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Modelling changes to electricity demand load duration curves as a consequence of predicted climate change for Australia

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  • Thatcher, Marcus J.

Abstract

In this paper, we describe a method for constructing regional electricity demand data sets at 30min intervals, which are consistent with climate change scenarios. Specifically, we modify a commonly used linear regression model between regional electricity demand and climate to also describe intraday variability in demand so that regional load duration curves (LDCs) can be predicted. The model is evaluated for four different Australian states that are participants in the Australian National Electricity Market (NEM) and the resultant data sets are found to reproduce each state's LDCs with reasonable accuracy. We also apply the demand model to CSIRO's Mk 3 global climate model data sets that have been downscaled to 60km resolution using CSIRO's conformal-cubic atmospheric model to estimate how LDCs change as a consequence of a 1°C increase in the average temperature of Australian state capital cities. These regional electricity demand data sets are then useful for economic modelling of electricity markets such as the NEM.

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  • Thatcher, Marcus J., 2007. "Modelling changes to electricity demand load duration curves as a consequence of predicted climate change for Australia," Energy, Elsevier, vol. 32(9), pages 1647-1659.
    • Handle: RePEc:eee:energy:v:32:y:2007:i:9:p:1647-1659
    DOI: 10.1016/j.energy.2006.12.005
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    Cited by:

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    4. Foster, John & Bell, William Paul & Wild, Phillip & Sharma, Deepak & Sandu, Suwin & Froome, Craig & Wagner, Liam & Misra, Suchi & Bagia, Ravindra, 2013. "Analysis of institutional adaptability to redress electricity infrastructure vulnerability due to climate change," MPRA Paper 47787, University Library of Munich, Germany.
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    11. Karodine Chreng & Han Soo Lee & Soklin Tuy, 2022. "A Hybrid Model for Electricity Demand Forecast Using Improved Ensemble Empirical Mode Decomposition and Recurrent Neural Networks with ERA5 Climate Variables," Energies, MDPI, vol. 15(19), pages 1-26, October.
    12. Hekkenberg, M. & Moll, H.C. & Uiterkamp, A.J.M. Schoot, 2009. "Dynamic temperature dependence patterns in future energy demand models in the context of climate change," Energy, Elsevier, vol. 34(11), pages 1797-1806.
    13. Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    14. Huva, Robert & Dargaville, Roger & Caine, Simon, 2012. "Prototype large-scale renewable energy system optimisation for Victoria, Australia," Energy, Elsevier, vol. 41(1), pages 326-334.
    15. Ahmed, T. & Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2018. "Load forecasting under changing climatic conditions for the city of Sydney, Australia," Energy, Elsevier, vol. 142(C), pages 911-919.
    16. Bell, William, 2012. "The impact of climate change on electricity demand in the Australian national electricity market," MPRA Paper 38110, University Library of Munich, Germany, revised 29 Feb 2012.
    17. Wang, Chi-hsiang & Grozev, George & Seo, Seongwon, 2012. "Decomposition and statistical analysis for regional electricity demand forecasting," Energy, Elsevier, vol. 41(1), pages 313-325.
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