IDEAS home Printed from https://ideas.repec.org/a/gam/jresou/v10y2021i5p52-d557075.html
   My bibliography  Save this article

A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model

Author

Listed:
  • Annette Steingrube

    (Fraunhofer Institute for Solar Energy Systems, Heidenhofstr. 2, 79110 Freiburg, Germany)

  • Keyu Bao

    (Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany)

  • Stefan Wieland

    (Fraunhofer Institute for Solar Energy Systems, Heidenhofstr. 2, 79110 Freiburg, Germany)

  • Andrés Lalama

    (Institute for Visualization and Interactive Systems, Faculty of Computer Science, Electrical Engineering and Information Technology, University of Stuttgart, Keplerstraße 7, 70174 Stuttgart, Germany)

  • Pithon M. Kabiro

    (Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany)

  • Volker Coors

    (Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany)

  • Bastian Schröter

    (Stuttgart University of Applied Sciences, Schellingstr. 24, 70174 Stuttgart, Germany)

Abstract

District heating is seen as an important concept to decarbonize heating systems and meet climate mitigation goals. However, the decision related to where central heating is most viable is dependent on many different aspects, like heating densities or current heating structures. An urban energy simulation platform based on 3D building objects can improve the accuracy of energy demand calculation on building level, but lacks a system perspective. Energy system models help to find economically optimal solutions for entire energy systems, including the optimal amount of centrally supplied heat, but do not usually provide information on building level. Coupling both methods through a novel heating grid disaggregation algorithm, we propose a framework that does three things simultaneously: optimize energy systems that can comprise all demand sectors as well as sector coupling, assess the role of centralized heating in such optimized energy systems, and determine the layouts of supplying district heating grids with a spatial resolution on the street level. The algorithm is tested on two case studies; one, an urban city quarter, and the other, a rural town. In the urban city quarter, district heating is economically feasible in all scenarios. Using heat pumps in addition to CHPs increases the optimal amount of centrally supplied heat. In the rural quarter, central heat pumps guarantee the feasibility of district heating, while standalone CHPs are more expensive than decentral heating technologies.

Suggested Citation

  • Annette Steingrube & Keyu Bao & Stefan Wieland & Andrés Lalama & Pithon M. Kabiro & Volker Coors & Bastian Schröter, 2021. "A Method for Optimizing and Spatially Distributing Heating Systems by Coupling an Urban Energy Simulation Platform and an Energy System Model," Resources, MDPI, vol. 10(5), pages 1-19, May.
    • Handle: RePEc:gam:jresou:v:10:y:2021:i:5:p:52-:d:557075
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2079-9276/10/5/52/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2079-9276/10/5/52/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Keirstead, James & Calderon, Carlos, 2012. "Capturing spatial effects, technology interactions, and uncertainty in urban energy and carbon models: Retrofitting newcastle as a case-study," Energy Policy, Elsevier, vol. 46(C), pages 253-267.
    2. Scott Kelly, 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model for England's residential sector," Working Papers EPRG 1117, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    3. Persson, Urban & Werner, Sven, 2011. "Heat distribution and the future competitiveness of district heating," Applied Energy, Elsevier, vol. 88(3), pages 568-576, March.
    4. Omu, Akomeno & Choudhary, Ruchi & Boies, Adam, 2013. "Distributed energy resource system optimisation using mixed integer linear programming," Energy Policy, Elsevier, vol. 61(C), pages 249-266.
    5. Kelly, Scott, 2011. "Do homes that are more energy efficient consume less energy?: A structural equation model of the English residential sector," Energy, Elsevier, vol. 36(9), pages 5610-5620.
    6. Richter, Jan, 2011. "DIMENSION - A Dispatch and Investment Model for European Electricity Markets," EWI Working Papers 2011-3, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    7. Jalil-Vega, F. & Hawkes, A.D., 2018. "Spatially resolved model for studying decarbonisation pathways for heat supply and infrastructure trade-offs," Applied Energy, Elsevier, vol. 210(C), pages 1051-1072.
    8. Nussbaumer, T. & Thalmann, S., 2016. "Influence of system design on heat distribution costs in district heating," Energy, Elsevier, vol. 101(C), pages 496-505.
    9. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2016. "Optimising urban energy systems: Simultaneous system sizing, operation and district heating network layout," Energy, Elsevier, vol. 116(P1), pages 619-636.
    10. 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.
    11. Björkqvist, Olof & Idefeldt, Jim & Larsson, Aron, 2010. "Risk assessment of new pricing strategies in the district heating market: A case study at Sundsvall Energi AB," Energy Policy, Elsevier, vol. 38(5), pages 2171-2178, May.
    12. Lukač, Niko & Žlaus, Danijel & Seme, Sebastijan & Žalik, Borut & Štumberger, Gorazd, 2013. "Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data," Applied Energy, Elsevier, vol. 102(C), pages 803-812.
    13. Unternährer, Jérémy & Moret, Stefano & Joost, Stéphane & Maréchal, François, 2017. "Spatial clustering for district heating integration in urban energy systems: Application to geothermal energy," Applied Energy, Elsevier, vol. 190(C), pages 749-763.
    14. Jalil-Vega, Francisca & Hawkes, Adam D., 2018. "The effect of spatial resolution on outcomes from energy systems modelling of heat decarbonisation," Energy, Elsevier, vol. 155(C), pages 339-350.
    15. Zirak, Maryam & Weiler, Verena & Hein, Martin & Eicker, Ursula, 2020. "Urban models enrichment for energy applications: Challenges in energy simulation using different data sources for building age information," Energy, Elsevier, vol. 190(C).
    16. Prina, Matteo Giacomo & Casalicchio, Valeria & Kaldemeyer, Cord & Manzolini, Giampaolo & Moser, David & Wanitschke, Alexander & Sparber, Wolfram, 2020. "Multi-objective investment optimization for energy system models in high temporal and spatial resolution," Applied Energy, Elsevier, vol. 264(C).
    17. Keyu Bao & Rushikesh Padsala & Volker Coors & Daniela Thrän & Bastian Schröter, 2020. "A Method for Assessing Regional Bioenergy Potentials Based on GIS Data and a Dynamic Yield Simulation Model," Energies, MDPI, vol. 13(24), pages 1-24, December.
    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. Kachirayil, Febin & Weinand, Jann Michael & Scheller, Fabian & McKenna, Russell, 2022. "Reviewing local and integrated energy system models: insights into flexibility and robustness challenges," Applied Energy, Elsevier, vol. 324(C).
    2. Hyunkyo Yu & Erik O. Ahlgren, 2023. "Enhancing Urban Heating Systems Planning through Spatially Explicit Participatory Modeling," Energies, MDPI, vol. 16(11), pages 1-26, May.

    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. Sachs, Julia & Moya, Diego & Giarola, Sara & Hawkes, Adam, 2019. "Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector," Applied Energy, Elsevier, vol. 250(C), pages 48-62.
    2. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    3. Persson, Urban & Wiechers, Eva & Möller, Bernd & Werner, Sven, 2019. "Heat Roadmap Europe: Heat distribution costs," Energy, Elsevier, vol. 176(C), pages 604-622.
    4. Chambers, Jonathan & Narula, Kapil & Sulzer, Matthias & Patel, Martin K., 2019. "Mapping district heating potential under evolving thermal demand scenarios and technologies: A case study for Switzerland," Energy, Elsevier, vol. 176(C), pages 682-692.
    5. Prasanna, Ashreeta & Dorer, Viktor & Vetterli, Nadège, 2017. "Optimisation of a district energy system with a low temperature network," Energy, Elsevier, vol. 137(C), pages 632-648.
    6. Ziemele, Jelena & Gravelsins, Armands & Blumberga, Andra & Blumberga, Dagnija, 2017. "Sustainability of heat energy tariff in district heating system: Statistic and dynamic methodologies," Energy, Elsevier, vol. 137(C), pages 834-845.
    7. Wirtz, Marco & Kivilip, Lukas & Remmen, Peter & Müller, Dirk, 2020. "5th Generation District Heating: A novel design approach based on mathematical optimization," Applied Energy, Elsevier, vol. 260(C).
    8. Ramos-Teodoro, Jerónimo & Álvarez, José Domingo & Torres, José Luis, 2024. "A methodology for feasibility analyses of district heating networks: A case study applied to greenhouse crops," Energy, Elsevier, vol. 301(C).
    9. Mengting Jiang & Camilo Rindt & David M. J. Smeulders, 2022. "Optimal Planning of Future District Heating Systems—A Review," Energies, MDPI, vol. 15(19), pages 1-38, September.
    10. Pakere, Ieva & Gravelsins, Armands & Lauka, Dace & Bazbauers, Gatis & Blumberga, Dagnija, 2021. "Linking energy efficiency policies toward 4th generation district heating system," Energy, Elsevier, vol. 234(C).
    11. Jalil-Vega, Francisca & Hawkes, Adam D., 2018. "The effect of spatial resolution on outcomes from energy systems modelling of heat decarbonisation," Energy, Elsevier, vol. 155(C), pages 339-350.
    12. Salah Vaisi & Saleh Mohammadi & Kyoumars Habibi, 2021. "Heat Mapping, a Method for Enhancing the Sustainability of the Smart District Heat Networks," Energies, MDPI, vol. 14(17), pages 1-17, September.
    13. Bachmann, Max & Kriegel, Martin, 2023. "Assessing the heat distribution costs of linear and radial district heating networks: A methodological approach," Energy, Elsevier, vol. 276(C).
    14. Canet, Alexandre & Qadrdan, Meysam & Jenkins, Nick, 2021. "Heat demand mapping and assessment of heat supply options for local areas – The case study of Neath Port Talbot," Energy, Elsevier, vol. 217(C).
    15. Ziemele, Jelena & Cilinskis, Einars & Blumberga, Dagnija, 2018. "Pathway and restriction in district heating systems development towards 4th generation district heating," Energy, Elsevier, vol. 152(C), pages 108-118.
    16. Fodstad, Marte & Crespo del Granado, Pedro & Hellemo, Lars & Knudsen, Brage Rugstad & Pisciella, Paolo & Silvast, Antti & Bordin, Chiara & Schmidt, Sarah & Straus, Julian, 2022. "Next frontiers in energy system modelling: A review on challenges and the state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    17. Jie, Pengfei & Kong, Xiangfei & Rong, Xian & Xie, Shangqun, 2016. "Selecting the optimum pressure drop per unit length of district heating piping network based on operating strategies," Applied Energy, Elsevier, vol. 177(C), pages 341-353.
    18. Li, Yu & Rezgui, Yacine & Zhu, Hanxing, 2017. "District heating and cooling optimization and enhancement – Towards integration of renewables, storage and smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 281-294.
    19. Jie, Pengfei & Zhao, Wanyue & Li, Fating & Wei, Fengjun & Li, Jing, 2020. "Optimizing the pressure drop per unit length of district heating piping networks from an environmental perspective," Energy, Elsevier, vol. 202(C).
    20. Morvaj, Boran & Evins, Ralph & Carmeliet, Jan, 2017. "Decarbonizing the electricity grid: The impact on urban energy systems, distribution grids and district heating potential," Applied Energy, Elsevier, vol. 191(C), pages 125-140.

    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:jresou:v:10:y:2021:i:5:p:52-:d:557075. 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.