IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v322y2025ics0360544225012022.html
   My bibliography  Save this article

Two-stage robust optimization of a virtual power plant considering a refined demand response

Author

Listed:
  • Liu, Jinpeng
  • Peng, Jinchun
  • Liu, Hushihan
  • Deng, Jiaming
  • Song, Xiaohua

Abstract

A reasonable demand response strategy and flexible resource planning technology are the key methods for constructing new power systems. Based on this, a virtual power plant economic optimization model considering a refined demand response strategy is proposed. First, considering the regulatory characteristics of flexible loads and the satisfaction degree of residents, flexible loads are finely classified, and a demand response model is constructed. Second, considering the power uncertainty of wind turbines and photovoltaics in virtual power plants, a two-stage robust optimization model with a min–max–min structure is constructed; then, a transformation method of fuzzy sets and subproblems is proposed to improve the solution efficiency. Finally, the total operating cost of virtual power plants under the deviation of power forecasts and fluctuations in intraday electricity prices is analysed. The simulation results reveal that refined load classification can reduce the system day-ahead operating cost by 7.12 %; the proposed two-stage robust optimization model reduces the total real time operating cost by 0.81–6.39 % compared to the deterministic optimization model.

Suggested Citation

  • Liu, Jinpeng & Peng, Jinchun & Liu, Hushihan & Deng, Jiaming & Song, Xiaohua, 2025. "Two-stage robust optimization of a virtual power plant considering a refined demand response," Energy, Elsevier, vol. 322(C).
  • Handle: RePEc:eee:energy:v:322:y:2025:i:c:s0360544225012022
    DOI: 10.1016/j.energy.2025.135560
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225012022
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2025.135560?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:322:y:2025:i:c:s0360544225012022. 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.