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An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

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  • Ali Ahmadian

    (College of Engineering, University of Waterloo, Waterloo, ON N2J 0A1, Canada
    Department of Electrical Engineering, University of Bonab, Bonab 5551761167, Iran)

  • Ali Elkamel

    (College of Engineering, University of Waterloo, Waterloo, ON N2J 0A1, Canada
    Department of Chemical Engineering, The Petroleum Institute, Khalifa University of Science and Technology, Abu Dhabi 127788, UAE)

  • Abdelkader Mazouz

    (College of Business Administration, Al Ain University of Science and Technology, Al Ain 64141, UAE)

Abstract

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3052-:d:255766
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    References listed on IDEAS

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    1. Ahmadian, Ali & Sedghi, Mahdi & Aliakbar-Golkar, Masoud & Elkamel, Ali & Fowler, Michael, 2016. "Optimal probabilistic based storage planning in tap-changer equipped distribution network including PEVs, capacitor banks and WDGs: A case study for Iran," Energy, Elsevier, vol. 112(C), pages 984-997.
    2. Ahmadian, Ali & Sedghi, Mahdi & Fgaier, Hedia & Mohammadi-ivatloo, Behnam & Golkar, Masoud Aliakbar & Elkamel, Ali, 2019. "PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect," Energy, Elsevier, vol. 175(C), pages 265-277.
    3. Daghi, Majid & Sedghi, Mahdi & Ahmadian, Ali & Aliakbar-Golkar, Masoud, 2016. "Factor analysis based optimal storage planning in active distribution network considering different battery technologies," Applied Energy, Elsevier, vol. 183(C), pages 456-469.
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    Cited by:

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    5. Julián Alejandro Vega-Forero & Jairo Stiven Ramos-Castellanos & Oscar Danilo Montoya, 2023. "Application of the Generalized Normal Distribution Optimization Algorithm to the Optimal Selection of Conductors in Three-Phase Asymmetric Distribution Networks," Energies, MDPI, vol. 16(3), pages 1-35, January.
    6. 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.
    7. Héctor Migallón & Akram Belazi & José-Luis Sánchez-Romero & Héctor Rico & Antonio Jimeno-Morenilla, 2020. "Settings-Free Hybrid Metaheuristic General Optimization Methods," Mathematics, MDPI, vol. 8(7), pages 1-25, July.

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