IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0235668.html
   My bibliography  Save this article

Optimal power flow using hybrid firefly and particle swarm optimization algorithm

Author

Listed:
  • Abdullah Khan
  • Hashim Hizam
  • Noor Izzri bin Abdul Wahab
  • Mohammad Lutfi Othman

Abstract

In this paper, a novel, effective meta-heuristic, population-based Hybrid Firefly Particle Swarm Optimization (HFPSO) algorithm is applied to solve different non-linear and convex optimal power flow (OPF) problems. The HFPSO algorithm is a hybridization of the Firefly Optimization (FFO) and the Particle Swarm Optimization (PSO) technique, to enhance the exploration, exploitation strategies, and to speed up the convergence rate. In this work, five objective functions of OPF problems are studied to prove the strength of the proposed method: total generation cost minimization, voltage profile improvement, voltage stability enhancement, the transmission lines active power loss reductions, and the transmission lines reactive power loss reductions. The particular fitness function is chosen as a single objective based on control parameters. The proposed HFPSO technique is coded using MATLAB software and its effectiveness is tested on the standard IEEE 30-bus test system. The obtained results of the proposed algorithm are compared to simulated results of the original Particle Swarm Optimization (PSO) method and the present state-of-the-art optimization techniques. The comparison of optimum solutions reveals that the recommended method can generate optimum, feasible, global solutions with fast convergence and can also deal with the challenges and complexities of various OPF problems.

Suggested Citation

  • Abdullah Khan & Hashim Hizam & Noor Izzri bin Abdul Wahab & Mohammad Lutfi Othman, 2020. "Optimal power flow using hybrid firefly and particle swarm optimization algorithm," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-21, August.
    • Handle: RePEc:plo:pone00:0235668
    DOI: 10.1371/journal.pone.0235668
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0235668
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0235668&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0235668?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "A Slime Mould Algorithm Programming for Solving Single and Multi-Objective Optimal Power Flow Problems with Pareto Front Approach: A Case Study of the Iraqi Super Grid High Voltage," Energies, MDPI, vol. 15(20), pages 1-33, October.
    2. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization," Energies, MDPI, vol. 15(22), pages 1-31, November.
    3. Ijaz Ahmed & Um-E-Habiba Alvi & Abdul Basit & Tayyaba Khursheed & Alwena Alvi & Keum-Shik Hong & Muhammad Rehan, 2022. "A novel hybrid soft computing optimization framework for dynamic economic dispatch problem of complex non-convex contiguous constrained machines," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-32, January.
    4. Olanrewaju Lasabi & Andrew Swanson & Leigh Jarvis & Anuoluwapo Aluko & Arman Goudarzi, 2024. "Coordinated Hybrid Approach Based on Firefly Algorithm and Particle Swarm Optimization for Distributed Secondary Control and Stability Analysis of Direct Current Microgrids," Sustainability, MDPI, vol. 16(3), pages 1-28, January.
    5. Zhang, Xiao & Wu, Zhi & Sun, Qirun & Gu, Wei & Zheng, Shu & Zhao, Jingtao, 2024. "Application and progress of artificial intelligence technology in the field of distribution network voltage Control：A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    6. Brayan A. Atoccsa & David W. Puma & Daygord Mendoza & Estefany Urday & Cristhian Ronceros & Modesto T. Palma, 2024. "Optimization of Ampacity in High-Voltage Underground Cables with Thermal Backfill Using Dynamic PSO and Adaptive Strategies," Energies, MDPI, vol. 17(5), pages 1-19, February.
    7. Mohamed S. Hashish & Hany M. Hasanien & Haoran Ji & Abdulaziz Alkuhayli & Mohammed Alharbi & Tlenshiyeva Akmaral & Rania A. Turky & Francisco Jurado & Ahmed O. Badr, 2023. "Monte Carlo Simulation and a Clustering Technique for Solving the Probabilistic Optimal Power Flow Problem for Hybrid Renewable Energy Systems," Sustainability, MDPI, vol. 15(1), pages 1-25, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Hatem Diab & Mahmoud Abdelsalam & Alaa Abdelbary, 2021. "A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques," Sustainability, MDPI, vol. 13(9), pages 1-25, April.
    3. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    4. Lukas Kriechbaum & Philipp Gradl & Romeo Reichenhauser & Thomas Kienberger, 2020. "Modelling Grid Constraints in a Multi-Energy Municipal Energy System Using Cumulative Exergy Consumption Minimisation," Energies, MDPI, vol. 13(15), pages 1-23, July.
    5. Nouha Dkhili & David Salas & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2021. "Innovative Application of Model-Based Predictive Control for Low-Voltage Power Distribution Grids with Significant Distributed Generation," Energies, MDPI, vol. 14(6), pages 1-28, March.
    6. Marcel Sarstedt & Leonard Kluß & Johannes Gerster & Tobias Meldau & Lutz Hofmann, 2021. "Survey and Comparison of Optimization-Based Aggregation Methods for the Determination of the Flexibility Potentials at Vertical System Interconnections," Energies, MDPI, vol. 14(3), pages 1-27, January.
    7. Konstantinos Kotsalos & Ismael Miranda & Nuno Silva & Helder Leite, 2019. "A Horizon Optimization Control Framework for the Coordinated Operation of Multiple Distributed Energy Resources in Low Voltage Distribution Networks," Energies, MDPI, vol. 12(6), pages 1-27, March.
    8. Igyso Zafeiratou & Ionela Prodan & Laurent Lefévre, 2021. "A Hierarchical Control Approach for Power Loss Minimization and Optimal Power Flow within a Meshed DC Microgrid," Energies, MDPI, vol. 14(16), pages 1-27, August.
    9. Diego Larrahondo & Ricardo Moreno & Harold R. Chamorro & Francisco Gonzalez-Longatt, 2021. "Comparative Performance of Multi-Period ACOPF and Multi-Period DCOPF under High Integration of Wind Power," Energies, MDPI, vol. 14(15), pages 1-15, July.
    10. Skolfield, J. Kyle & Escobedo, Adolfo R., 2022. "Operations research in optimal power flow: A guide to recent and emerging methodologies and applications," European Journal of Operational Research, Elsevier, vol. 300(2), pages 387-404.
    11. M. Rambabu & G. V. Nagesh Kumar & S. Sivanagaraju, 2019. "Optimal Power Flow of Integrated Renewable Energy System using a Thyristor Controlled SeriesCompensator and a Grey-Wolf Algorithm," Energies, MDPI, vol. 12(11), pages 1-18, June.
    12. Ottenburger, Sadeeb Simon & Çakmak, Hüseyin Kemal & Jakob, Wilfried & Blattmann, Andreas & Trybushnyi, Dmytro & Raskob, Wolfgang & Kühnapfel, Uwe & Hagenmeyer, Veit, 2020. "A novel optimization method for urban resilient and fair power distribution preventing critical network states," International Journal of Critical Infrastructure Protection, Elsevier, vol. 29(C).
    13. Li, Rui & Wang, Wei & Wu, Xuezhi & Tang, Fen & Chen, Zhe, 2019. "Cooperative planning model of renewable energy sources and energy storage units in active distribution systems: A bi-level model and Pareto analysis," Energy, Elsevier, vol. 168(C), pages 30-42.
    14. Pranjal Pragya Verma & Mohammad Reza Hesamzadeh & Steffen Rebennack & Derek Bunn & K. Shanti Swarup & Dipti Srinivasan, 2024. "Optimal investment by large consumers in an electricity market with generator market power," Computational Management Science, Springer, vol. 21(1), pages 1-56, June.
    15. Zhang, Jingrui & Wang, Silu & Tang, Qinghui & Zhou, Yulu & Zeng, Tao, 2019. "An improved NSGA-III integrating adaptive elimination strategy to solution of many-objective optimal power flow problems," Energy, Elsevier, vol. 172(C), pages 945-957.
    16. Zhongyang Zhao & Caisheng Wang & Masoud H. Nazari, 2024. "Revenue Analysis of Stationary and Transportable Battery Storage for Power Systems: A Market Participant Perspective," Sustainability, MDPI, vol. 16(6), pages 1-16, March.
    17. Murtadha Al-Kaabi & Virgil Dumbrava & Mircea Eremia, 2022. "Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization," Energies, MDPI, vol. 15(22), pages 1-31, November.
    18. Nouha Dkhili & Julien Eynard & Stéphane Thil & Stéphane Grieu, 2021. "Resilient Predictive Control Coupled with a Worst-Case Scenario Approach for a Distributed-Generation-Rich Power Distribution Grid," Clean Technol., MDPI, vol. 3(3), pages 1-27, August.
    19. 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.
    20. Tianyu Cui & Francesco Caravelli & Cozmin Ududec, 2017. "Correlations and Clustering in Wholesale Electricity Markets," Papers 1710.11184, arXiv.org, revised Nov 2017.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0235668. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.