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Models for generating place and time dependent urban energy demand profiles

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  • Mikkola, Jani
  • Lund, Peter D.

Abstract

In this paper, we present a new model for generating spatiotemporal power demand data for urban areas of the form P(x,y,t). The model is flexible and can be adjusted to different cases and local conditions. The dimensions of the model are not restricted, but a typical case would comprise an hour-by-hour simulation over a whole year with a spatial resolution from a few hundred meters up to several kilometers, depending on the area to be covered. These kinds of load profiles are useful when analyzing, e.g., smart grids, demand side management, and renewable energy in the urban context. The model was applied to two cities, Helsinki with detailed input data available, and Shanghai with access to rough data only. In both cases, the generated load patterns appeared logical in terms of empirical observations on how power demand behaves in space and time.

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  • 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.
    • Handle: RePEc:eee:appene:v:130:y:2014:i:c:p:256-264
    DOI: 10.1016/j.apenergy.2014.05.039
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    2. Voulis, Nina & Warnier, Martijn & Brazier, Frances M.T., 2018. "Understanding spatio-temporal electricity demand at different urban scales: A data-driven approach," Applied Energy, Elsevier, vol. 230(C), pages 1157-1171.
    3. Fonseca, Jimeno A. & Schlueter, Arno, 2015. "Integrated model for characterization of spatiotemporal building energy consumption patterns in neighborhoods and city districts," Applied Energy, Elsevier, vol. 142(C), pages 247-265.
    4. Mikkola, Jani & Lund, Peter D., 2016. "Modeling flexibility and optimal use of existing power plants with large-scale variable renewable power schemes," Energy, Elsevier, vol. 112(C), pages 364-375.
    5. 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).
    6. Porse, Erik & Fournier, Eric & Cheng, Dan & Hirashiki, Claire & Gustafson, Hannah & Federico, Felicia & Pincetl, Stephanie, 2020. "Net solar generation potential from urban rooftops in Los Angeles," Energy Policy, Elsevier, vol. 142(C).
    7. 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.
    8. Voulis, Nina & Warnier, Martijn & Brazier, Frances M.T., 2017. "Impact of service sector loads on renewable resource integration," Applied Energy, Elsevier, vol. 205(C), pages 1311-1326.
    9. Job Taminiau & John Byrne & Jongkyu Kim & Min‐whi Kim & Jeongseok Seo, 2021. "Infrastructure‐scale sustainable energy planning in the cityscape: Transforming urban energy metabolism in East Asia," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    10. 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.
    11. 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.
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    14. Chen, Han & Chen, Wenying, 2019. "Potential impact of shifting coal to gas and electricity for building sectors in 28 major northern cities of China," Applied Energy, Elsevier, vol. 236(C), pages 1049-1061.

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