IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i12p4618-d1167816.html
   My bibliography  Save this article

Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty

Author

Listed:
  • Sizu Hou

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Yisu Hou

    (School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China)

  • Baikui Li

    (CEPRI, China Electric Power Research Institute, Beijing 100192, China)

  • Ziqi Wang

    (School of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, China)

Abstract

In the face of multiple failures caused by extreme disasters, the power and communication sides of the distribution network are interdependent in the fault recovery process. To improve the post-disaster recovery efficiency of the distribution network, this paper proposes a coordinated optimization strategy for distribution network reconfiguration and repair, which integrates the power and communication aspects. First, the recovery process is divided into islanding–reconfiguration and dynamic emergency repair. The coupling relationship between power and communication is considered; that is, power failure may cause communication nodes to lose power, and communication failure may affect the effective operation of remote control devices. Based on this, the fault recovery process is optimized with the objective of maximizing load transfer and direct recovery while introducing a stochastic model predictive control method to handle the uncertainty of distributed power generation by rolling optimization of typical scenarios. Finally, the effectiveness of the proposed strategy is verified using an improved IEEE33-node distribution network system. The simulation results show that the proposed method can recover power to the maximum extent and reduce loss while ensuring the safe and stable operation of the distribution system.

Suggested Citation

  • Sizu Hou & Yisu Hou & Baikui Li & Ziqi Wang, 2023. "Fault Recovery Strategy for Power–Communication Coupled Distribution Network Considering Uncertainty," Energies, MDPI, vol. 16(12), pages 1-21, June.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4618-:d:1167816
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/12/4618/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/16/12/4618/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles," Mathematics, MDPI, vol. 11(3), pages 1-19, January.
    2. Ouyang, Tinghui & Zha, Xiaoming & Qin, Liang & He, Yusen & Tang, Zhenhao, 2019. "Prediction of wind power ramp events based on residual correction," Renewable Energy, Elsevier, vol. 136(C), pages 781-792.
    3. Peijie Li & Di Xu & Hang Su & Zhiyuan Sun, 2023. "A Second-Order Cone Programming Model of Controlled Islanding Strategy Considering Frequency Stability Constraints," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    4. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    5. Hsin-Ching Chih & Wei-Chen Lin & Wei-Tzer Huang & Kai-Chao Yao, 2022. "Implementation of EDGE Computing Platform in Feeder Terminal Unit for Smart Applications in Distribution Networks with Distributed Renewable Energies," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
    6. Madina E. Isametova & Rollan Nussipali & Nikita V. Martyushev & Boris V. Malozyomov & Egor A. Efremenkov & Aysen Isametov, 2022. "Mathematical Modeling of the Reliability of Polymer Composite Materials," Mathematics, MDPI, vol. 10(21), pages 1-19, October.
    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. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Denis V. Valuev & Mengxu Qi, 2023. "Review Models and Methods for Determining and Predicting the Reliability of Technical Systems and Transport," Mathematics, MDPI, vol. 11(15), pages 1-31, July.
    2. Boris V. Malozyomov & Nikita V. Martyushev & Vladimir Yu. Konyukhov & Tatiana A. Oparina & Nikolay A. Zagorodnii & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Analysis of the Reliability of Modern Trolleybuses and Electric Buses," Mathematics, MDPI, vol. 11(15), pages 1-25, July.
    3. Boris V. Malozyomov & Nikita V. Martyushev & Elena V. Voitovich & Roman V. Kononenko & Vladimir Yu. Konyukhov & Vadim Tynchenko & Viktor Alekseevich Kukartsev & Yadviga Aleksandrovna Tynchenko, 2023. "Designing the Optimal Configuration of a Small Power System for Autonomous Power Supply of Weather Station Equipment," Energies, MDPI, vol. 16(13), pages 1-30, June.
    4. Nikita V. Martyushev & Boris V. Malozyomov & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling the Performance of an Electric Vehicle Considering Various Driving Cycles," Mathematics, MDPI, vol. 11(11), pages 1-26, June.
    5. Pranjal Barman & Lachit Dutta & Brian Azzopardi, 2023. "Electric Vehicle Battery Supply Chain and Critical Materials: A Brief Survey of State of the Art," Energies, MDPI, vol. 16(8), pages 1-23, April.
    6. Boris V. Malozyomov & Nikita V. Martyushev & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Sergei Vasilievich Tynchenko & Roman V. Klyuev & Nikolay A. Zagorodnii & Yadviga Aleksandr, 2023. "Study of Supercapacitors Built in the Start-Up System of the Main Diesel Locomotive," Energies, MDPI, vol. 16(9), pages 1-24, May.
    7. Boris V. Malozyomov & Nikita V. Martyushev & Vladislav V. Kukartsev & Vadim S. Tynchenko & Vladimir V. Bukhtoyarov & Xiaogang Wu & Yadviga A. Tyncheko & Viktor A. Kukartsev, 2023. "Overview of Methods for Enhanced Oil Recovery from Conventional and Unconventional Reservoirs," Energies, MDPI, vol. 16(13), pages 1-48, June.
    8. Boris V. Malozyomov & Nikita V. Martyushev & Svetlana N. Sorokova & Egor A. Efremenkov & Mengxu Qi, 2023. "Mathematical Modeling of Mechanical Forces and Power Balance in Electromechanical Energy Converter," Mathematics, MDPI, vol. 11(10), pages 1-11, May.
    9. Abdulelah Alkesaiberi & Fouzi Harrou & Ying Sun, 2022. "Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study," Energies, MDPI, vol. 15(7), pages 1-24, March.
    10. Junwei Fu & Yuna Ni & Yuming Ma & Jian Zhao & Qiuyi Yang & Shiyi Xu & Xiang Zhang & Yuhua Liu, 2023. "A Visualization-Based Ramp Event Detection Model for Wind Power Generation," Energies, MDPI, vol. 16(3), pages 1-16, January.
    11. Tri-Cuong Do & Hoai-An Trinh & Kyoung-Kwan Ahn, 2023. "Hierarchical Control Strategy with Battery Dynamic Consideration for a Dual Fuel Cell/Battery Tramway," Mathematics, MDPI, vol. 11(10), pages 1-19, May.
    12. Boris V. Malozyomov & Vladimir Ivanovich Golik & Vladimir Brigida & Vladislav V. Kukartsev & Yadviga A. Tynchenko & Andrey A. Boyko & Sergey V. Tynchenko, 2023. "Substantiation of Drilling Parameters for Undermined Drainage Boreholes for Increasing Methane Production from Unconventional Coal-Gas Collectors," Energies, MDPI, vol. 16(11), pages 1-16, May.
    13. Zoltán Pusztai & Péter Kőrös & Ferenc Szauter & Ferenc Friedler, 2023. "Implementation of Optimized Regenerative Braking in Energy Efficient Driving Strategies," Energies, MDPI, vol. 16(6), pages 1-20, March.
    14. Li Han & Yan Qiao & Mengjie Li & Liping Shi, 2020. "Wind Power Ramp Event Forecasting Based on Feature Extraction and Deep Learning," Energies, MDPI, vol. 13(23), pages 1-19, December.
    15. Meng, Anbo & Chen, Shu & Ou, Zuhong & Xiao, Jianhua & Zhang, Jianfeng & Chen, Shun & Zhang, Zheng & Liang, Ruduo & Zhang, Zhan & Xian, Zikang & Wang, Chenen & Yin, Hao & Yan, Baiping, 2022. "A novel few-shot learning approach for wind power prediction applying secondary evolutionary generative adversarial network," Energy, Elsevier, vol. 261(PA).
    16. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
    17. Alexander Gusev & Alexander Chervyakov & Anna Alexeenko & Evgeny Nikulchev, 2023. "Particle Swarm Training of a Neural Network for the Lower Upper Bound Estimation of the Prediction Intervals of Time Series," Mathematics, MDPI, vol. 11(20), pages 1-12, October.
    18. Vasyl Mateichyk & Nataliia Kostian & Miroslaw Smieszek & Igor Gritsuk & Valerii Verbovskyi, 2023. "Review of Methods for Evaluating the Energy Efficiency of Vehicles with Conventional and Alternative Power Plants," Energies, MDPI, vol. 16(17), pages 1-25, August.
    19. Yin, Linfei & Wu, Yunzhi, 2022. "Mode-decomposition memory reinforcement network strategy for smart generation control in multi-area power systems containing renewable energy," Applied Energy, Elsevier, vol. 307(C).
    20. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.

    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:12:p:4618-:d:1167816. 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.