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

Author

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  • 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
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    References listed on IDEAS

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    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).
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.

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