IDEAS home Printed from https://ideas.repec.org/p/prc/mpaper/ks--2023-mp01.html
   My bibliography  Save this paper

Modeling and Projecting Regional Electricity Demand for Saudi Arabia

Author

Listed:
  • Jeyhun Mikayilov
  • Abdulelah Darandary

    (King Abdullah Petroleum Studies and Research Center)

Abstract

This paper utilizes a structural time series approach to model Saudi Arabia’s regional electricity demand, capturing undetected forces of variability in the data-generating process that include improvements in technology, energy-saving behavior, and other underlying trends that are excluded under conventional estimation methods. National models of aggregate electricity consumption might not be representative, as electricity prices are administered regionally and Saudi Arabia’s regions have unique social and economic characteristics. We find evidence that the regions have unique responses to prices and income levels with regard to electricity demand. Additionally, we use our estimated model to project the regional baseline demand for electricity for Saudi Arabia and create a scenario to demonstrate how a price increase would impact these regions differently. This information is valuable for policymakers in Saudi Arabia, as the fuel mix to generate electricity differs between regions. Our baseline electricity demand projections indicate that under the assumptions of moderate economic growth and no price changes, total electricity demand in Saudi Arabia will reach 366 TWh by 2030.

Suggested Citation

  • Jeyhun Mikayilov & Abdulelah Darandary, 2023. "Modeling and Projecting Regional Electricity Demand for Saudi Arabia," Methodology Papers ks--2023-mp01, King Abdullah Petroleum Studies and Research Center.
  • Handle: RePEc:prc:mpaper:ks--2023-mp01
    DOI: 10.30573/KS--2023-MP01
    as

    Download full text from publisher

    File URL: https://www.kapsarc.org/research/publications/distribution-hosting-capacity-tool/
    File Function: First version, 2023
    Download Restriction: no

    File URL: https://libkey.io/10.30573/KS--2023-MP01?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
    ---><---

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:prc:mpaper:ks--2023-mp01. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Michael Gaffney (email available below). General contact details of provider: https://edirc.repec.org/data/kapsasa.html .

    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.