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Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation

Author

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  • Usama Khaled

    (Electrical Engineering Department, College of Engineering, King Saud University, P.O. Box. 800, Riyadh 11421, Saudi Arabia
    Electrical Engineering Department, Faculty of Energy Engineering, Aswan University, Aswan Governorate 81528, Egypt)

  • Ali M. Eltamaly

    (Electrical Engineering Department, Mansoura University, Mansoura 35516, Egypt
    Sustainable Energy Technology Center, King Saud University, Riyadh 11421, Saudi Arabia)

  • Abderrahmane Beroual

    (Ecole Centrale de Lyon, University of Lyon, Ampere CNRS UMR 5005, 36 avenue Guy Collongue, Ecully 69134, France)

Abstract

The problem of voltage collapse in power systems due to increased loads can be solved by adding renewable energy sources like wind and photovoltaic (PV) to some bus-bars. This option can reduce the cost of the generated energy and increase the system efficiency and reliability. In this paper, a modified smart technique using particle swarm optimization (PSO) has been introduced to select the hourly optimal load flow with renewable distributed generation (DG) integration under different operating conditions in the 30-bus IEEE system. Solar PV and wind power plants have been introduced to selected buses to evaluate theirs benefits as DG. Different solar radiation and wind speeds for the Dammam site in Saudi Arabia have been used as an example to study the feasibility of renewable energy integration and its effect on power system operation. Sensitivity analysis to the load and the other input data has been carried out to predict the sensitivity of the results to any deviation in the input data of the system. The obtained results from the proposed system prove that using of renewable energy sources as a DG reduces the generation and operation cost of the overall power system.

Suggested Citation

  • Usama Khaled & Ali M. Eltamaly & Abderrahmane Beroual, 2017. "Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation," Energies, MDPI, vol. 10(7), pages 1-14, July.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:7:p:1013-:d:104901
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    References listed on IDEAS

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    Cited by:

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    12. Samson Ademola Adegoke & Yanxia Sun, 2023. "Diminishing Active Power Loss and Improving Voltage Profile Using an Improved Pathfinder Algorithm Based on Inertia Weight," Energies, MDPI, vol. 16(3), pages 1-14, January.
    13. Gracita Batista Rosas & Elizete Maria Lourenço & Djalma Mosqueira Falcão & Thelma Solange Piazza Fernandes, 2019. "An Expeditious Methodology to Assess the Effects of Intermittent Generation on Power Systems," Energies, MDPI, vol. 12(6), pages 1-18, March.
    14. Aqsa Naeem & Naveed Ul Hassan & Chau Yuen & S. M. Muyeen, 2019. "Maximizing the Economic Benefits of a Grid-Tied Microgrid Using Solar-Wind Complementarity," Energies, MDPI, vol. 12(3), pages 1-22, January.
    15. Gonggui Chen & Zhengmei Lu & Zhizhong Zhang, 2018. "Improved Krill Herd Algorithm with Novel Constraint Handling Method for Solving Optimal Power Flow Problems," Energies, MDPI, vol. 11(1), pages 1-27, January.

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