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Optimal Power Flow Using the Jaya Algorithm

Author

Listed:
  • Warid Warid

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Centre for Advanced Power and Energy Research, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Technical Institute Shatra, Southern Technical University, Foundation of Technical Education, Ministry of Higher Education & Scientific Research, Thi-qar 64001, Iraq)

  • Hashim Hizam

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Centre for Advanced Power and Energy Research, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Norman Mariun

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Centre for Advanced Power and Energy Research, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

  • Noor Izzri Abdul-Wahab

    (Department of Electrical and Electronic Engineering, Faculty of Engineering, Universiti Putra Malaysia, Serdang 43400, Selangor, Malaysia
    Centre for Advanced Power and Energy Research, Faculty of Engineering, University Putra Malaysia, Serdang 43400, Selangor, Malaysia)

Abstract

This paper presents application of a new effective metaheuristic optimization method namely, the Jaya algorithm to deal with different optimum power flow (OPF) problems. Unlike other population-based optimization methods, no algorithm-particular controlling parameters are required for this algorithm. In this work, three goal functions are considered for the OPF solution: generation cost minimization, real power loss reduction, and voltage stability improvement. In addition, the effect of distributed generation (DG) is incorporated into the OPF problem using a modified formulation. For best allocation of DG unit(s), a sensitivity-based procedure is introduced. Simulations are carried out on the modified IEEE 30-bus and IEEE 118-bus networks to determine the effectiveness of the Jaya algorithm. The single objective optimization cases are performed both with and without DG. For all considered cases, results demonstrate that Jaya algorithm can produce an optimum solution with rapid convergence. Statistical analysis is also carried out to check the reliability of the Jaya algorithm. The optimal solution obtained by the Jaya algorithm is compared with different stochastic algorithms, and demonstrably outperforms them in terms of solution optimality and solution feasibility, proving its effectiveness and potential. Notably, optimal placement of DGs results in even better solutions.

Suggested Citation

  • Warid Warid & Hashim Hizam & Norman Mariun & Noor Izzri Abdul-Wahab, 2016. "Optimal Power Flow Using the Jaya Algorithm," Energies, MDPI, vol. 9(9), pages 1-18, August.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:9:p:678-:d:76674
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    References listed on IDEAS

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    1. Stephen Frank & Steffen Rebennack, 2016. "An introduction to optimal power flow: Theory, formulation, and examples," IISE Transactions, Taylor & Francis Journals, vol. 48(12), pages 1172-1197, December.
    2. Younes, Mimoun & Khodja, Fouad & Kherfane, Riad Lakhdar, 2014. "Multi-objective economic emission dispatch solution using hybrid FFA (firefly algorithm) and considering wind power penetration," Energy, Elsevier, vol. 67(C), pages 595-606.
    3. Xuanhu He & Wei Wang & Jiuchun Jiang & Lijie Xu, 2015. "An Improved Artificial Bee Colony Algorithm and Its Application to Multi-Objective Optimal Power Flow," Energies, MDPI, vol. 8(4), pages 1-26, March.
    4. Eleonora Riva Sanseverino & Maria Luisa Di Silvestre & Romina Badalamenti & Ninh Quang Nguyen & Josep Maria Guerrero & Lexuan Meng, 2015. "Optimal Power Flow in Islanded Microgrids Using a Simple Distributed Algorithm," Energies, MDPI, vol. 8(10), pages 1-22, October.
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