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Net electricity load profiles: Shape and variability considering customer-mix at transformers on the island of Oahu, Hawai'i

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  • Anukoolthamchote, Pam Chasuta
  • Assané, Djeto
  • Konan, Denise Eby

Abstract

This paper uses data provided by Hawaiian Electric Company (HECO) for the period from September 2010 to May 2014. The study explores the effect of customer mix of each distributed transformer on the shape of load profiles along with their variability. Results suggest that in a more residential-concentrated area, net load generally has two peaks — morning and night, while a more commercial-or industrial-concentrated area exhibits one midday peak. The shape of a given areas’ load profile is mostly influenced by its customer-mix and the time-of-day, while its load volatility is largely the result of weather patterns and the level of PV penetration. Since solar power typically exhibits different generation characteristics from power produced by other conventional sources, more precise solar forecasts enable electric system operators to better manage electricity generation with fluctuating solar output.

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  • Anukoolthamchote, Pam Chasuta & Assané, Djeto & Konan, Denise Eby, 2020. "Net electricity load profiles: Shape and variability considering customer-mix at transformers on the island of Oahu, Hawai'i," Energy Policy, Elsevier, vol. 147(C).
  • Handle: RePEc:eee:enepol:v:147:y:2020:i:c:s0301421520304584
    DOI: 10.1016/j.enpol.2020.111732
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    1. Wee, Sherilyn, 2016. "The effect of residential solar photovoltaic systems on home value: A case study of Hawai‘i," Renewable Energy, Elsevier, vol. 91(C), pages 282-292.
    2. Zhou, Kai-le & Yang, Shan-lin & Shen, Chao, 2013. "A review of electric load classification in smart grid environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 103-110.
    3. Asadoorian, Malcolm O. & Eckaus, Richard S. & Schlosser, C. Adam, 2008. "Modeling climate feedbacks to electricity demand: The case of China," Energy Economics, Elsevier, vol. 30(4), pages 1577-1602, July.
    4. Moral-Carcedo, Julián & Pérez-García, Julián, 2015. "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, Elsevier, vol. 142(C), pages 407-425.
    5. Assané, Djeto & Konan, Denise Eby & Anukoolthamchote, Pam Chasuta, 2019. "Assessing variability of photovoltaic load supply in Hawai‘i," Energy Policy, Elsevier, vol. 132(C), pages 290-298.
    6. Lam, Joseph C. & Tang, H.L. & Li, Danny H.W., 2008. "Seasonal variations in residential and commercial sector electricity consumption in Hong Kong," Energy, Elsevier, vol. 33(3), pages 513-523.
    7. Yilmaz, S. & Rinaldi, A. & Patel, M.K., 2020. "DSM interactions: What is the impact of appliance energy efficiency measures on the demand response (peak load management)?," Energy Policy, Elsevier, vol. 139(C).
    8. Vassileva, Iana & Wallin, Fredrik & Dahlquist, Erik, 2012. "Analytical comparison between electricity consumption and behavioral characteristics of Swedish households in rented apartments," Applied Energy, Elsevier, vol. 90(1), pages 182-188.
    9. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    10. Yilmaz, S. & Weber, S. & Patel, M.K., 2019. "Who is sensitive to DSM? Understanding the determinants of the shape of electricity load curves and demand shifting: Socio-demographic characteristics, appliance use and attitudes," Energy Policy, Elsevier, vol. 133(C).
    11. Tung, Ching-Pin & Tseng, Tze-Chi & Huang, An-Lei & Liu, Tzu-Ming & Hu, Ming-Che, 2013. "Impact of climate change on Taiwanese power market determined using linear complementarity model," Applied Energy, Elsevier, vol. 102(C), pages 432-439.
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    2. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).

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    More about this item

    Keywords

    Net electricity load; Customer mix; Transformer; Shape of load profile; Load volatility; And PV penetration;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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