IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i9p2001-d1381198.html
   My bibliography  Save this article

A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm

Author

Listed:
  • Yonggang Wang

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

  • Fuxian Li

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

  • Ruimin Xiao

    (State Grid Huludao Electric Power Supply Company, Huludao 125000, China)

  • Nannan Zhang

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

Abstract

With the expansion of the scale of electric power, high-quality electrical energy remains a crucial aspect of power system management and operation. The generation of reactive power is the primary cause of the decline in electrical energy quality. Therefore, optimization of reactive power in the power system becomes particularly important. The primary objective of this article is to create a multi-objective reactive power optimization (MORPO) model for distribution networks. The model aims to minimize reactive power loss, reduce the overall compensation required for reactive power devices, and minimize the total sum of node voltage deviations. To tackle the MORPO problems for distribution networks, the improved sparrow search algorithm–particle swarm optimization (ISSA-PSO) algorithm is proposed. Specifically, two improvements are proposed in this paper. The first is to introduce a chaotic mapping mechanism to enhance the diversity of the population during initialization. The second is to introduce a three-stage differential evolution mechanism to improve the global exploration capability of the algorithm. The proposed algorithm is tested on the IEEE 33-node system and the practical 22-node system. The results indicate a reduction of 32.71% in network losses for the IEEE 33-node system after optimization, and the average voltage of the circuit increases from 0.9485 p.u. to 0.9748 p.u. At the same time, optimization results in a reduction of 44.07% in network losses for the practical 22-node system, and the average voltage of the circuit increases from 0.9838 p.u. to 0.9921 p.u. Therefore, the proposed method exhibits better performance for reducing network losses and enhancing voltage levels.

Suggested Citation

  • Yonggang Wang & Fuxian Li & Ruimin Xiao & Nannan Zhang, 2024. "A Systematic Investigation into the Optimization of Reactive Power in Distribution Networks Using the Improved Sparrow Search Algorithm–Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 17(9), pages 1-22, April.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:9:p:2001-:d:1381198
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/9/2001/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/9/2001/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yonggang Wang & Shengnan Dai & Pinchi Liu & Xinyu Zhao, 2023. "A Hybrid Particle Swarm Optimization with Butterfly Optimization Algorithm Based Maximum Power Point Tracking for Photovoltaic Array under Partial Shading Conditions," Sustainability, MDPI, vol. 15(16), pages 1-21, August.
    2. Hu, Wei & Guo, Qiuting & Wang, Wei & Wang, Weiheng & Song, Shuhong, 2022. "Loss reduction strategy and evaluation system based on reasonable line loss interval of transformer area," Applied Energy, Elsevier, vol. 306(PB).
    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. Chen Liang & Chang Chen & Weizhou Wang & Xiping Ma & Yuying Li & Tong Jiang, 2022. "Line Loss Interval Algorithm for Distribution Network with DG Based on Linear Optimization under Abnormal or Missing Measurement Data," Energies, MDPI, vol. 15(11), pages 1-16, June.
    2. Wang, Yonggang & Zhao, Kaixing & Hao, Yue & Yao, Yilin, 2024. "Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory," Applied Energy, Elsevier, vol. 366(C).
    3. Li Huang & Gan Zhou & Jian Zhang & Ying Zeng & Lei Li, 2023. "Calculation Method of Theoretical Line Loss in Low-Voltage Grids Based on Improved Random Forest Algorithm," Energies, MDPI, vol. 16(7), pages 1-16, March.
    4. Mantas Plienis & Tomas Deveikis & Audrius Jonaitis & Saulius Gudžius, 2023. "Design of IOT-Based Framework for Evaluation of Energy Efficiency in Power Transformers," Energies, MDPI, vol. 16(11), pages 1-15, May.

    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:17:y:2024:i:9:p:2001-:d:1381198. 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.