IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v329y2025ics0360544225021176.html
   My bibliography  Save this article

Closed-loop integrated prediction-and-dispatching framework for unit commitment in power system with renewables and hydrogen energy

Author

Listed:
  • Zhang, Yi
  • Zhang, Xiaoming
  • Jia, Kejin

Abstract

Wind and photovoltaic power coupling hydrogen is an effective way to improve the accommodation of renewable energy. As an important task of power system operation, unit commitment needs to consider not only randomness of renewables, but also the operation characteristics of electrolyzer and fuel cell. The traditional model for unit commitment problem is based on predict-then-dispatch implemented in a queue. The relevant dispatching information (i.e. cost, objective, constraint) is not fed back to the accuracy-oriented prediction model. Models with similar prediction accuracy can achieve different economic performance. Collaborative framework for integrating prediction and dispatching is proposed in this paper, which forms a closed loop structure to reduce the system cost. Economics evaluation model contains the income from hydrogen sale and operation cost of hydrogen subsystem. In addition to conventional constraints, dispatching optimization model includes not only network constraint of power flow security, but also the operational exclusivity of electrolyzer and fuel cell. MILP solver for optimal dispatching cannot efficiently deal with the computational burden, which is caused by the rapid increase of decision variables and constraints due to many historical scenarios. Therefore, data-driven machine learning method is introduced into the optimization problem. The cost-oriented prediction model is trained by the presented adaptive neuro-fuzzy interference system. How to select statistical and temporal characteristics of time series as model inputs has an impact on the performance of proposed integration framework. Furthermore, this paper discusses in detail the relationship between the model structure and system cost. Case study on the modified IEEE 24-bus power system demonstrates that the integrated prediction-and-dispatching framework can availably improve economics compared with traditional model.

Suggested Citation

  • Zhang, Yi & Zhang, Xiaoming & Jia, Kejin, 2025. "Closed-loop integrated prediction-and-dispatching framework for unit commitment in power system with renewables and hydrogen energy," Energy, Elsevier, vol. 329(C).
    • Handle: RePEc:eee:energy:v:329:y:2025:i:c:s0360544225021176
    DOI: 10.1016/j.energy.2025.136475
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225021176
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.136475?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:329:y:2025:i:c:s0360544225021176. 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.