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A Multi-Objective Optimal Power Flow Control of Electrical Transmission Networks Using Intelligent Meta-Heuristic Optimization Techniques

Author

Listed:
  • Hatem Diab

    (Electrical Energy Department, Arab Academy for Science and Technology and Maritime Transport, Giza 12212, Egypt)

  • Mahmoud Abdelsalam

    (Electrical Energy Department, Arab Academy for Science and Technology and Maritime Transport, Giza 12212, Egypt)

  • Alaa Abdelbary

    (Applied Sciences Department, Arab Academy for Science and Technology and Maritime Transport, Alexandria 2033, Egypt)

Abstract

Optimal power flow (OPF) is considered one of the most critical challenges that can substantially impact the sustainable performance of power systems. Solving the OPF problem reduces three essential items: operation costs, transmission losses, and voltage drops. An intelligent controller is needed to adjust the power system’s control parameters to solve this problem optimally. However, many constraints must be considered that make the design process of the OPF algorithm exceedingly tricky due to the increased number of limitations and control variables. This paper proposes a multi-objective intelligent control technique based on three different meta-heuristic optimization algorithms: multi-verse optimization (MVO), grasshopper optimization (GOA), and Harris hawks optimization (HHO) to solve the OPF problem. The proposed control techniques were validated by applying them to the IEEE-30 bus system under different operating conditions through MATLAB simulations. The proposed techniques were then compared with the particle swarm optimization (PSO) algorithm, which is very popular in the literature studying how to solving the OPF problem. The obtained results show that the proposed methods are more effective in solving the OPF problem when compared to the commonly used PSO algorithm. The proposed HHO, in particular, shows that it can form a reliable candidate in solving power systems’ optimization problems.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:9:p:4979-:d:545867
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    References listed on IDEAS

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    1. 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.
    2. Yehia Gad & Hatem Diab & Mahmoud Abdelsalam & Yasser Galal, 2020. "Smart Energy Management System of Environmentally Friendly Microgrid Based on Grasshopper Optimization Technique," Energies, MDPI, vol. 13(19), pages 1-22, September.
    3. 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.
    4. Mahmoud Abdelsalam & Hatem Y. Diab, 2019. "Optimal Coordination of DOC Relays Incorporated into a Distributed Generation-Based Micro-Grid Using a Meta-Heuristic MVO Algorithm," Energies, MDPI, vol. 12(21), pages 1-16, October.
    5. 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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    Cited by:

    1. 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.
    2. 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.

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