IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i11p9034-d1162998.html
   My bibliography  Save this article

Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach

Author

Listed:
  • Abdulaziz Alanazi

    (Department of Electrical Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi Arabia)

  • Tarek I. Alanazi

    (Department of Physics, College of Science, Northern Border University, Arar 73222, Saudi Arabia)

Abstract

Distribution network operators and planners face a significant challenge in optimizing planning and scheduling strategies to enhance distribution network efficiency. Using improved particle swarm optimization (IPSO), this paper presents an effective method for improving distribution system performance by concurrently deploying remote-controlled sectionalized switches, distributed generation (DG), and optimal network reconfiguration. The proposed optimization problem’s main objectives are to reduce switch costs, maximize reliability, reduce power losses, and enhance voltage profiles. An analytical reliability evaluation is proposed for DG-enhanced reconfigurable distribution systems, considering both switching-only and repairs and switching interruptions. The problem is formulated in the form of a mixed integer nonlinear programming problem, which is known as an NP-hard problem. To solve the problem effectively while improving conventional particle swarm optimization (PSO) exploration and exploitation capabilities, a novel chaotic inertia weight and crossover operation mechanism is developed here. It is demonstrated that IPSO can be applied to both single- and multi-objective optimization problems, where distribution systems’ optimization strategies are considered sequentially and simultaneously. Furthermore, IPSO’s effectiveness is validated and evaluated against well-known state-of-the-art metaheuristic techniques for optimizing IEEE 69-node distribution systems.

Suggested Citation

  • Abdulaziz Alanazi & Tarek I. Alanazi, 2023. "Multi-Objective Framework for Optimal Placement of Distributed Generations and Switches in Reconfigurable Distribution Networks: An Improved Particle Swarm Optimization Approach," Sustainability, MDPI, vol. 15(11), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:11:p:9034-:d:1162998
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/11/9034/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/11/9034/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alanazi, Mohana & Alanazi, Abdulaziz & Akbari, Mohammad Amin & Deriche, Mohamed & Memon, Zulfiqar Ali, 2023. "A non-simulation-based linear model for analytical reliability evaluation of radial distribution systems considering renewable DGs," Applied Energy, Elsevier, vol. 342(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Samson Oladayo Ayanlade & Funso Kehinde Ariyo & Abdulrasaq Jimoh & Kayode Timothy Akindeji & Adeleye Oluwaseye Adetunji & Emmanuel Idowu Ogunwole & Dolapo Eniola Owolabi, 2023. "Optimal Allocation of Photovoltaic Distributed Generations in Radial Distribution Networks," Sustainability, MDPI, vol. 15(18), pages 1-26, September.
    2. Abdullrahman A. Al-Shamma’a & Hassan M. Hussein Farh & Khalil Alsharabi, 2024. "Integrating Firefly and Crow Algorithms for the Resilient Sizing and Siting of Renewable Distributed Generation Systems under Faulty Scenarios," Sustainability, MDPI, vol. 16(4), pages 1-17, February.
    3. Muhammad Usman Riaz & Suheel Abdullah Malik & Amil Daraz & Hasan Alrajhi & Ahmed N. M. Alahmadi & Abdul Rahman Afzal, 2024. "Advanced Energy Management in a Sustainable Integrated Hybrid Power Network Using a Computational Intelligence Control Strategy," Energies, MDPI, vol. 17(20), pages 1-53, October.
    4. David W. Puma & Y. P. Molina & Brayan A. Atoccsa & J. E. Luyo & Zocimo Ñaupari, 2024. "Distribution Network Reconfiguration Optimization Using a New Algorithm Hyperbolic Tangent Particle Swarm Optimization (HT-PSO)," Energies, MDPI, vol. 17(15), pages 1-13, August.

    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.

      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:11:p:9034-:d:1162998. 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.