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A Novel Graphically-Based Network Reconfiguration for Power Loss Minimization in Large Distribution Systems

Author

Listed:
  • Ibrahim Mohamed Diaaeldin

    (Engineering Physics and Mathematics Department, Ain Shams University, Cairo 11517, Egypt)

  • Shady H. E. Abdel Aleem

    (Mathematical, Physical and Engineering Sciences Department, 15th of May Higher Institute of Engineering, Cairo 11731, Egypt)

  • Ahmed El-Rafei

    (Engineering Physics and Mathematics Department, Ain Shams University, Cairo 11517, Egypt)

  • Almoataz Y. Abdelaziz

    (Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Ahmed F. Zobaa

    (Electronic and Computer Engineering Department, Brunel University London, Uxbridge UB8 3PH, UK)

Abstract

Distribution network reconfiguration (DNR) is the optimized change in the topological structure of distribution systems without violating its radial configuration. DNR has been of interest in applied mathematics and engineering because of its importance in modern power systems. In literature, various optimization techniques that constitute a large area of applied mathematics were proposed to obtain optimized radial configurations; however, most of them were tested in small distribution systems. In this paper, a novel graphically-based DNR is proposed to obtain the optimized radial configurations for power loss minimization. The proposed DNR is based on the graphical representation of the distribution system without any need for a radiality check. Case studies were conducted on 16-, 33-, 70-, 83-, 136-, 415-, 880-, 1760-, and 4400-node distribution systems in order to minimize the total power loss. Results have proven the ability of the proposed graphical DNR for power loss minimization by obtaining fast radial configurations in comparison with previous studies and also its ability to deal with large distribution systems efficiently. The proposed DNR succeeded in minimizing the total losses for large distribution systems as the 880-, 1760-, and 4400-node distribution systems by 69.45%, 72.51%, and 74.35%, respectively.

Suggested Citation

  • Ibrahim Mohamed Diaaeldin & Shady H. E. Abdel Aleem & Ahmed El-Rafei & Almoataz Y. Abdelaziz & Ahmed F. Zobaa, 2019. "A Novel Graphically-Based Network Reconfiguration for Power Loss Minimization in Large Distribution Systems," Mathematics, MDPI, vol. 7(12), pages 1-17, December.
    • Handle: RePEc:gam:jmathe:v:7:y:2019:i:12:p:1182-:d:293726
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    References listed on IDEAS

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    1. Ibrahim Diaaeldin & Shady Abdel Aleem & Ahmed El-Rafei & Almoataz Abdelaziz & Ahmed F. Zobaa, 2019. "Optimal Network Reconfiguration in Active Distribution Networks with Soft Open Points and Distributed Generation," Energies, MDPI, vol. 12(21), pages 1-31, November.
    2. Sherif M. Ismael & Shady H. E. Abdel Aleem & Almoataz Y. Abdelaziz & Ahmed F. Zobaa, 2019. "Probabilistic Hosting Capacity Enhancement in Non-Sinusoidal Power Distribution Systems Using a Hybrid PSOGSA Optimization Algorithm," Energies, MDPI, vol. 12(6), pages 1-23, March.
    3. Ali, E.S. & Abd Elazim, S.M. & Abdelaziz, A.Y., 2017. "Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations," Renewable Energy, Elsevier, vol. 101(C), pages 1311-1324.
    4. Badran, Ola & Mekhilef, Saad & Mokhlis, Hazlie & Dahalan, Wardiah, 2017. "Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 854-867.
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    Cited by:

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    2. Zbigniew Kłosowski & Łukasz Mazur, 2023. "Influence of the Type of Receiver on Electrical Energy Losses in Power Grids," Energies, MDPI, vol. 16(15), pages 1-22, July.
    3. Artis, Reza & Assili, Mohsen & Shivaie, Mojtaba, 2022. "A seismic-resilient multi-level framework for distribution network reinforcement planning considering renewable-based multi-microgrids," Applied Energy, Elsevier, vol. 325(C).
    4. 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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