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Constructing Australian Residential Electricity Load Profile for Supporting Future Network Studies

Author

Listed:
  • Umme Mumtahina

    (School of Engineering and Technology, Central Queensland University, Rockhampton 4701, Australia)

  • Sanath Alahakoon

    (School of Engineering and Technology, Central Queensland University, Rockhampton 4701, Australia)

  • Peter Wolfs

    (School of Engineering and Technology, Central Queensland University, Rockhampton 4701, Australia)

  • Jiannan Liu

    (EleXsys Ptd Ltd., Brisbane 4072, Australia)

Abstract

This paper examines how Australian residential load profiles may evolve in the short to medium term future. These profiles can be used to support simulation studies of the future Australian network within an environment that is transitioning to renewable energy and broader use of electricity as a tool for decarbonisation. The daily profiles rely heavily on the Australian Energy Market Operator (AEMO) forecasts for future annual energy usage. The period from 2024 to 2050 will be transformational. In the residential networks, two secular trends are particularly important in expanding residential generation and electrification. New daily load profiles have been constructed using historical Australian profiles and adding additional components for solar generation, battery operation and electrification activities. The entire aggregated residential network is expected to have reverse midday power flow on any average day from 2024 onwards due to the rapid increase in electric vehicle (EV) usage. The domestic energy demand forecasting methodology presented in this work related to Australia can easily be adopted to carry out similar forecasting for any country of the world.

Suggested Citation

  • Umme Mumtahina & Sanath Alahakoon & Peter Wolfs & Jiannan Liu, 2024. "Constructing Australian Residential Electricity Load Profile for Supporting Future Network Studies," Energies, MDPI, vol. 17(12), pages 1-14, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:12:p:2908-:d:1414006
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    References listed on IDEAS

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    1. McKenna, R. & Djapic, P. & Weinand, J. & Fichtner, W. & Strbac, G., 2018. "Assessing the implications of socioeconomic diversity for low carbon technology uptake in electrical distribution networks," Applied Energy, Elsevier, vol. 210(C), pages 856-869.
    2. Giasemidis, Georgios & Haben, Stephen & Lee, Tamsin & Singleton, Colin & Grindrod, Peter, 2017. "A genetic algorithm approach for modelling low voltage network demands," Applied Energy, Elsevier, vol. 203(C), pages 463-473.
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