IDEAS home Printed from https://ideas.repec.org/a/eee/enepol/v53y2013icp90-96.html
   My bibliography  Save this article

Extreme daily increases in peak electricity demand: Tail-quantile estimation

Author

Listed:
  • Sigauke, Caston
  • Verster, Andréhette
  • Chikobvu, Delson

Abstract

A Generalized Pareto Distribution (GPD) is used to model extreme daily increases in peak electricity demand. The model is fitted to years 2000–2011 recorded data for South Africa to make a comparative analysis with the Generalized Pareto-type (GP-type) distribution. Peak electricity demand is influenced by the tails of probability distributions as well as by means or averages. At times there is a need to depart from the average thinking and exploit information provided by the extremes (tails). Empirical results show that both the GP-type and the GPD are a good fit to the data. One of the main advantages of the GP-type is the estimation of only one parameter. Modelling of extreme daily increases in peak electricity demand helps in quantifying the amount of electricity which can be shifted from the grid to off peak periods. One of the policy implications derived from this study is the need for day-time use of electricity billing system similar to the one used in the cellular telephone/and fixed line-billing technology. This will result in the shifting of electricity demand on the grid to off peak time slots as users try to avoid high peak hour charges.

Suggested Citation

  • Sigauke, Caston & Verster, Andréhette & Chikobvu, Delson, 2013. "Extreme daily increases in peak electricity demand: Tail-quantile estimation," Energy Policy, Elsevier, vol. 53(C), pages 90-96.
    • Handle: RePEc:eee:enepol:v:53:y:2013:i:c:p:90-96
    DOI: 10.1016/j.enpol.2012.10.073
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0301421512009585
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.enpol.2012.10.073?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. Newsham, Guy R. & Birt, Benjamin J. & Rowlands, Ian H., 2011. "A comparison of four methods to evaluate the effect of a utility residential air-conditioner load control program on peak electricity use," Energy Policy, Elsevier, vol. 39(10), pages 6376-6389, October.
    2. Newsham, Guy R. & Bowker, Brent G., 2010. "The effect of utility time-varying pricing and load control strategies on residential summer peak electricity use: A review," Energy Policy, Elsevier, vol. 38(7), pages 3289-3296, July.
    3. Dlamini, Ndumiso G. & Cromieres, Fabien, 2012. "Implementing peak load reduction algorithms for household electrical appliances," Energy Policy, Elsevier, vol. 44(C), pages 280-290.
    4. MacDonald, A. & Scarrott, C.J. & Lee, D. & Darlow, B. & Reale, M. & Russell, G., 2011. "A flexible extreme value mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2137-2157, June.
    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. Katleho Makatjane, 2022. "Forecasting Uncertainty Intervals for Return Period of Extreme Daily Electricity Consumption," International Journal of Energy Economics and Policy, Econjournals, vol. 12(4), pages 217-225, July.
    2. Sigauke, Caston & Bere, Alphonce, 2017. "Modelling non-stationary time series using a peaks over threshold distribution with time varying covariates and threshold: An application to peak electricity demand," Energy, Elsevier, vol. 119(C), pages 152-166.
    3. Norman Maswanganyi & Caston Sigauke & Edmore Ranganai, 2021. "Prediction of Extreme Conditional Quantiles of Electricity Demand: An Application Using South African Data," Energies, MDPI, vol. 14(20), pages 1-21, October.
    4. Miller, J. Isaac & Nam, Kyungsik, 2022. "Modeling peak electricity demand: A semiparametric approach using weather-driven cross-temperature response functions," Energy Economics, Elsevier, vol. 114(C).
    5. Chabouni, Naima & Belarbi, Yacine & Benhassine, Wassim, 2020. "Electricity load dynamics, temperature and seasonality Nexus in Algeria," Energy, Elsevier, vol. 200(C).
    6. Nijhuis, M. & Gibescu, M. & Cobben, J.F.G., 2017. "Analysis of reflectivity & predictability of electricity network tariff structures for household consumers," Energy Policy, Elsevier, vol. 109(C), pages 631-641.
    7. Stephen Chan & Saralees Nadarajah, 2015. "Extreme value analysis of electricity demand in the UK," Applied Economics Letters, Taylor & Francis Journals, vol. 22(15), pages 1246-1251, October.

    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. Lang, Corey & Okwelum, Edson, 2015. "The mitigating effect of strategic behavior on the net benefits of a direct load control program," Energy Economics, Elsevier, vol. 49(C), pages 141-148.
    2. Zheng, Menglian & Meinrenken, Christoph J. & Lackner, Klaus S., 2014. "Agent-based model for electricity consumption and storage to evaluate economic viability of tariff arbitrage for residential sector demand response," Applied Energy, Elsevier, vol. 126(C), pages 297-306.
    3. Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
    4. Francesco Liberati & Alessandro Di Giorgio, 2017. "Economic Model Predictive and Feedback Control of a Smart Grid Prosumer Node," Energies, MDPI, vol. 11(1), pages 1-23, December.
    5. Öhrlund, Isak & Schultzberg, Mårten & Bartusch, Cajsa, 2019. "Identifying and estimating the effects of a mandatory billing demand charge," Applied Energy, Elsevier, vol. 237(C), pages 885-895.
    6. Klaassen, E.A.M. & Kobus, C.B.A. & Frunt, J. & Slootweg, J.G., 2016. "Responsiveness of residential electricity demand to dynamic tariffs: Experiences from a large field test in the Netherlands," Applied Energy, Elsevier, vol. 183(C), pages 1065-1074.
    7. Fátima Brilhante, M. & Ivette Gomes, M. & Pestana, Dinis, 2013. "A simple generalisation of the Hill estimator," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 518-535.
    8. Eid, Cherrelle & Codani, Paul & Perez, Yannick & Reneses, Javier & Hakvoort, Rudi, 2016. "Managing electric flexibility from Distributed Energy Resources: A review of incentives for market design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 237-247.
    9. Nicole D. Sintov & P. Wesley Schultz, 2017. "Adjustable Green Defaults Can Help Make Smart Homes More Sustainable," Sustainability, MDPI, vol. 9(4), pages 1-12, April.
    10. Zhang, Qi & Mclellan, Benjamin C. & Tezuka, Tetsuo & Ishihara, Keiichi N., 2013. "A methodology for economic and environmental analysis of electric vehicles with different operational conditions," Energy, Elsevier, vol. 61(C), pages 118-127.
    11. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    12. Xu, Xiaojing & Chen, Chien-fei & Zhu, Xiaojuan & Hu, Qinran, 2018. "Promoting acceptance of direct load control programs in the United States: Financial incentive versus control option," Energy, Elsevier, vol. 147(C), pages 1278-1287.
    13. Dadalau Diana, 2012. "Integrated Estimation Model Of The Difficulty Status Of Entreprise," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 130-143, December.
    14. Steele Santos, Paulo E. & Coradi Leme, Rafael & Galvão, Leandro, 2012. "On the electrical two-part tariff—The Brazilian perspective," Energy Policy, Elsevier, vol. 40(C), pages 123-130.
    15. Miller, Reid & Golab, Lukasz & Rosenberg, Catherine, 2017. "Modelling weather effects for impact analysis of residential time-of-use electricity pricing," Energy Policy, Elsevier, vol. 105(C), pages 534-546.
    16. Cédric Clastres & Olivier Rebenaque & Patrick Jochem, 2020. "Provision of Demand Response from the prosumers in multiple markets," Working Papers 2008, Chaire Economie du climat.
    17. Olga Bogdanova & Karīna Viskuba & Laila Zemīte, 2023. "A Review of Barriers and Enables in Demand Response Performance Chain," Energies, MDPI, vol. 16(18), pages 1-33, September.
    18. Yunusov, Timur & Torriti, Jacopo, 2021. "Distributional effects of Time of Use tariffs based on electricity demand and time use," Energy Policy, Elsevier, vol. 156(C).
    19. Ren, Zhengen & Grozev, George & Higgins, Andrew, 2016. "Modelling impact of PV battery systems on energy consumption and bill savings of Australian houses under alternative tariff structures," Renewable Energy, Elsevier, vol. 89(C), pages 317-330.
    20. Ren'e Aid & Dylan Possamai & Nizar Touzi, 2018. "Optimal electricity demand response contracting with responsiveness incentives," Papers 1810.09063, arXiv.org, revised May 2019.

    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:53:y:2013:i:c:p:90-96. 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.