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An Improved Equilibrium Optimizer for Solving Optimal Power Flow Problem

Author

Listed:
  • Zhouxin Lan

    (College of Big Data & Information Engineering, Guizhou University, Guiyang 550025, China)

  • Qing He

    (College of Big Data & Information Engineering, Guizhou University, Guiyang 550025, China
    Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China)

  • Hongzan Jiao

    (Department of Urban Planning, School of Urban Design, Wuhan University, Wuhan 430072, China
    Engineering Research Center of Human Settlements and Environment of Hubei Province, Wuhan 430072, China)

  • Liu Yang

    (College of Architecture and Urban Planning, Guizhou University, Guiyang 550025, China)

Abstract

With the rapid development of the economy, the quality of power systems has assumed an increasingly prominent influence on people’s daily lives. In this paper, an improved equilibrium optimizer (IEO) is proposed to solve the optimal power flow (OPF) problem. The algorithm uses the chaotic equilibrium pool to enhance the information interaction between individuals. In addition, a nonlinear dynamic generation mechanism is introduced to balance the global search and local development capabilities. At the same time, the improved algorithm uses the golden sine strategy to update the individual position and enhance the ability of the algorithm to jump out of local optimums. Sixteen benchmark test functions, Wilcoxon rank sum test and 30 CEC2014 complex test function optimization results show that the improved algorithm has better global searching ability than the basic equilibrium optimizer, as well as faster convergence and a more accurate solution than other improved equilibrium optimizers and metaheuristic algorithms. Finally, the improved algorithm is applied to the standard IEEE 30-bus test systems for different objectives. The obtained results demonstrate that the improved algorithm has better solutions than other algorithms in the literature for solving the optimal power flow problem.

Suggested Citation

  • Zhouxin Lan & Qing He & Hongzan Jiao & Liu Yang, 2022. "An Improved Equilibrium Optimizer for Solving Optimal Power Flow Problem," Sustainability, MDPI, vol. 14(9), pages 1-27, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:9:p:4992-:d:798839
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    References listed on IDEAS

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    1. Sirote Khunkitti & Apirat Siritaratiwat & Suttichai Premrudeepreechacharn, 2021. "Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm," Sustainability, MDPI, vol. 13(13), pages 1-21, July.
    2. Abdullah Khan & Hashim Hizam & Noor Izzri Abdul-Wahab & Mohammad Lutfi Othman, 2020. "Solution of Optimal Power Flow Using Non-Dominated Sorting Multi Objective Based Hybrid Firefly and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 13(16), pages 1-24, August.
    3. 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.
    4. Mohammad Zohrul Islam & Mohammad Lutfi Othman & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy & Saifur Rahman Opu & Abinaya Inbamani & Vishalakshi Annamalai, 2021. "Marine predators algorithm for solving single-objective optimal power flow," PLOS ONE, Public Library of Science, vol. 16(8), pages 1-27, August.
    5. Mohammad Zohrul Islam & Noor Izzri Abdul Wahab & Veerapandiyan Veerasamy & Hashim Hizam & Nashiren Farzilah Mailah & Josep M. Guerrero & Mohamad Nasrun Mohd Nasir, 2020. "A Harris Hawks Optimization Based Single- and Multi-Objective Optimal Power Flow Considering Environmental Emission," Sustainability, MDPI, vol. 12(13), pages 1-25, June.
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    1. Qing He & Zhouxin Lan & Damin Zhang & Liu Yang & Shihang Luo, 2022. "Improved Marine Predator Algorithm for Wireless Sensor Network Coverage Optimization Problem," Sustainability, MDPI, vol. 14(16), pages 1-19, August.

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