IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i14p4078-d589438.html
   My bibliography  Save this article

A High Resolution Spatiotemporal Urban Heat Load Model for GB

Author

Listed:
  • Salman Siddiqui

    (UCL Energy Institute, London WC1H 0NN, UK)

  • Mark Barrett

    (UCL Energy Institute, London WC1H 0NN, UK)

  • John Macadam

    (UCL Energy Institute, London WC1H 0NN, UK)

Abstract

The decarbonisation of heating in the United Kingdom is likely to entail both the mass adoption of heat pumps and widespread development of district heating infrastructure. Estimation of the spatially disaggregated heat demand is needed for both electrical distribution network with electrified heating and for the development of district heating. The temporal variation of heat demand is important when considering the operation of district heating, thermal energy storage and electrical grid storage. The difference between the national and urban heat demands profiles will vary due to the type and occupancy of buildings leading to temporal variations which have not been widely surveyed. This paper develops a high-resolution spatiotemporal heat load model for Great Britain (GB: England, Scotland a Wales) by identifying the appropriate datasets, archetype segmentation and characterisation for the domestic and nondomestic building stock. This is applied to a thermal model and calibrated on the local scale using gas consumption statistics. The annual GB heat demand was in close agreement with other estimates and the peak demand was 219 GW th . The urban heat demand was found to have a lower peak to trough ratio than the average national demand profile. This will have important implications for the uptake of heating technologies and design of district heating.

Suggested Citation

  • Salman Siddiqui & Mark Barrett & John Macadam, 2021. "A High Resolution Spatiotemporal Urban Heat Load Model for GB," Energies, MDPI, vol. 14(14), pages 1-28, July.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:14:p:4078-:d:589438
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/14/4078/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/14/4078/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    2. Love, Jenny & Smith, Andrew Z.P. & Watson, Stephen & Oikonomou, Eleni & Summerfield, Alex & Gleeson, Colin & Biddulph, Phillip & Chiu, Lai Fong & Wingfield, Jez & Martin, Chris & Stone, Andy & Lowe, R, 2017. "The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial," Applied Energy, Elsevier, vol. 204(C), pages 332-342.
    3. Dahl, Magnus & Brun, Adam & Andresen, Gorm B., 2017. "Using ensemble weather predictions in district heating operation and load forecasting," Applied Energy, Elsevier, vol. 193(C), pages 455-465.
    4. Lindberg, K.B. & Bakker, S.J. & Sartori, I., 2019. "Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts," Utilities Policy, Elsevier, vol. 58(C), pages 63-88.
    5. 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.
    6. Watson, S.D. & Lomas, K.J. & Buswell, R.A., 2019. "Decarbonising domestic heating: What is the peak GB demand?," Energy Policy, Elsevier, vol. 126(C), pages 533-544.
    7. Eggimann, Sven & Hall, Jim W. & Eyre, Nick, 2019. "A high-resolution spatio-temporal energy demand simulation to explore the potential of heating demand side management with large-scale heat pump diffusion," Applied Energy, Elsevier, vol. 236(C), pages 997-1010.
    8. Guelpa, Elisa & Marincioni, Ludovica & Capone, Martina & Deputato, Stefania & Verda, Vittorio, 2019. "Thermal load prediction in district heating systems," Energy, Elsevier, vol. 176(C), pages 693-703.
    9. Wang, Zhikun & Crawley, Jenny & Li, Francis G.N. & Lowe, Robert, 2020. "Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK," Energy, Elsevier, vol. 193(C).
    10. Fuentes, E. & Arce, L. & Salom, J., 2018. "A review of domestic hot water consumption profiles for application in systems and buildings energy performance analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1530-1547.
    11. 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.
    12. Bruhns, Harry & Steadman, Philip & Herring, Horace, 2000. "A database for modeling energy use in the non-domestic building stock of England and Wales," Applied Energy, Elsevier, vol. 66(4), pages 277-297, August.
    13. Mikkola, Jani & Lund, Peter D., 2014. "Models for generating place and time dependent urban energy demand profiles," Applied Energy, Elsevier, vol. 130(C), pages 256-264.
    14. Andrei David & Brian Vad Mathiesen & Helge Averfalk & Sven Werner & Henrik Lund, 2017. "Heat Roadmap Europe: Large-Scale Electric Heat Pumps in District Heating Systems," Energies, MDPI, vol. 10(4), pages 1-18, April.
    15. Clegg, Stephen & Mancarella, Pierluigi, 2019. "Integrated electricity-heat-gas modelling and assessment, with applications to the Great Britain system. Part II: Transmission network analysis and low carbon technology and resilience case studies," Energy, Elsevier, vol. 184(C), pages 191-203.
    16. Quiggin, Daniel & Buswell, Richard, 2016. "The implications of heat electrification on national electrical supply-demand balance under published 2050 energy scenarios," Energy, Elsevier, vol. 98(C), pages 253-270.
    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. Simone Ferrari & Federica Zagarella & Paola Caputo & Giuliano Dall’O’, 2021. "A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies," Energies, MDPI, vol. 14(17), pages 1-16, September.

    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. Deakin, Matthew & Bloomfield, Hannah & Greenwood, David & Sheehy, Sarah & Walker, Sara & Taylor, Phil C., 2021. "Impacts of heat decarbonization on system adequacy considering increased meteorological sensitivity," Applied Energy, Elsevier, vol. 298(C).
    2. Vassilis M. Charitopoulos & Mathilde Fajardy & Chi Kong Chyong & David M. Reiner, 2022. "The case of 100% electrification of domestic heat in Great Britain," Working Papers EPRG2206, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    3. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
    4. Ferrari, Simone & Zagarella, Federica & Caputo, Paola & D'Amico, Antonino, 2019. "Results of a literature review on methods for estimating buildings energy demand at district level," Energy, Elsevier, vol. 175(C), pages 1130-1137.
    5. Liu, Guoqiang & Zhou, Xuan & Yan, Junwei & Yan, Gang, 2021. "A temperature and time-sharing dynamic control approach for space heating of buildings in district heating system," Energy, Elsevier, vol. 221(C).
    6. Eggimann, Sven & Usher, Will & Eyre, Nick & Hall, Jim W., 2020. "How weather affects energy demand variability in the transition towards sustainable heating," Energy, Elsevier, vol. 195(C).
    7. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    8. Peacock, Malcolm & Fragaki, Aikaterini & Matuszewski, Bogdan J, 2023. "The impact of heat electrification on the seasonal and interannual electricity demand of Great Britain," Applied Energy, Elsevier, vol. 337(C).
    9. Guelpa, Elisa & Bischi, Aldo & Verda, Vittorio & Chertkov, Michael & Lund, Henrik, 2019. "Towards future infrastructures for sustainable multi-energy systems: A review," Energy, Elsevier, vol. 184(C), pages 2-21.
    10. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    11. Kavian, Soheil & Hakkaki-Fard, Ali & Jafari Mosleh, Hassan, 2020. "Energy performance and economic feasibility of hot spring-based district heating system – A case study," Energy, Elsevier, vol. 211(C).
    12. Ian M. Trotter & Torjus F. Bolkesj{o} & Eirik O. J{aa}stad & Jon Gustav Kirkerud, 2021. "Increased Electrification of Heating and Weather Risk in the Nordic Power System," Papers 2112.02893, arXiv.org.
    13. 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.
    14. Popovski, Eftim & Aydemir, Ali & Fleiter, Tobias & Bellstädt, Daniel & Büchele, Richard & Steinbach, Jan, 2019. "The role and costs of large-scale heat pumps in decarbonising existing district heating networks – A case study for the city of Herten in Germany," Energy, Elsevier, vol. 180(C), pages 918-933.
    15. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    16. 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.
    17. Mengyao Lu & Guitao Xu & Jianjuan Yuan, 2023. "Installation Principle and Calculation Model of the Representative Indoor Temperature-Monitoring Points in Large-Scale Buildings," Energies, MDPI, vol. 16(17), pages 1-19, September.
    18. Dominik Keiner & Larissa D.S.N.S. Barbosa & Dmitrii Bogdanov & Arman Aghahosseini & Ashish Gulagi & Solomon Oyewo & Michael Child & Siavash Khalili & Christian Breyer, 2021. "Global-Local Heat Demand Development for the Energy Transition Time Frame Up to 2050," Energies, MDPI, vol. 14(13), pages 1-51, June.
    19. Hofbauer, Leonhard & McDowall, Will & Pye, Steve, 2022. "Challenges and opportunities for energy system modelling to foster multi-level governance of energy transitions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    20. Ma, Zheng & Knotzer, Armin & Billanes, Joy Dalmacio & Jørgensen, Bo Nørregaard, 2020. "A literature review of energy flexibility in district heating with a survey of the stakeholders’ participation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(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:gam:jeners:v:14:y:2021:i:14:p:4078-:d:589438. 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.