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Data informed physical models for district heating grids with distributed heat sources to understand thermal and hydraulic aspects

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  • Nord, Natasa
  • Shakerin, Mohammad
  • Tereshchenko, Tymofii
  • Verda, Vittorio
  • Borchiellini, Romano

Abstract

The aim of the study was to develop data informed physical models for simulation of district heating (DH) grids for better presentation of hydraulic and thermal aspects in the DH grids integrating heat prosumers. A DH grid organized as a ring and integrating a heat prosumer from a data center was analyzed. In this study, an extensive analysis for thermal and hydraulic aspects of theDH grid considering different configurations of distributed sources was performed. Different configurations for the prosumer connection, the return to return and the return to supply, together with the pressure and temperature control, were investigated. The results showed that increasing the share of renewable heat from the prosumer to the DH grid caused a pressure imbalance in substations close to it. Variable speed pump control was the solution for these issues and it gave up to 34% electricity savings. Lowering temperature levels in the DH network led to a decrease in DH heat losses of up to 14%. The return to supply configuration showed advantages in integrating the prosumer, as regards lower return temperatures and better waste heat utilization. The results indicated the main hydraulic and thermal features of integrating the prosumer in the DH grid.

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  • Nord, Natasa & Shakerin, Mohammad & Tereshchenko, Tymofii & Verda, Vittorio & Borchiellini, Romano, 2021. "Data informed physical models for district heating grids with distributed heat sources to understand thermal and hydraulic aspects," Energy, Elsevier, vol. 222(C).
    • Handle: RePEc:eee:energy:v:222:y:2021:i:c:s0360544221002140
    DOI: 10.1016/j.energy.2021.119965
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    References listed on IDEAS

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    1. Li, Haoran & Hou, Juan & Hong, Tianzhen & Nord, Natasa, 2022. "Distinguish between the economic optimal and lowest distribution temperatures for heat-prosumer-based district heating systems with short-term thermal energy storage," Energy, Elsevier, vol. 248(C).
    2. Jan Stock & André Xhonneux & Dirk Müller, 2022. "Framework for the Automated Identification of Possible District Heating Separations to Utilise Present Heat Sources Based on Existing Network Topology," Energies, MDPI, vol. 15(21), pages 1-31, November.
    3. Hering, Dominik & Faller, Michael R. & Xhonneux, André & Müller, Dirk, 2022. "Operational optimization of a 4th generation district heating network with mixed integer quadratically constrained programming," Energy, Elsevier, vol. 250(C).
    4. Edtmayer, Hermann & Nageler, Peter & Heimrath, Richard & Mach, Thomas & Hochenauer, Christoph, 2021. "Investigation on sector coupling potentials of a 5th generation district heating and cooling network," Energy, Elsevier, vol. 230(C).
    5. Giuseppe Edoardo Dino & Pietro Catrini & Valeria Palomba & Andrea Frazzica & Antonio Piacentino, 2023. "Promoting the Flexibility of Thermal Prosumers Equipped with Heat Pumps to Support Power Grid Management," Sustainability, MDPI, vol. 15(9), pages 1-22, May.
    6. Dino, Giuseppe Edoardo & Catrini, Pietro & Buscemi, Alessandro & Piacentino, Antonio & Palomba, Valeria & Frazzica, Andrea, 2023. "Modeling of a bidirectional substation in a district heating network: Validation, dynamic analysis, and application to a solar prosumer," Energy, Elsevier, vol. 284(C).
    7. Hou, Juan & Li, Haoran & Nord, Natasa & Huang, Gongsheng, 2023. "Model predictive control for a university heat prosumer with data centre waste heat and thermal energy storage," Energy, Elsevier, vol. 267(C).
    8. Frafjord, Aksel Johan & Radicke, Jan-Philip & Keprate, Arvind & Komulainen, Tiina M., 2024. "Data-driven approaches for deriving a soft sensor in a district heating network," Energy, Elsevier, vol. 292(C).
    9. Li, Haoran & Hou, Juan & Tian, Zhiyong & Hong, Tianzhen & Nord, Natasa & Rohde, Daniel, 2022. "Optimize heat prosumers' economic performance under current heating price models by using water tank thermal energy storage," Energy, Elsevier, vol. 239(PB).

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