IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v239y2025ics096014812402113x.html
   My bibliography  Save this article

Bayesian Belief Networks: Redefining wholesale electricity price modelling in high penetration non-firm renewable generation power systems

Author

Listed:
  • Maticka, Martin J.
  • Mahmoud, Thair S.

Abstract

The transition of electricity generation from firm to non-firm renewable generation is driving changes in wholesale electricity price dynamics that are increasingly challenging to model due to the stochastic nature of wind and solar as the primary energy source. Robust pricing models are essential for optimising financial performance in liberalised electricity markets. The novelty of this paper is the application of Bayesian Belief Networks in the modelling of wholesale electricity price formation, specifically in power systems with a high penetration of non-firm renewable generation. This paper links the mathematical Bayesian representation to established statistical and computational approaches using a functional supply-side wholesale electricity market pricing model. In addition, the paper introduces a novel validation method employing volatility analysis to assess the case study's performance.

Suggested Citation

  • Maticka, Martin J. & Mahmoud, Thair S., 2025. "Bayesian Belief Networks: Redefining wholesale electricity price modelling in high penetration non-firm renewable generation power systems," Renewable Energy, Elsevier, vol. 239(C).
    • Handle: RePEc:eee:renene:v:239:y:2025:i:c:s096014812402113x
    DOI: 10.1016/j.renene.2024.122045
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096014812402113X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2024.122045?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.

    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:renene:v:239:y:2025:i:c:s096014812402113x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/renewable-energy .

    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.