IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v12y2019i12p2339-d240978.html
   My bibliography  Save this article

Multivariable Regression Equivalent Model of Interconnected Active Distribution Networks Based on Boundary Measurement

Author

Listed:
  • Anan Zhang

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Huang Huang

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Wei Yang

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

  • Hongwei Li

    (School of Electrical and Information Engineering, Southwest Petroleum University, Chengdu 610500, China)

Abstract

With the increasing complexity of the active distribution network (ADN) due to distributed generation (DG) integration, together with the electricity market evolution, the traditional ADN is divided into multiple areas to operate independently. Due to technical problems or business privacy, the internal network regional control center cannot grasp the changes of the external regional network in time. In order to accurately reflect the distribution network operation state, a multivariable regression equivalent model is proposed in this paper. Firstly, the external network is made equivalent to a multi-port Norton model. The multivariable linear regression model is then derived based on the equivalent distribution network, and the regression model variables are constructed using boundary node information collected by the measurement equipment. Finally, the maximum likelihood estimation (MLE) is used to estimate the parameters of the multivariable linear regression model. Furthermore, case studies demonstrate the effectiveness and robustness of the proposed method, and detailed information of external ADN is unnecessary, except for the boundary node information. The proposed method can also be applied for three-phase unbalanced ADN efficiently.

Suggested Citation

  • Anan Zhang & Huang Huang & Wei Yang & Hongwei Li, 2019. "Multivariable Regression Equivalent Model of Interconnected Active Distribution Networks Based on Boundary Measurement," Energies, MDPI, vol. 12(12), pages 1-17, June.
  • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2339-:d:240978
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/12/12/2339/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/12/12/2339/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Heng-Yi Su & Tzu-Yi Liu, 2017. "A PMU-Based Method for Smart Transmission Grid Voltage Security Visualization and Monitoring," Energies, MDPI, vol. 10(8), pages 1-16, July.
    2. Tiankui Sun & Zhimin Li & Shuang Rong & Jian Lu & Weixing Li, 2017. "Effect of Load Change on the Thevenin Equivalent Impedance of Power System," Energies, MDPI, vol. 10(3), pages 1-6, March.
    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. Heng-Yi Su & Tzu-Yi Liu, 2017. "GECM-Based Voltage Stability Assessment Using Wide-Area Synchrophasors," Energies, MDPI, vol. 10(10), pages 1-16, October.
    2. Do-In Kim, 2021. "Complementary Feature Extractions for Event Identification in Power Systems Using Multi-Channel Convolutional Neural Network," Energies, MDPI, vol. 14(15), pages 1-15, July.
    3. Jiang Li & Wenzhen Wei & Shuo Zhang & Guoqing Li & Chenghong Gu, 2018. "Conditional Maximum Likelihood of Three-Phase Phasor Estimation for μPMU in Active Distribution Networks," Energies, MDPI, vol. 11(5), pages 1-18, May.
    4. Heng-Yi Su & Tzu-Yi Liu, 2017. "A PMU-Based Method for Smart Transmission Grid Voltage Security Visualization and Monitoring," Energies, MDPI, vol. 10(8), pages 1-16, July.
    5. Martin Kanálik & Anastázia Margitová & Ľubomír Beňa & Andrea Kanáliková, 2020. "Power System Impedance Estimation Using a Fast Voltage and Current Changes Measurements," Energies, MDPI, vol. 14(1), pages 1-22, December.

    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:12:y:2019:i:12:p:2339-:d:240978. 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.