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Energy Saving Maximization of Balanced and Unbalanced Distribution Power Systems via Network Reconfiguration and Optimum Capacitor Allocation Using a Hybrid Metaheuristic Algorithm

Author

Listed:
  • Mohamed Abd-El-Hakeem Mohamed

    (Faculty of Engineering, Electric Department, Al-Azhar University, Qena 83511, Egypt)

  • Ziad M. Ali

    (Electrical Engineering Department, College of Engineering at Wadi Addawaser, Prince Sattam bin AbdulAziz University, Wadi Addawaser 11991, Saudi Arabia
    Electrical Engineering Department, Aswan faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Mahrous Ahmed

    (Department of Electrical Engineering, College of Engineering, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Saad F. Al-Gahtani

    (Electrical Engineering Department, Faculty of Engineering, King Khalid University, Abha 61411, Saudi Arabia)

Abstract

The main aim of this work was the maximization of the energy saving of balanced and unbalanced distribution power systems via system reconfiguration and the optimum capacitor’s bank choice, which were estimated by using a new algorithm: modified Tabu search and Harper sphere search (MTS-HSSA). The results demonstrated that the proposed method is appropriate for energy saving and improving performance compared with other methods reported in the literature for IEEE 33-bus adopted systems, including large scale systems such as IEEE 119 and the IEEE 123 unbalanced distribution system. Moreover, it can be used for unbalanced distribution systems distributed generators (DGs). The results demonstrated that the proposed method (the optimal choice of shunt capacitor (SC) banks and the optimal reconfiguration via the proposed algorithm) is appropriate for energy saving compared with different strategies for energy saving, which included distributed generation (DG) at different cost levels.

Suggested Citation

  • Mohamed Abd-El-Hakeem Mohamed & Ziad M. Ali & Mahrous Ahmed & Saad F. Al-Gahtani, 2021. "Energy Saving Maximization of Balanced and Unbalanced Distribution Power Systems via Network Reconfiguration and Optimum Capacitor Allocation Using a Hybrid Metaheuristic Algorithm," Energies, MDPI, vol. 14(11), pages 1-24, May.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:11:p:3205-:d:565793
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    References listed on IDEAS

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    1. Ali Ahmadian & Ali Elkamel & Abdelkader Mazouz, 2019. "An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network," Energies, MDPI, vol. 12(16), pages 1-14, August.
    2. 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.
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    4. Tung Tran The & Dieu Vo Ngoc & Nguyen Tran Anh, 2020. "Distribution Network Reconfiguration for Power Loss Reduction and Voltage Profile Improvement Using Chaotic Stochastic Fractal Search Algorithm," Complexity, Hindawi, vol. 2020, pages 1-15, March.
    5. Talavera, D.L. & Pérez-Higueras, P. & Ruíz-Arias, J.A. & Fernández, E.F., 2015. "Levelised cost of electricity in high concentrated photovoltaic grid connected systems: Spatial analysis of Spain," Applied Energy, Elsevier, vol. 151(C), pages 49-59.
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    Cited by:

    1. Nicholas D. de Andrade & Ruben B. Godoy & Edson A. Batista & Moacyr A. G. de Brito & Rafael L. R. Soares, 2022. "Embedded FPGA Controllers for Current Compensation Based on Modern Power Theories," Energies, MDPI, vol. 15(17), pages 1-17, August.
    2. Minsheng Yang & Jianqi Li & Rui Du & Jianying Li & Jian Sun & Xiaofang Yuan & Jiazhu Xu & Shifu Huang, 2022. "Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization," Energies, MDPI, vol. 15(6), pages 1-16, March.
    3. Cassio Gerez & Eduardo Coelho Marques Costa & Alfeu J. Sguarezi Filho, 2022. "Distribution Network Reconfiguration Considering Voltage and Current Unbalance Indexes and Variable Demand Solved through a Selective Bio-Inspired Metaheuristic," Energies, MDPI, vol. 15(5), pages 1-27, February.

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