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A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids

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Listed:
  • Mohamed Tolba

    (Nuclear Researches Center, Egyptian Atomic Energy Authority, 11787 Cairo, Egypt
    Electrical Power Systems Department, Moscow Power Engineering Institute, 111250 Moscow, Russia)

  • Hegazy Rezk

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, 11991 Wadi Addawaser, Saudi Arabia
    Electrical Engineering Dept., Faculty of Engineering, Minia University, 61111 Minia, Egypt)

  • Ahmed A. Zaki Diab

    (Electrical Engineering Dept., Faculty of Engineering, Minia University, 61111 Minia, Egypt)

  • Mujahed Al-Dhaifallah

    (College of Engineering at Wadi Addawaser, Prince Sattam Bin Abdulaziz University, 11991 Wadi Addawaser, Saudi Arabia
    Systems Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

A novel methodology based on the recent metaheuristic optimization algorithm Salp Swarm Algorithm (SSA) for locating and optimal sizing of renewable distributed generators (RDGs) and shunt capacitor banks (SCBs) on radial distribution networks (RDNs) is proposed. A multi-objective function index ( MOFI ) approach is used for assuring the power quality (PQ) through enhancing the voltage level in addition to minimizing the power losses of the system and the whole operating cost of the grid. The proposed methodology is tested via 33-Bus standard radial distribution networks at different scenarios to prove their validity and performance. The obtained results are compared with the Grasshopper Optimization Algorithm (GOA), and the hybrid Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (PSOGSA). The SSA optimizer proved its superiority with high attitude and accuracy for solving the problems of RDGs’ and SCBs’ locations and capacities simultaneously. An Egyptian practical case study at different load levels via different scenarios including the control operation within 24 h is considered.

Suggested Citation

  • Mohamed Tolba & Hegazy Rezk & Ahmed A. Zaki Diab & Mujahed Al-Dhaifallah, 2018. "A Novel Robust Methodology Based Salp Swarm Algorithm for Allocation and Capacity of Renewable Distributed Generators on Distribution Grids," Energies, MDPI, vol. 11(10), pages 1-34, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:10:p:2556-:d:171993
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    References listed on IDEAS

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    1. Mohamed A. Tolba & Hegazy Rezk & Vladimir Tulsky & Ahmed A. Zaki Diab & Almoataz Y. Abdelaziz & Artem Vanin, 2018. "Impact of Optimum Allocation of Renewable Distributed Generations on Distribution Networks Based on Different Optimization Algorithms," Energies, MDPI, vol. 11(1), pages 1-33, January.
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    5. Sultana, U. & Khairuddin, Azhar B. & Aman, M.M. & Mokhtar, A.S. & Zareen, N., 2016. "A review of optimum DG placement based on minimization of power losses and voltage stability enhancement of distribution system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 63(C), pages 363-378.
    6. 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.
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    Cited by:

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    6. S. Angalaeswari & P. Sanjeevikumar & K. Jamuna & Zbigniew Leonowicz, 2020. "Hybrid PIPSO-SQP Algorithm for Real Power Loss Minimization in Radial Distribution Systems with Optimal Placement of Distributed Generation," Sustainability, MDPI, vol. 12(14), pages 1-21, July.
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    8. Hegazy Rezk & Ahmed Fathy & Ahmed A. Zaki Diab & Mujahed Al-Dhaifallah, 2019. "The Application of Water Cycle Optimization Algorithm for Optimal Placement of Wind Turbines in Wind Farms," Energies, MDPI, vol. 12(22), pages 1-19, November.
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    10. Bin Yang & Kangli Liu & Sen Zhang & Jianfeng Zhao, 2018. "Design and Implementation of Novel Multi-Converter-Based Unified Power Quality Conditioner for Low-Voltage High-Current Distribution System," Energies, MDPI, vol. 11(11), pages 1-21, November.

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