IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i8p1279-d792108.html
   My bibliography  Save this article

On Optimal Settings for a Family of Runge–Kutta-Based Power-Flow Solvers Suitable for Large-Scale Ill-Conditioned Cases

Author

Listed:
  • Marcos Tostado-Véliz

    (Department of Electrical Engineering, University of Jaén, 23700 Jaén, Spain)

  • Talal Alharbi

    (Department of Electrical Engineering, College of Engineering, Qassim University, Buraydah 52571, Saudi Arabia)

  • Hisham Alharbi

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21974, Saudi Arabia)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Francisco Jurado

    (Department of Electrical Engineering, University of Jaén, 23700 Jaén, Spain)

Abstract

Growing demand, interconnection of multiple systems, and difficulty in upgrading existing infrastructures are limiting the capabilities of conventional computational tools employed in power system analysis. Recent studies manifest the importance of efficiently solving well- and ill-conditioned Power-Flow cases in a modern power-system paradigm. While the well-conditioned cases are easily solvable using standard methods, the ill-conditioned ones suppose a challenge for such solvers. In this regard, methods based on the Continuous Newton’s principle have demonstrated their ability to address ill-conditioned cases with acceptable efficiency. This paper demonstrates that the approaches proposed so far do not extract the best numerical properties of such solvers. To fill this gap, an optimization framework is proposed by which the parameters involved in the two-stage Runge–Kutta-based solvers are appropriately set, so that the stability and convergence order of the numerical mapping are maximized. By using the developed optimization technique, three solvers with quadratic, cubic, and 4th order of convergence are developed. The new proposals are tested on a variety of large-scale ill-conditioned cases. Results obtained were promising, outperforming other conventional and robust approaches.

Suggested Citation

  • Marcos Tostado-Véliz & Talal Alharbi & Hisham Alharbi & Salah Kamel & Francisco Jurado, 2022. "On Optimal Settings for a Family of Runge–Kutta-Based Power-Flow Solvers Suitable for Large-Scale Ill-Conditioned Cases," Mathematics, MDPI, vol. 10(8), pages 1-19, April.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:8:p:1279-:d:792108
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/8/1279/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/10/8/1279/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Talal Alharbi & Marcos Tostado-Véliz & Omar Alrumayh & Francisco Jurado, 2021. "On Various High-Order Newton-Like Power Flow Methods for Well and Ill-Conditioned Cases," Mathematics, MDPI, vol. 9(17), pages 1-17, August.
    2. Marcos Tostado-Véliz & Salah Kamel & Francisco Jurado & Francisco J. Ruiz-Rodriguez, 2021. "On the Applicability of Two Families of Cubic Techniques for Power Flow Analysis," Energies, MDPI, vol. 14(14), pages 1-15, July.
    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.
    1. Diego Carrión & Edwin García & Manuel Jaramillo & Jorge W. González, 2021. "A Novel Methodology for Optimal SVC Location Considering N-1 Contingencies and Reactive Power Flows Reconfiguration," Energies, MDPI, vol. 14(20), pages 1-17, October.

    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:jmathe:v:10:y:2022:i:8:p:1279-:d:792108. 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.