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Developed Gorilla Troops Technique for Optimal Power Flow Problem in Electrical Power Systems

Author

Listed:
  • Abdullah Shaheen

    (Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Ahmed Ginidi

    (Department of Electrical Engineering, Faculty of Engineering, Suez University, Suez 43533, Egypt)

  • Ragab El-Sehiemy

    (Department of Electrical Engineering, Faculty of Engineering, Kafrelsheikh University, Kafrelsheikh 33516, Egypt)

  • Abdallah Elsayed

    (Department of Electrical Engineering, Faculty of Engineering, Damietta University, Damietta 34517, Egypt)

  • Ehab Elattar

    (Department of Electrical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Hassen T. Dorrah

    (Department of Electrical Engineering, Cairo University, Giza 12613, Egypt)

Abstract

This paper presents a developed solution based on gorilla troops optimization technique for OPFP in EPSs. The GTOT is motivated by gorillas’ group behaviors in which several methods are replicated, such as migration to an unfamiliar location, traveling to other gorillas, migration toward a specific spot, accompanying the silverback, and competing for adult females. The multi-dimension OPFP in EPSs is examined in this article with numerous optimizing objectives of fuel cost, power losses, and harmful pollutants. The system’s power demand and transmission losses must be met as well. The developed GTOT’s evaluation is conducted using an IEEE standard 30-bus EPS and practical EPS from Egypt. The created GTOT is employed in numerous evaluations and statistical analyses using many modern methods such as CST, GWT, ISHT, NBT, and SST. When compared to other similar approaches in the literature, the simulated results demonstrate the GTOT’s solution efficiency and robustness.

Suggested Citation

  • Abdullah Shaheen & Ahmed Ginidi & Ragab El-Sehiemy & Abdallah Elsayed & Ehab Elattar & Hassen T. Dorrah, 2022. "Developed Gorilla Troops Technique for Optimal Power Flow Problem in Electrical Power Systems," Mathematics, MDPI, vol. 10(10), pages 1-29, May.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:10:p:1636-:d:813289
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    References listed on IDEAS

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    1. Ragab El-Sehiemy & Abdallah Elsayed & Abdullah Shaheen & Ehab Elattar & Ahmed Ginidi, 2021. "Scheduling of Generation Stations, OLTC Substation Transformers and VAR Sources for Sustainable Power System Operation Using SNS Optimizer," Sustainability, MDPI, vol. 13(21), pages 1-24, October.
    2. Nguyen, Thang Trung, 2019. "A high performance social spider optimization algorithm for optimal power flow solution with single objective optimization," Energy, Elsevier, vol. 171(C), pages 218-240.
    3. 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.
    4. Horng, Shih-Cheng & Lin, Shieh-Shing, 2019. "Bat algorithm assisted by ordinal optimization for solving discrete probabilistic bicriteria optimization problems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 166(C), pages 346-364.
    5. Ahmed Ginidi & Sherif M. Ghoneim & Abdallah Elsayed & Ragab El-Sehiemy & Abdullah Shaheen & Attia El-Fergany, 2021. "Gorilla Troops Optimizer for Electrically Based Single and Double-Diode Models of Solar Photovoltaic Systems," Sustainability, MDPI, vol. 13(16), pages 1-28, August.
    6. Mostafa Abdo & Salah Kamel & Mohamed Ebeed & Juan Yu & Francisco Jurado, 2018. "Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer," Energies, MDPI, vol. 11(7), pages 1-16, June.
    7. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    8. Shaheen, Abdullah M. & El-Sehiemy, Ragab A. & Alharthi, Mosleh M. & Ghoneim, Sherif S.M. & Ginidi, Ahmed R., 2021. "Multi-objective jellyfish search optimizer for efficient power system operation based on multi-dimensional OPF framework," Energy, Elsevier, vol. 237(C).
    9. Elattar, Ehab E. & ElSayed, Salah K., 2019. "Modified JAYA algorithm for optimal power flow incorporating renewable energy sources considering the cost, emission, power loss and voltage profile improvement," Energy, Elsevier, vol. 178(C), pages 598-609.
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    Cited by:

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    3. Jinhua You & Heming Jia & Di Wu & Honghua Rao & Changsheng Wen & Qingxin Liu & Laith Abualigah, 2023. "Modified Artificial Gorilla Troop Optimization Algorithm for Solving Constrained Engineering Optimization Problems," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    4. Shahenda Sarhan & Abdullah Shaheen & Ragab El-Sehiemy & Mona Gafar, 2023. "An Augmented Social Network Search Algorithm for Optimal Reactive Power Dispatch Problem," Mathematics, MDPI, vol. 11(5), pages 1-42, March.
    5. Kumeshan Reddy & Akshay Kumar Saha, 2022. "An Investigation into the Utilization of Swarm Intelligence for the Design of Dual Vector and Proportional–Resonant Controllers for Regulation of Doubly Fed Induction Generators Subject to Unbalanced ," Energies, MDPI, vol. 15(20), pages 1-36, October.
    6. Jaime Pilatásig & Diego Carrión & Manuel Jaramillo, 2022. "Resilience Maximization in Electrical Power Systems through Switching of Power Transmission Lines," Energies, MDPI, vol. 15(21), pages 1-15, November.
    7. Jamal, Raheela & Zhang, Junzhe & Men, Baohui & Khan, Noor Habib & Ebeed, Mohamed & Jamal, Tanzeela & Mohamed, Emad A., 2024. "Chaotic-quasi-oppositional-phasor based multi populations gorilla troop optimizer for optimal power flow solution," Energy, Elsevier, vol. 301(C).
    8. Shahenda Sarhan & Abdullah Mohamed Shaheen & Ragab A. El-Sehiemy & Mona Gafar, 2022. "An Enhanced Slime Mould Optimizer That Uses Chaotic Behavior and an Elitist Group for Solving Engineering Problems," Mathematics, MDPI, vol. 10(12), pages 1-30, June.

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