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Long-term projections of the hourly electricity consumption in Danish municipalities

Author

Listed:
  • Andersen, F.M.
  • Henningsen, G.
  • Møller, N.F.
  • Larsen, H.V.

Abstract

To assess future challenges for the energy system, the transmission - and distribution grids, long-term projections of the spatial distribution of electricity consumption with an hourly resolution is important. Based on 2015 data from hourly meters in Denmark, we develop a model that converts national projections of the annual electricity consumption to projections of the hourly consumption in Danish municipalities. Due to different weights and consumption profiles for categories of customers, both the level and the hourly consumption profile differ between municipalities; also future changes will differ.

Suggested Citation

  • Andersen, F.M. & Henningsen, G. & Møller, N.F. & Larsen, H.V., 2019. "Long-term projections of the hourly electricity consumption in Danish municipalities," Energy, Elsevier, vol. 186(C).
    • Handle: RePEc:eee:energy:v:186:y:2019:i:c:s0360544219315622
    DOI: 10.1016/j.energy.2019.115890
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    Cited by:

    1. Dong, Chaoyu & Sun, Jianwen & Li, Yanran & Zheng, Yan & Hao, Jianye & Liu, Yang & Jia, Hongjie, 2022. "Hybrid process model and smart policy network of electric-vehicle resources for instantaneous power flow imbalances," Applied Energy, Elsevier, vol. 314(C).
    2. White, Philip R. & Rhodes, Joshua D. & Wilson, Eric J.H. & Webber, Michael E., 2021. "Quantifying the impact of residential space heating electrification on the Texas electric grid," Applied Energy, Elsevier, vol. 298(C).
    3. Helena Martín & Jordi de la Hoz & Arnau Aliana & Sergio Coronas & José Matas, 2021. "Analysis of the Net Metering Schemes for PV Self-Consumption in Denmark," Energies, MDPI, vol. 14(7), pages 1-22, April.
    4. Kang, J. & Reiner, D., 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Cambridge Working Papers in Economics 2143, Faculty of Economics, University of Cambridge.
    5. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
    6. Jose Juan Caceres-Hernandez & Gloria Martin-Rodriguez & Jonay Hernandez-Martin, 2022. "A proposal for measuring and comparing seasonal variations in hourly economic time series," Empirical Economics, Springer, vol. 62(4), pages 1995-2021, April.
    7. Neirotti, Francesco & Noussan, Michel & Simonetti, Marco, 2020. "Towards the electrification of buildings heating - Real heat pumps electricity mixes based on high resolution operational profiles," Energy, Elsevier, vol. 195(C).
    8. Valkering, Pieter & Moglianesi, Andrea & Godon, Louis & Duerinck, Jan & Huber, Dominik & Costa, Daniele, 2023. "Representing decentralized generation and local energy use flexibility in an energy system optimization model," Applied Energy, Elsevier, vol. 348(C).

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