IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v252y2019ic18.html
   My bibliography  Save this article

Prototype of a simulation framework for georeferenced large-scale dynamic simulations of district energy systems

Author

Listed:
  • Nageler, P.
  • Heimrath, R.
  • Mach, T.
  • Hochenauer, C.

Abstract

With the increasing complexity of district energy systems, both researchers and enterprises require ever more complex modelling and simulation methods. This has resulted in the need for new approaches that can be taken to create large-scale simulations as well as new methods to clearly visualize their dynamic simulation results. This study presents a prototype of a simulation framework for large-scale simulations of district energy systems that offers three main advantages compared to the state of the art: (i) scalable simulation models are made possible; (ii) a novel heating network model generation tool was developed that can be used to create models for the IDA ICE simulation environment; and (iii) the dynamic results of the simulation of the heating network and the buildings can be visualized as animations in QGIS, which makes it possible to clearly visualize the superimposed result layers of the buildings and the network. The prototype was tested in a two-part case study. In the first part of the study, the extent of the district heating network model size was examined, and decoupling methods were applied to 1531 customers. The second part of the study involved a failure scenario, which evaluated the effects of a malfunction at the heat generation plant on the 469 connected buildings. One significant finding was that the temperature progression in the network reacts comparatively quickly, while the room air temperature in well-insulated buildings with larger thermal storage masses cool more slowly. Another finding was that IDA ICE allows fast and accurate simulations with up to 8200 customers or 36,000 pipes in meshed heating networks without parallelization techniques.

Suggested Citation

  • Nageler, P. & Heimrath, R. & Mach, T. & Hochenauer, C., 2019. "Prototype of a simulation framework for georeferenced large-scale dynamic simulations of district energy systems," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
  • Handle: RePEc:eee:appene:v:252:y:2019:i:c:18
    DOI: 10.1016/j.apenergy.2019.113469
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261919311432
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2019.113469?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    2. Schweiger, Gerald & Heimrath, Richard & Falay, Basak & O'Donovan, Keith & Nageler, Peter & Pertschy, Reinhard & Engel, Georg & Streicher, Wolfgang & Leusbrock, Ingo, 2018. "District energy systems: Modelling paradigms and general-purpose tools," Energy, Elsevier, vol. 164(C), pages 1326-1340.
    3. Fuchs, Marcus & Teichmann, Jens & Lauster, Moritz & Remmen, Peter & Streblow, Rita & Müller, Dirk, 2016. "Workflow automation for combined modeling of buildings and district energy systems," Energy, Elsevier, vol. 117(P2), pages 478-484.
    4. Dénarié, A. & Aprile, M. & Motta, M., 2019. "Heat transmission over long pipes: New model for fast and accurate district heating simulations," Energy, Elsevier, vol. 166(C), pages 267-276.
    5. 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.
    6. Nageler, P. & Zahrer, G. & Heimrath, R. & Mach, T. & Mauthner, F. & Leusbrock, I. & Schranzhofer, H. & Hochenauer, C., 2017. "Novel validated method for GIS based automated dynamic urban building energy simulations," Energy, Elsevier, vol. 139(C), pages 142-154.
    7. Guelpa, Elisa & Toro, Claudia & Sciacovelli, Adriano & Melli, Roberto & Sciubba, Enrico & Verda, Vittorio, 2016. "Optimal operation of large district heating networks through fast fluid-dynamic simulation," Energy, Elsevier, vol. 102(C), pages 586-595.
    8. Vandermeulen, Annelies & van der Heijde, Bram & Helsen, Lieve, 2018. "Controlling district heating and cooling networks to unlock flexibility: A review," Energy, Elsevier, vol. 151(C), pages 103-115.
    9. Sartor, K. & Dewalef, P., 2017. "Experimental validation of heat transport modelling in district heating networks," Energy, Elsevier, vol. 137(C), pages 961-968.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    2. Heidenthaler, Daniel & Deng, Yingwen & Leeb, Markus & Grobbauer, Michael & Kranzl, Lukas & Seiwald, Lena & Mascherbauer, Philipp & Reindl, Patricia & Bednar, Thomas, 2023. "Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts," Energy, Elsevier, vol. 278(PB).
    3. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    4. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. 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).
    6. Michael Mans & Tobias Blacha & Thomas Schreiber & Dirk Müller, 2022. "Development and Application of an Open-Source Framework for Automated Thermal Network Generation and Simulations in Modelica," Energies, MDPI, vol. 15(12), pages 1-25, June.

    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. Johan Simonsson & Khalid Tourkey Atta & Gerald Schweiger & Wolfgang Birk, 2021. "Experiences from City-Scale Simulation of Thermal Grids," Resources, MDPI, vol. 10(2), pages 1-20, January.
    2. Nageler, P. & Schweiger, G. & Schranzhofer, H. & Mach, T. & Heimrath, R. & Hochenauer, C., 2018. "Novel method to simulate large-scale thermal city models," Energy, Elsevier, vol. 157(C), pages 633-646.
    3. Vivian, Jacopo & Quaggiotto, Davide & Zarrella, Angelo, 2020. "Increasing the energy flexibility of existing district heating networks through flow rate variations," Applied Energy, Elsevier, vol. 275(C).
    4. Cai, Hanmin & You, Shi & Wu, Jianzhong, 2020. "Agent-based distributed demand response in district heating systems," Applied Energy, Elsevier, vol. 262(C).
    5. Egging-Bratseth, Ruud & Kauko, Hanne & Knudsen, Brage Rugstad & Bakke, Sara Angell & Ettayebi, Amina & Haufe, Ina Renate, 2021. "Seasonal storage and demand side management in district heating systems with demand uncertainty," Applied Energy, Elsevier, vol. 285(C).
    6. 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.
    7. Hirsch, Hauke & Nicolai, Andreas, 2022. "An efficient numerical solution method for detailed modelling of large 5th generation district heating and cooling networks," Energy, Elsevier, vol. 255(C).
    8. Hering, Dominik & Cansev, Mehmet Ege & Tamassia, Eugenio & Xhonneux, André & Müller, Dirk, 2021. "Temperature control of a low-temperature district heating network with Model Predictive Control and Mixed-Integer Quadratically Constrained Programming," Energy, Elsevier, vol. 224(C).
    9. Moser, A. & Muschick, D. & Gölles, M. & Nageler, P. & Schranzhofer, H. & Mach, T. & Ribas Tugores, C. & Leusbrock, I. & Stark, S. & Lackner, F. & Hofer, A., 2020. "A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis," Applied Energy, Elsevier, vol. 261(C).
    10. Guelpa, Elisa & Verda, Vittorio, 2021. "Demand response and other demand side management techniques for district heating: A review," Energy, Elsevier, vol. 219(C).
    11. Zheng, Xuejing & Shi, Zhiyuan & Wang, Yaran & Zhang, Huan & Liu, Huzhen, 2023. "Thermo-hydraulic condition optimization of large-scale complex district heating network: A case study of Tianjin," Energy, Elsevier, vol. 266(C).
    12. Zheng, Xuejing & Sun, Qihang & Wang, Yaran & Zheng, Lijun & Gao, Xinyong & You, Shijun & Zhang, Huan & Shi, Kaiyu, 2021. "Thermo-hydraulic coupled simulation and analysis of a real large-scale complex district heating network in Tianjin," Energy, Elsevier, vol. 236(C).
    13. Ksenija Stepanovic & Jichen Wu & Rob Everhardt & Mathijs de Weerdt, 2022. "Unlocking the Flexibility of District Heating Pipeline Energy Storage with Reinforcement Learning," Energies, MDPI, vol. 15(9), pages 1-25, April.
    14. Wang, Hai & Meng, Hua, 2018. "Improved thermal transient modeling with new 3-order numerical solution for a district heating network with consideration of the pipe wall's thermal inertia," Energy, Elsevier, vol. 160(C), pages 171-183.
    15. Michael Mans & Tobias Blacha & Thomas Schreiber & Dirk Müller, 2022. "Development and Application of an Open-Source Framework for Automated Thermal Network Generation and Simulations in Modelica," Energies, MDPI, vol. 15(12), pages 1-25, June.
    16. Wendel, Frank & Blesl, Markus & Brodecki, Lukasz & Hufendiek, Kai, 2022. "Expansion or decommission? – Transformation of existing district heating networks by reducing temperature levels in a cost-optimum network design," Applied Energy, Elsevier, vol. 310(C).
    17. Heidenthaler, Daniel & Deng, Yingwen & Leeb, Markus & Grobbauer, Michael & Kranzl, Lukas & Seiwald, Lena & Mascherbauer, Philipp & Reindl, Patricia & Bednar, Thomas, 2023. "Automated energy performance certificate based urban building energy modelling approach for predicting heat load profiles of districts," Energy, Elsevier, vol. 278(PB).
    18. 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).
    19. De Jaeger, Ina & Reynders, Glenn & Ma, Yixiao & Saelens, Dirk, 2018. "Impact of building geometry description within district energy simulations," Energy, Elsevier, vol. 158(C), pages 1060-1069.
    20. 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).

    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:eee:appene:v:252:y:2019:i:c:18. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.