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On State Estimation Modeling of Smart Distribution Networks: A Technical Review

Author

Listed:
  • Junjun Xu

    (State Key Laboratory of Smart Grid Protection and Control, Nanjing 211106, China
    College of Automation & College of Artificial Intelligence, Nanjing University of Posts and Telecommunications, Nanjing 210023, China)

  • Yulong Jin

    (NARI Technology Co., Ltd., Nanjing 211106, China
    NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Tao Zheng

    (NARI Technology Co., Ltd., Nanjing 211106, China
    NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China)

  • Gaojun Meng

    (Jiangsu Collaborative Innovation Center for Smart Distribution Network, Nanjing 211167, China)

Abstract

State estimation (SE) is regarded as an essential tool for achieving the secure and efficient operation of distribution networks, and extensive research on SE has been conducted over the past three decades. Nonetheless, the high penetration of distribution generations (DGs) is accompanied by uncertainties and dynamics, and the extensive application of intelligent electronic devices (IEDs) is associated with data processing issues, all of which raise new challenges, and these issues must be taken care of for further development of SE in smart distribution networks. This paper attempts to present a comprehensive literature review of numerous works that address various issues in SE, examining key technical research issues and future perspectives. Hopefully, it will be able to meet the needs for the development of smart distribution networks.

Suggested Citation

  • Junjun Xu & Yulong Jin & Tao Zheng & Gaojun Meng, 2023. "On State Estimation Modeling of Smart Distribution Networks: A Technical Review," Energies, MDPI, vol. 16(4), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1891-:d:1068321
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    References listed on IDEAS

    as
    1. Leijiao Ge & Yuanliang Li & Yuanliang Li & Jun Yan & Yonghui Sun, 2022. "Smart Distribution Network Situation Awareness for High-Quality Operation and Maintenance: A Brief Review," Energies, MDPI, vol. 15(3), pages 1-24, January.
    2. David Macii & Daniele Fontanelli & Grazia Barchi, 2020. "A Distribution System State Estimator Based on an Extended Kalman Filter Enhanced with a Prior Evaluation of Power Injections at Unmonitored Buses," Energies, MDPI, vol. 13(22), pages 1-25, November.
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    Cited by:

    1. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.

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