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A Dynamic Optimization Tool to Size and Operate Solar Thermal District Heating Networks Production Plants

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  • Régis Delubac

    (LaTEP (Laboratory of Thermics, Energetics and Processes), E2S UPPA, Universite de Pau et des Pays de l’Adour, 64000 Pau, France)

  • Sylvain Serra

    (LaTEP (Laboratory of Thermics, Energetics and Processes), E2S UPPA, Universite de Pau et des Pays de l’Adour, 64000 Pau, France)

  • Sabine Sochard

    (LaTEP (Laboratory of Thermics, Energetics and Processes), E2S UPPA, Universite de Pau et des Pays de l’Adour, 64000 Pau, France)

  • Jean-Michel Reneaume

    (LaTEP (Laboratory of Thermics, Energetics and Processes), E2S UPPA, Universite de Pau et des Pays de l’Adour, 64000 Pau, France)

Abstract

The aim of the ISORC/OPTIMISER project is to increase and improve the use of solar thermal energy in district heating networks. One of the main tasks of the project is to develop an optimization tool for the sizing and operation of a solar district heating network. This is the first optimization tool using an open-source interface (Julia, JuMP) and solver (Ipopt) to solve nonlinear problems. This paper presents the multi-period optimization problem which is implemented to consider the dynamic variations in a year, represented by four typical days, with an hourly resolution. The optimum is calculated for a total duration of 20 years. First, this paper presents the modeling of the different components of a solar district heating network production plant: district network demand, storage and three sources, i.e., a fossil (gas) and two renewable (solar and biomass) sources. In order to avoid prohibitive computational time, the modeling of sources and storage has to be fairly simple. The multi-period optimization problem was formulated. The chosen objective function is economic: The provided economic model is accurate and use nonlinear equations. Finally the formulated problem is a nonlinear Programming problem. Optimization of the studied case exhibits consistent operating profiles and design. A comparison is made of different types of storage connection at the production site, highlighting the relevance of placing the storage at the solar field outlet. The optimum configuration supplies 49% of demand using solar energy, achieving a renewable rate of 69% in combination with the biomass boiler.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:8003-:d:691969
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    1. Sartor, K. & Quoilin, S. & Dewallef, P., 2014. "Simulation and optimization of a CHP biomass plant and district heating network," Applied Energy, Elsevier, vol. 130(C), pages 474-483.
    2. Kotzur, Leander & Markewitz, Peter & Robinius, Martin & Stolten, Detlef, 2018. "Time series aggregation for energy system design: Modeling seasonal storage," Applied Energy, Elsevier, vol. 213(C), pages 123-135.
    3. Casisi, M. & Pinamonti, P. & Reini, M., 2009. "Optimal lay-out and operation of combined heat & power (CHP) distributed generation systems," Energy, Elsevier, vol. 34(12), pages 2175-2183.
    4. Connolly, D. & Lund, H. & Mathiesen, B.V. & Leahy, M., 2010. "A review of computer tools for analysing the integration of renewable energy into various energy systems," Applied Energy, Elsevier, vol. 87(4), pages 1059-1082, April.
    5. Limpens, Gauthier & Moret, Stefano & Jeanmart, Hervé & Maréchal, Francois, 2019. "EnergyScope TD: A novel open-source model for regional energy systems," Applied Energy, Elsevier, vol. 255(C).
    6. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
    7. Mazhar, Abdur Rehman & Liu, Shuli & Shukla, Ashish, 2018. "A state of art review on the district heating systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 420-439.
    8. van der Heijde, Bram & Vandermeulen, Annelies & Salenbien, Robbe & Helsen, Lieve, 2019. "Representative days selection for district energy system optimisation: a solar district heating system with seasonal storage," Applied Energy, Elsevier, vol. 248(C), pages 79-94.
    9. Mertz, Théophile & Serra, Sylvain & Henon, Aurélien & Reneaume, Jean-Michel, 2016. "A MINLP optimization of the configuration and the design of a district heating network: Academic study cases," Energy, Elsevier, vol. 117(P2), pages 450-464.
    10. Lake, Andrew & Rezaie, Behanz & Beyerlein, Steven, 2017. "Review of district heating and cooling systems for a sustainable future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 417-425.
    11. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout," Energy, Elsevier, vol. 116(P1), pages 619-636.
    12. Fabien Marty & Sylvain Serra & Sabine Sochard & Jean-Michel Reneaume, 2019. "Exergy Analysis and Optimization of a Combined Heat and Power Geothermal Plant," Energies, MDPI, vol. 12(6), pages 1-22, March.
    13. Wang, Yang & Zhang, Shanhong & Chow, David & Kuckelkorn, Jens M., 2021. "Evaluation and optimization of district energy network performance: Present and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    14. Lund, Henrik & Werner, Sven & Wiltshire, Robin & Svendsen, Svend & Thorsen, Jan Eric & Hvelplund, Frede & Mathiesen, Brian Vad, 2014. "4th Generation District Heating (4GDH)," Energy, Elsevier, vol. 68(C), pages 1-11.
    15. Gao, Lei & Hwang, Yunho & Cao, Tao, 2019. "An overview of optimization technologies applied in combined cooling, heating and power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    16. Carpaneto, E. & Lazzeroni, P. & Repetto, M., 2015. "Optimal integration of solar energy in a district heating network," Renewable Energy, Elsevier, vol. 75(C), pages 714-721.
    17. Werner, Sven, 2017. "International review of district heating and cooling," Energy, Elsevier, vol. 137(C), pages 617-631.
    18. Roos, Philipp & Haselbacher, Andreas, 2021. "Thermocline control through multi-tank thermal-energy storage systems," Applied Energy, Elsevier, vol. 281(C).
    19. Gauthier Limpens & Hervé Jeanmart & Francois Maréchal, 2020. "Belgian Energy Transition: What Are the Options?," Energies, MDPI, vol. 13(1), pages 1-29, January.
    20. Buoro, Dario & Pinamonti, Piero & Reini, Mauro, 2014. "Optimization of a Distributed Cogeneration System with solar district heating," Applied Energy, Elsevier, vol. 124(C), pages 298-308.
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    1. Régis Delubac & Mohammad Sadr & Sabine Sochard & Sylvain Serra & Jean-Michel Reneaume, 2023. "Optimized Operation and Sizing of Solar District Heating Networks with Small Daily Storage," Energies, MDPI, vol. 16(3), pages 1-20, January.
    2. Untrau, Alix & Sochard, Sabine & Marias, Frédéric & Reneaume, Jean-Michel & Le Roux, Galo A.C. & Serra, Sylvain, 2024. "Storage management in a rolling horizon Dynamic Real-Time Optimization (DRTO) methodology for a non-concentrating solar thermal plant for low temperature heat production," Applied Energy, Elsevier, vol. 360(C).
    3. 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).

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