IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3316-d1118589.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3316/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/8/3316/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jae-Ki Byun & Young-Don Choi & Jong-Keun Shin & Myung-Ho Park & Dong-Kurl Kwak, 2012. "Study on the Development of an Optimal Heat Supply Control Algorithm for Group Energy Apartment Buildings According to the Variation of Outdoor Air Temperature," Energies, MDPI, vol. 5(5), pages 1-19, May.
    2. Weinand, Jann Michael & Kleinebrahm, Max & McKenna, Russell & Mainzer, Kai & Fichtner, Wolf, 2019. "Developing a combinatorial optimisation approach to design district heating networks based on deep geothermal energy," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    3. Difs, Kristina & Danestig, Maria & Trygg, Louise, 2009. "Increased use of district heating in industrial processes - Impacts on heat load duration," Applied Energy, Elsevier, vol. 86(11), pages 2327-2334, November.
    4. Wack, Yannick & Serra, Sylvain & Baelmans, Martine & Reneaume, Jean-Michel & Blommaert, Maarten, 2023. "Nonlinear topology optimization of District Heating Networks: A benchmark of a mixed-integer and a density-based approach," Energy, Elsevier, vol. 278(PB).
    5. Cai, Hanmin & Ziras, Charalampos & You, Shi & Li, Rongling & Honoré, Kristian & Bindner, Henrik W., 2018. "Demand side management in urban district heating networks," Applied Energy, Elsevier, vol. 230(C), pages 506-518.
    6. Wack, Yannick & Baelmans, Martine & Salenbien, Robbe & Blommaert, Maarten, 2023. "Economic topology optimization of District Heating Networks using a pipe penalization approach," Energy, Elsevier, vol. 264(C).
    7. Keirstead, James & Jennings, Mark & Sivakumar, Aruna, 2012. "A review of urban energy system models: Approaches, challenges and opportunities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3847-3866.
    8. Felten, Björn, 2020. "An integrated model of coupled heat and power sectors for large-scale energy system analyses," Applied Energy, Elsevier, vol. 266(C).
    9. 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).
    10. Aunedi, Marko & Pantaleo, Antonio Marco & Kuriyan, Kamal & Strbac, Goran & Shah, Nilay, 2020. "Modelling of national and local interactions between heat and electricity networks in low-carbon energy systems," Applied Energy, Elsevier, vol. 276(C).
    11. Ba, Birome Holo & Prins, Christian & Prodhon, Caroline, 2016. "Models for optimization and performance evaluation of biomass supply chains: An Operations Research perspective," Renewable Energy, Elsevier, vol. 87(P2), pages 977-989.
    12. Magnus Dahl & Adam Brun & Oliver S. Kirsebom & Gorm B. Andresen, 2018. "Improving Short-Term Heat Load Forecasts with Calendar and Holiday Data," Energies, MDPI, vol. 11(7), pages 1-16, June.
    13. Soheil Kavian & Mohsen Saffari Pour & Ali Hakkaki-Fard, 2019. "Optimized Design of the District Heating System by Considering the Techno-Economic Aspects and Future Weather Projection," Energies, MDPI, vol. 12(9), pages 1-30, May.
    14. Yong Zeng & Yanpeng Cai & Guohe Huang & Jing Dai, 2011. "A Review on Optimization Modeling of Energy Systems Planning and GHG Emission Mitigation under Uncertainty," Energies, MDPI, vol. 4(10), pages 1-33, October.
    15. Talebi, Behrang & Haghighat, Fariborz & Tuohy, Paul & Mirzaei, Parham A., 2018. "Validation of a community district energy system model using field measured data," Energy, Elsevier, vol. 144(C), pages 694-706.
    16. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    17. Eriksson, Anders & Eliasson, Lars & Sikanen, Lauri & Hansson, Per-Anders & Jirjis, Raida, 2017. "Evaluation of delivery strategies for forest fuels applying a model for Weather-driven Analysis of Forest Fuel Systems (WAFFS)," Applied Energy, Elsevier, vol. 188(C), pages 420-430.
    18. Merlet, Yannis & Baviere, Roland & Vasset, Nicolas, 2022. "Formulation and assessment of multi-objective optimal sizing of district heating network," Energy, Elsevier, vol. 252(C).
    19. Lozano, Miguel A. & Ramos, Jose C. & Serra, Luis M., 2010. "Cost optimization of the design of CHCP (combined heat, cooling and power) systems under legal constraints," Energy, Elsevier, vol. 35(2), pages 794-805.
    20. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3316-:d:1118589. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.