IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i8p3540-d1127565.html
   My bibliography  Save this article

A Diagnosis Method of Power Flow Convergence Failure for Bulk Power Systems Based on Intermediate Iteration Data

Author

Listed:
  • Gang Mu

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

  • Yibo Zhou

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

  • Mao Yang

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

  • Jiahao Chen

    (Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin City 132012, China)

Abstract

Power flow calculation is the foundation of security analyses in a power system, and the phenomenon of convergence failure is becoming more prominent with the expansion of the power grid. The existing convergence failure diagnosis methods based on optimization modeling and local feature recognition are no longer viable for bulk power systems. This paper proposes a diagnosis method based on intermediate iteration data and the identification of the transmission power congested channel. Firstly, the transmission power congestion index is constructed, and then a method for identifying transmission congestion channels is proposed. The reasons for convergence failure of the power flow are diagnosed from two aspects: excessive power to be transmitted and insufficient transmission capacity. Finally, with the aim of alleviating transmission channel congestion, a correction strategy for power flow injection space data was constructed, which generates relaxation schemes for operational variables. The effectiveness of the proposed strategy was verified using the simulation results of an actual provincial power grid and a standard example power system with 13,659 buses. The method proposed in this paper is entirely based on intermediate power flow iteration data, which avoids the complex modeling of the power flow adjustment and provides methodological support for power flow diagnosis in bulk power systems.

Suggested Citation

  • Gang Mu & Yibo Zhou & Mao Yang & Jiahao Chen, 2023. "A Diagnosis Method of Power Flow Convergence Failure for Bulk Power Systems Based on Intermediate Iteration Data," Energies, MDPI, vol. 16(8), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3540-:d:1127565
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/8/3540/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/8/3540/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Steinegger, Josef & Wallner, Stefan & Greiml, Matthias & Kienberger, Thomas, 2023. "A new quasi-dynamic load flow calculation for district heating networks," Energy, Elsevier, vol. 266(C).
    2. Alex Valenzuela & Silvio Simani & Esteban Inga, 2021. "Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication," Energies, MDPI, vol. 14(11), pages 1-22, June.
    3. Jingcheng Zhou & Wei Wei & Ruizhi Zhang & Zhiming Zheng, 2021. "Damped Newton Stochastic Gradient Descent Method for Neural Networks Training," Mathematics, MDPI, vol. 9(13), pages 1-12, June.
    4. Xiao, Hao & Pei, Wei & Wu, Lei & Ma, Li & Ma, Tengfei & Hua, Weiqi, 2023. "A novel deep learning based probabilistic power flow method for Multi-Microgrids distribution system with incomplete network information," Applied Energy, Elsevier, vol. 335(C).
    5. Edy Quintana & Esteban Inga, 2022. "Optimal Reconfiguration of Electrical Distribution System Using Heuristic Methods with Geopositioning Constraints," Energies, MDPI, vol. 15(15), pages 1-20, 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. Alex Guamán & Alex Valenzuela, 2021. "Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms," Energies, MDPI, vol. 14(20), pages 1-16, October.
    2. Dénarié, A. & Aprile, M. & Motta, M., 2023. "Dynamical modelling and experimental validation of a fast and accurate district heating thermo-hydraulic modular simulation tool," Energy, Elsevier, vol. 282(C).
    3. Chen, Yujia & Pei, Wei & Ma, Tengfei & Xiao, Hao, 2023. "Asymmetric Nash bargaining model for peer-to-peer energy transactions combined with shared energy storage," Energy, Elsevier, vol. 278(PB).
    4. Harold Doran, 2023. "A Collection of Numerical Recipes Useful for Building Scalable Psychometric Applications," Journal of Educational and Behavioral Statistics, , vol. 48(1), pages 37-69, February.
    5. Asier Zulueta & Decebal Aitor Ispas-Gil & Ekaitz Zulueta & Joseba Garcia-Ortega & Unai Fernandez-Gamiz, 2022. "Battery Sizing Optimization in Power Smoothing Applications," Energies, MDPI, vol. 15(3), pages 1-20, January.
    6. Liu, Zhikai & Zhang, Huan & Wang, Yaran & Fan, Xianwang & You, Shijun & Jiang, Yan & Gao, Xinlei, 2023. "Optimization of hydraulic distribution using loop adjustment method in meshed district heating system with multiple heat sources," Energy, Elsevier, vol. 284(C).
    7. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.
    8. Lin, Xiaojie & Mao, Yihui & Chen, Jiaying & Zhong, Wei, 2023. "Dynamic modeling and uncertainty quantification of district heating systems considering renewable energy access," Applied Energy, Elsevier, vol. 349(C).

    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:16:y:2023:i:8:p:3540-:d:1127565. 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.