IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v147y2020ics0301421520304584.html
   My bibliography  Save this article

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

Author

Listed:
  • 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.

Suggested Citation

  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421520304584
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2020.111732?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    5. 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).
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Esparza, Ángel E. & Rowan, Gillian & Newhook, Ashley & Deglint, Hanford J. & Garrison, Billy & Orth-Lashley, Bryn & Girard, Marianne & Shaw, Warren, 2023. "Analysis of a tiered top-down approach using satellite and aircraft platforms to monitor oil and gas facilities in the Permian basin," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
    3. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
    4. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
    5. Apadula, Francesco & Bassini, Alessandra & Elli, Alberto & Scapin, Simone, 2012. "Relationships between meteorological variables and monthly electricity demand," Applied Energy, Elsevier, vol. 98(C), pages 346-356.
    6. Khosrowpour, Ardalan & Jain, Rishee K. & Taylor, John E. & Peschiera, Gabriel & Chen, Jiayu & Gulbinas, Rimas, 2018. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation," Applied Energy, Elsevier, vol. 218(C), pages 304-316.
    7. Herrerias, M.J., 2013. "Seasonal anomalies in electricity intensity across Chinese regions," Applied Energy, Elsevier, vol. 112(C), pages 1548-1557.
    8. Fransson, Victor & Bagge, Hans & Johansson, Dennis, 2019. "Impact of variations in residential use of household electricity on the energy and power demand for space heating – Variations from measurements in 1000 apartments," Applied Energy, Elsevier, vol. 254(C).
    9. Rinaldi, Arthur & Yilmaz, Selin & Patel, Martin K. & Parra, David, 2022. "What adds more flexibility? An energy system analysis of storage, demand-side response, heating electrification, and distribution reinforcement," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. Ang, B.W. & Wang, H. & Ma, Xiaojing, 2017. "Climatic influence on electricity consumption: The case of Singapore and Hong Kong," Energy, Elsevier, vol. 127(C), pages 534-543.
    11. Zhou, Kaile & Yang, Shanlin, 2016. "Understanding household energy consumption behavior: The contribution of energy big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 810-819.
    12. Ünal, Berat Berkan & Onaygil, Sermin & Acuner, Ebru & Cin, Rabia, 2022. "Application of energy efficiency obligation scheme for electricity distribution companies in Turkey," Energy Policy, Elsevier, vol. 163(C).
    13. Melissa Dell & Benjamin F. Jones & Benjamin A. Olken, 2014. "What Do We Learn from the Weather? The New Climate-Economy Literature," Journal of Economic Literature, American Economic Association, vol. 52(3), pages 740-798, September.
    14. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    15. Haidar, Ahmed M.A. & Muttaqi, Kashem & Sutanto, Danny, 2015. "Smart Grid and its future perspectives in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1375-1389.
    16. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    17. Lan, Haifeng & Gou, Zhonghua & Yang, Linchuan, 2020. "House price premium associated with residential solar photovoltaics and the effect from feed-in tariffs: A case study of Southport in Queensland, Australia," Renewable Energy, Elsevier, vol. 161(C), pages 907-916.
    18. Nilsson, Andreas & Bergstad, Cecilia Jakobsson & Thuvander, Liane & Andersson, David & Andersson, Kristin & Meiling, Pär, 2014. "Effects of continuous feedback on households’ electricity consumption: Potentials and barriers," Applied Energy, Elsevier, vol. 122(C), pages 17-23.
    19. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
    20. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:enepol:v:147:y:2020:i:c:s0301421520304584. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/enpol .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.