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Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution

Author

Listed:
  • Dhivya Swaminathan

    (School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Arul Rajagopalan

    (School of Electrical Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India)

  • Oscar Danilo Montoya

    (Grupo de Compatibilidad e Interferencia Electromagnética (GCEM), Facultad de Ingeniería, Universidad Distrital Francisco José de Caldas, Bogotá 110231, Colombia)

  • Savitha Arul

    (Department of Electrical and Electronics Engineering, Rajalakshmi Engineering College, Chennai 602105, Tamil Nadu, India)

  • Luis Fernando Grisales-Noreña

    (Department of Electrical Engineering, Faculty of Engineering, Universidad de Talca, Curicó 3340000, Chile)

Abstract

Power losses (PL) are one of the most—if not the most—vital concerns in power distribution networks (DN). With respect to sustainability, distribution network reconfiguration (DNR) is an effective course of action to minimize power losses. However, the optimal DNR is usually a non-convex optimization process that necessitates the employment of powerful global optimization methods. This paper proposes a novel hybrid metaheuristic optimization (MO) method called the chaotic golden flower algorithm (CGFA) for PL minimization. As the name implies, the proposed method combines the golden search method with the flower pollination algorithm to multiply their benefits, guarantee the best solution, and reduce convergence time. The performance of the algorithm has been evaluated under different test systems, including the IEEE 33-bus, IEEE 69-bus, and IEEE 119-bus systems and the smart city (SC) network, each of which includes distributed-generation (DG) units and energy storage systems (ESS). In addition, the locations of tie-switches in the DN, which used to be considered as given information in previous studies, are assumed to be variable, and a branch-exchange adaption is included in the reconfiguration process. Furthermore, uncertainty analysis, such as bus and/or line fault conditions, are studied, and the performance of the proposed method is compared with other pioneering MO algorithms with minimal standard deviations ranging from 0.0012 to 0.0101. The case study of SC is considered and the obtained simulation results show the superiority of the algorithm in finding higher PL reduction under different scenarios, with the lowest standard deviations ranging from 0.012 to 0.0432.

Suggested Citation

  • Dhivya Swaminathan & Arul Rajagopalan & Oscar Danilo Montoya & Savitha Arul & Luis Fernando Grisales-Noreña, 2023. "Distribution Network Reconfiguration Based on Hybrid Golden Flower Algorithm for Smart Cities Evolution," Energies, MDPI, vol. 16(5), pages 1-24, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2454-:d:1087811
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    References listed on IDEAS

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    1. Mohammadian, M. & Lorestani, A. & Ardehali, M.M., 2018. "Optimization of single and multi-areas economic dispatch problems based on evolutionary particle swarm optimization algorithm," Energy, Elsevier, vol. 161(C), pages 710-724.
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    4. Dhivya Swaminathan & Arul Rajagopalan, 2022. "Optimized Network Reconfiguration with Integrated Generation Using Tangent Golden Flower Algorithm," Energies, MDPI, vol. 15(21), pages 1-19, November.
    5. Arul Rajagopalan & Dhivya Swaminathan & Meshal Alharbi & Sudhakar Sengan & Oscar Danilo Montoya & Walid El-Shafai & Mostafa M. Fouda & Moustafa H. Aly, 2022. "Modernized Planning of Smart Grid Based on Distributed Power Generations and Energy Storage Systems Using Soft Computing Methods," Energies, MDPI, vol. 15(23), pages 1-18, November.
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    Cited by:

    1. Santiago Bustamante-Mesa & Jorge W. Gonzalez-Sanchez & Sergio D. Saldarriaga-Zuluaga & Jesús M. López-Lezama & Nicolás Muñoz-Galeano, 2024. "Optimal Estimation of Under-Frequency Load Shedding Scheme Parameters by Considering Virtual Inertia Injection," Energies, MDPI, vol. 17(2), pages 1-20, January.
    2. Xin Yan & Qian Zhang, 2023. "Research on Combination of Distributed Generation Placement and Dynamic Distribution Network Reconfiguration Based on MIBWOA," Sustainability, MDPI, vol. 15(12), pages 1-34, June.
    3. Wei-Chen Lin & Chao-Hsien Hsiao & Wei-Tzer Huang & Kai-Chao Yao & Yih-Der Lee & Jheng-Lun Jian & Yuan Hsieh, 2024. "Network Reconfiguration Framework for CO 2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms," Sustainability, MDPI, vol. 16(4), pages 1-17, February.
    4. Elham Mahdavi & Seifollah Asadpour & Leonardo H. Macedo & Rubén Romero, 2023. "Reconfiguration of Distribution Networks with Simultaneous Allocation of Distributed Generation Using the Whale Optimization Algorithm," Energies, MDPI, vol. 16(12), pages 1-19, June.
    5. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "Optimal Integration of Distribution Network Reconfiguration and Conductor Selection in Power Distribution Systems via MILP," Energies, MDPI, vol. 16(19), pages 1-25, October.
    6. Mohammed Alqahtani & Ponnusamy Marimuthu & Veerasamy Moorthy & B. Pangedaiah & Ch. Rami Reddy & M. Kiran Kumar & Muhammad Khalid, 2023. "Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization," Energies, MDPI, vol. 16(12), pages 1-15, June.
    7. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.

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