IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v315y2025ics0360544225001124.html
   My bibliography  Save this article

A scheduling optimization model for a gas-electricity interconnected virtual power plant considering green certificates-carbon joint trading and source-load uncertainties

Author

Listed:
  • Zhang, Jinliang
  • Liu, Ziyi
  • Liu, Yishuo

Abstract

Virtual power plant (VPP) play a positive role in enhancing the flexibility of new power systems and guaranteeing the safe and stable supply of electricity. In order to enhance the sustainability of gas-electricity interconnected virtual power plant (GVPP) operation and reduce the impact of source-load uncertainty on GVPP scheduling, a research scheme based on interval prediction of source-load data, analysis of trading mechanism, scheduling model construction and solution, and multi-scenario comparison is proposed. First, an improved hybrid interval prediction model is proposed to construct the prediction interval of source-load and portray the uncertainty through the upper and lower bounds of the interval. Second, the coupling principle between green certificate trading and carbon emission trading and the feasibility of GVPP participation in trading are analyzed. Again, the net profit and carbon emission are taken as two optimization objectives to establish a multi-objective optimal scheduling model of GVPP considering the emission reduction effect of green certificate and the source-load uncertainty. Finally, based on the considerations of the model, different scenarios are set up and the comparison of the scheduling results of each scenario is realized. The simulation results show that under the uncertainty condition, the optimal net profit of GVPP is 464,657.39¥, and the carbon emissions offset by green certificates are 246.43t. The proposed model can enhance its willingness to take the initiative to control emissions on the basis of safeguarding the benefits of the system, which improves the plannability of the scheduling scheme.

Suggested Citation

  • Zhang, Jinliang & Liu, Ziyi & Liu, Yishuo, 2025. "A scheduling optimization model for a gas-electricity interconnected virtual power plant considering green certificates-carbon joint trading and source-load uncertainties," Energy, Elsevier, vol. 315(C).
    • Handle: RePEc:eee:energy:v:315:y:2025:i:c:s0360544225001124
    DOI: 10.1016/j.energy.2025.134470
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225001124
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.134470?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:energy:v:315:y:2025:i:c:s0360544225001124. 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/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.