IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i13p10732-d1189277.html
   My bibliography  Save this article

Voltage Zoning Regulation Method of Distribution Network with High Proportion of Photovoltaic Considering Energy Storage Configuration

Author

Listed:
  • Fangfang Zheng

    (College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Xiaofang Meng

    (College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Tiefeng Xu

    (College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China)

  • Yongchang Sun

    (Economic and Technological Development Zone Heating Limited Company, Dalian 116600, China)

  • Nannan Zhang

    (College of Information and Electric Engineering, Shenyang Agricultural University, Shenyang 110866, China)

Abstract

Photovoltaics have uncertain characteristics. If a high proportion of photovoltaics are connected to the distribution network, the voltage will exceed the limit. In order to solve this problem, a voltage regulation method of a distribution network considering energy storage partition configuration is proposed. Taking the minimum total voltage deviation, the minimum total cost, the minimum total power loss, and the minimum energy storage device installation ratio as the objective function, and considering various conditions, such as voltage deviation constraint and energy storage constraint, a mathematical model of voltage regulation is established. Firstly, a high proportion of photovoltaics are connected to the distribution network, and the voltage deviation curve is obtained. The optimal k value is determined by the elbow rule. The voltage deviation curve of each node is clustered by the k-means algorithm so as to determine the energy storage device partition. The energy storage device is connected to various clustering centers, and then the weighting factor of each objective function is determined by the fuzzy comprehensive evaluation method. For comparison and analysis, ( k + 1) schemes are determined through the partition configuration of ( k + 1) energy storage devices. Then, the model is solved by particle swarm optimization, and the unit output result and the minimum objective function value are obtained. Finally, an example of IEEE33 is used to verify the effectiveness of the proposed model.

Suggested Citation

  • Fangfang Zheng & Xiaofang Meng & Tiefeng Xu & Yongchang Sun & Nannan Zhang, 2023. "Voltage Zoning Regulation Method of Distribution Network with High Proportion of Photovoltaic Considering Energy Storage Configuration," Sustainability, MDPI, vol. 15(13), pages 1-19, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10732-:d:1189277
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/13/10732/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/13/10732/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fangfang Zheng & Xiaofang Meng & Lidi Wang & Nannan Zhang, 2023. "Power Flow Optimization Strategy of Distribution Network with Source and Load Storage Considering Period Clustering," Sustainability, MDPI, vol. 15(5), pages 1-14, March.
    2. Zhang, Zhengfa & da Silva, Filipe Faria & Guo, Yifei & Bak, Claus Leth & Chen, Zhe, 2022. "Coordinated voltage control in unbalanced distribution networks with two-stage distributionally robust chance-constrained receding horizon control," Renewable Energy, Elsevier, vol. 198(C), pages 907-915.
    3. Fangfang Zheng & Xiaofang Meng & Lidi Wang & Nannan Zhang, 2023. "Operation Optimization Method of Distribution Network with Wind Turbine and Photovoltaic Considering Clustering and Energy Storage," Sustainability, MDPI, vol. 15(3), pages 1-22, January.
    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. Yin, Linfei & He, Xiaoyu, 2023. "Artificial emotional deep Q learning for real-time smart voltage control of cyber-physical social power systems," Energy, Elsevier, vol. 273(C).
    2. Lin Zhu & Xiaofang Meng & Lidi Wang & Nannan Zhang & Hui Wang, 2023. "Voltage Control Strategy for Low-Voltage Distribution Network with Distributed Energy Storage Participating in Regulation under Low-Carbon Background," Sustainability, MDPI, vol. 15(13), pages 1-15, June.

    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:jsusta:v:15:y:2023:i:13:p:10732-:d:1189277. 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.