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Thermo-fluid dynamic model of large district heating networks for the analysis of primary energy savings

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  • Guelpa, Elisa
  • Sciacovelli, Adriano
  • Verda, Vittorio

Abstract

Among the various heating technologies that can be applied to urban areas district heating is recognized to allow significant reduction in primary energy consumption, provided that the system is properly designed and operated. Thermo-fluid dynamic simulation tools can be of extreme importance in order to achieve this objective. This paper aims at presenting a thermo fluid dynamic model for the detailed simulation of large district heating network and showing how it can be usefully applied to examine options for the reduction of primary energy consumption. The model is tested using experimental data and then applied for analyzing transient operations of the Turin district heating network, which is the largest network in Italy and one of the largest in Europe. A comparison between simulations and experimental results shows that the model is able to predict the temperature in the nodes of the network with good accuracy. The thermal power required to each plant is also calculated with a good level of accuracy. The model can be used for the simulation of operational strategies, thus representing a versatile and important tool for the implementation of advanced management such as the installation of local storage units or the variation of user request schedules.

Suggested Citation

  • Guelpa, Elisa & Sciacovelli, Adriano & Verda, Vittorio, 2019. "Thermo-fluid dynamic model of large district heating networks for the analysis of primary energy savings," Energy, Elsevier, vol. 184(C), pages 34-44.
    • Handle: RePEc:eee:energy:v:184:y:2019:i:c:p:34-44
    DOI: 10.1016/j.energy.2017.07.177
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    Cited by:

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    2. Annelies Vandermeulen & Ina De Jaeger & Tijs Van Oevelen & Dirk Saelens & Lieve Helsen, 2020. "Analysis of Building Parameter Uncertainty in District Heating for Optimal Control of Network Flexibility," Energies, MDPI, vol. 13(23), pages 1-25, November.
    3. Dénarié, A. & Aprile, M. & Motta, M., 2023. "Dynamical modelling and experimental validation of a fast and accurate district heating thermo-hydraulic modular simulation tool," Energy, Elsevier, vol. 282(C).
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    5. Brown, Alastair & Foley, Aoife & Laverty, David & McLoone, Seán & Keatley, Patrick, 2022. "Heating and cooling networks: A comprehensive review of modelling approaches to map future directions," Energy, Elsevier, vol. 261(PB).
    6. Guelpa, E. & Capone, M. & Sciacovelli, A. & Vasset, N. & Baviere, R. & Verda, V., 2023. "Reduction of supply temperature in existing district heating: A review of strategies and implementations," Energy, Elsevier, vol. 262(PB).
    7. Capone, Martina & Guelpa, Elisa & Mancò, Giulia & Verda, Vittorio, 2021. "Integration of storage and thermal demand response to unlock flexibility in district multi-energy systems," Energy, Elsevier, vol. 237(C).
    8. 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).
    9. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).
    10. Dong, Zhe & Cheng, Zhonghua & Zhu, Yunlong & Zhang, Zuoyi & Dong, Yujie & Huang, Xiaojin, 2024. "Passivity-based control of fluid flow networks with capacitance," Energy, Elsevier, vol. 299(C).
    11. Wang, Yaran & Shi, Kaiyu & Zheng, Xuejing & You, Shijun & Zhang, Huan & Zhu, Chengzhi & Li, Liang & Wei, Shen & Ding, Chao & Wang, Na, 2020. "Thermo-hydraulic coupled analysis of meshed district heating networks based on improved breadth first search method," Energy, Elsevier, vol. 205(C).
    12. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2021. "Multi-objective optimization of district energy systems with demand response," Energy, Elsevier, vol. 227(C).
    13. Yang, Weijia & Huang, Yuping & Zhao, Daiqing, 2023. "A coupled hydraulic–thermal dynamic model for the steam network in a heat–electricity integrated energy system," Energy, Elsevier, vol. 263(PC).
    14. Boussaid, Taha & Rousset, François & Scuturici, Vasile-Marian & Clausse, Marc, 2024. "Enabling fast prediction of district heating networks transients via a physics-guided graph neural network," Applied Energy, Elsevier, vol. 370(C).
    15. Capone, Martina & Guelpa, Elisa & Verda, Vittorio, 2024. "Exploring opportunities for temperature reduction in existing district heating infrastructures," Energy, Elsevier, vol. 302(C).
    16. Manservigi, Lucrezia & Bahlawan, Hilal & Losi, Enzo & Morini, Mirko & Spina, Pier Ruggero & Venturini, Mauro, 2022. "A diagnostic approach for fault detection and identification in district heating networks," Energy, Elsevier, vol. 251(C).
    17. Martina Capone & Elisa Guelpa & Vittorio Verda, 2023. "Optimal Installation of Heat Pumps in Large District Heating Networks," Energies, MDPI, vol. 16(3), pages 1-23, February.

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