IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i8p1917-d1377450.html
   My bibliography  Save this article

Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production

Author

Listed:
  • Anna Eingartner

    (Fraunhofer IOSB-AST: Cognitive Energy Systems, 98693 Ilmenau, Germany)

  • Steffi Naumann

    (Fraunhofer IOSB-AST: Cognitive Energy Systems, 98693 Ilmenau, Germany)

  • Philipp Schmitz

    (Optimization-Based Control Group, Institute of Mathematics, Technische Universität Ilmenau, 98693 Ilmenau, Germany)

  • Karl Worthmann

    (Optimization-Based Control Group, Institute of Mathematics, Technische Universität Ilmenau, 98693 Ilmenau, Germany)

Abstract

In this paper, the application of the method of affinely adjustable robust optimization to a planning model of an energy system under uncertain parameters is presented, and the total scheduling costs in comparison with the deterministic model are evaluated. First, the basics of optimization under uncertain data are recapped, and it is described how these methods can be used in different applications for energy systems. This is followed by the methodology of adjustable robust optimization by defining the affinely adjustable robust counterpart. Finally, a numerical case study is conducted to compare the adjustable robust method with a rolling deterministic scheduling method. Both are implemented on a model of an energy system and compared with each other by simulation using real-world data. By calculating the total operating costs for both methods, it can be concluded that the adjustable robust optimization provides a significantly more cost-effective solution to the scheduling problem.

Suggested Citation

  • Anna Eingartner & Steffi Naumann & Philipp Schmitz & Karl Worthmann, 2024. "Adjustable Robust Energy Operation Planning under Uncertain Renewable Energy Production," Energies, MDPI, vol. 17(8), pages 1-14, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:8:p:1917-:d:1377450
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/8/1917/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/8/1917/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhibin Liu & Feng Guo & Jiaqi Liu & Xinyan Lin & Ao Li & Zhaoyan Zhang & Zhiheng Liu, 2023. "A Compound Coordinated Optimal Operation Strategy of Day-Ahead-Rolling-Realtime in Integrated Energy System," Energies, MDPI, vol. 16(1), pages 1-19, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.

      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:gam:jeners:v:17:y:2024:i:8:p:1917-:d:1377450. 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.

      If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

      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.