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District Heating System Optimisation: A Three-Phase Thermo-Hydraulic Linear Model

Author

Listed:
  • Piotr Pałka

    (Faculty of Electronics and Information Technology, Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warsaw, Poland)

  • Marcin Malec

    (Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, Wybickiego 7A, 31-261 Kraków, Poland)

  • Przemysław Kaszyński

    (Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, Wybickiego 7A, 31-261 Kraków, Poland)

  • Jacek Kamiński

    (Mineral and Energy Economy Research Institute of the Polish Academy of Sciences, Wybickiego 7A, 31-261 Kraków, Poland)

  • Piotr Saługa

    (WSB Academy, Cieplaka 1C, 41-300 Dąbrowa Górnicza, Poland)

Abstract

Investments in the development of the district heating system require a thorough analysis of the technical, economic, and legal aspects. Regarding the technical and economic context, a mathematical model of the district heating system has been proposed. It optimizes both the technical and economic aspects of the function and development of a district heating system. To deal with non-linearities, the developed linear programming model is divided into three phases: flow, thermal, and pressure. Therein, non-linear dependencies are calculated between the linear optimization phases. This paper presents the main assumptions and equations that were used to calculate the parameters of the heating system, along with the optimal level of heat production, the flow rate of the heating medium in the heat nodes and edges of the network graph, the heat, power, and temperature losses at each edge, and the purchase costs of heat and its transmission. The operation of the model was tested on a real-world district heating system. The case study results confirm that the model is effective and can be used in decision support. The economic results of the model, before the calibration process, were 3.6% different from historical values. After the calibration process, they were very similar to the real data—all percentage deviations were within 1%.

Suggested Citation

  • Piotr Pałka & Marcin Malec & Przemysław Kaszyński & Jacek Kamiński & Piotr Saługa, 2023. "District Heating System Optimisation: A Three-Phase Thermo-Hydraulic Linear Model," Energies, MDPI, vol. 16(8), pages 1-18, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3316-:d:1118589
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    References listed on IDEAS

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    1. Thibaut Résimont & Quentin Louveaux & Pierre Dewallef, 2021. "Optimization Tool for the Strategic Outline and Sizing of District Heating Networks Using a Geographic Information System," Energies, MDPI, vol. 14(17), pages 1-24, September.
    2. Chinese, Damiana & Meneghetti, Antonella, 2005. "Optimisation models for decision support in the development of biomass-based industrial district-heating networks in Italy," Applied Energy, Elsevier, vol. 82(3), pages 228-254, November.
    3. Dotzauer, Erik, 2002. "Simple model for prediction of loads in district-heating systems," Applied Energy, Elsevier, vol. 73(3-4), pages 277-284, November.
    4. Blommaert, Maarten & Wack, Y. & Baelmans, M., 2020. "An adjoint optimization approach for the topological design of large-scale district heating networks based on nonlinear models," Applied Energy, Elsevier, vol. 280(C).
    5. Dotzauer, Erik, 2003. "Experiences in mid-term planning of district heating systems," Energy, Elsevier, vol. 28(15), pages 1545-1555.
    6. Delangle, Axelle & Lambert, Romain S.C. & Shah, Nilay & Acha, Salvador & Markides, Christos N., 2017. "Modelling and optimising the marginal expansion of an existing district heating network," Energy, Elsevier, vol. 140(P1), pages 209-223.
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