(C), pages 287-300.
    5. Hering, Dominik & Cansev, Mehmet Ege & Tamassia, Eugenio & Xhonneux, André & Müller, Dirk, 2021. "Temperature control of a low-temperature district heating network with Model Predictive Control and Mixed-Integer Quadratically Constrained Programming," Energy, Elsevier, vol. 224(C).
    6. Régis Delubac & Sylvain Serra & Sabine Sochard & Jean-Michel Reneaume, 2021. "A Dynamic Optimization Tool to Size and Operate Solar Thermal District Heating Networks Production Plants," Energies, MDPI, vol. 14(23), pages 1-27, November.
    7. Juan D. Gil & Jerónimo Ramos-Teodoro & José A. Romero-Ramos & Rodrigo Escobar & José M. Cardemil & Cynthia Giagnocavo & Manuel Pérez, 2021. "Demand-Side Optimal Sizing of a Solar Energy–Biomass Hybrid System for Isolated Greenhouse Environments: Methodology and Application Example," Energies, MDPI, vol. 14(13), pages 1-22, June.
    8. Huang, Junpeng & Fan, Jianhua & Furbo, Simon & Chen, Daochuan & Dai, Yanjun & Kong, Weiqiang, 2019. "Economic analysis and optimization of combined solar district heating technologies and systems," Energy, Elsevier, vol. 186(C).
    9. Sharma, Naveen & Varun, & Siddhartha,, 2012. "Stochastic techniques used for optimization in solar systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1399-1411.
    10. Gil, Juan D. & Mendes, Paulo R.C. & Camponogara, E. & Roca, Lidia & Álvarez, J.D. & Normey-Rico, Julio E., 2020. "A general optimal operating strategy for commercial membrane distillation facilities," Renewable Energy, Elsevier, vol. 156(C), pages 220-234.
    11. Tschopp, Daniel & Tian, Zhiyong & Berberich, Magdalena & Fan, Jianhua & Perers, Bengt & Furbo, Simon, 2020. "Large-scale solar thermal systems in leading countries: A review and comparative study of Denmark, China, Germany and Austria," Applied Energy, Elsevier, vol. 270(C).
    12. Lyons, Ben & O’Dwyer, Edward & Shah, Nilay, 2020. "Model reduction for Model Predictive Control of district and communal heating systems within cooperative energy systems," Energy, Elsevier, vol. 197(C).
    13. Wang, Chendong & Yuan, Jianjuan & Zhang, Ji & Deng, Na & Zhou, Zhihua & Gao, Feng, 2020. "Multi-criteria comprehensive study on predictive algorithm of heating energy consumption of district heating station based on timeseries processing," Energy, Elsevier, vol. 202(C).
    14. Leitner, Benedikt & Widl, Edmund & Gawlik, Wolfgang & Hofmann, René, 2020. "Control assessment in coupled local district heating and electrical distribution grids: Model predictive control of electric booster heaters," Energy, Elsevier, vol. 210(C).
    15. Gil, Juan D. & Topa, A. & Álvarez, J.D. & Torres, J.L. & Pérez, M., 2022. "A review from design to control of solar systems for supplying heat in industrial process applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    16. Bava, Federico & Furbo, Simon, 2017. "Development and validation of a detailed TRNSYS-Matlab model for large solar collector fields for district heating applications," Energy, Elsevier, vol. 135(C), pages 698-708.
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