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Multi-Objective Optimal Power Flow Problems Based on Slime Mould Algorithm

Author

Listed:
  • Sirote Khunkitti

    (Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

  • Apirat Siritaratiwat

    (Department of Electrical Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen 40002, Thailand)

  • Suttichai Premrudeepreechacharn

    (Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand)

Abstract

Solving the optimal power flow problems (OPF) is an important step in optimally dispatching the generation with the considered objective functions. A single-objective function is inadequate for modern power systems, required high-performance generation, so the problem becomes multi-objective optimal power flow (MOOPF). Although the MOOPF problem has been widely solved by many algorithms, new solutions are still required to obtain better performance of generation. Slime mould algorithm (SMA) is a recently proposed metaheuristic algorithm that has been applied to solve several optimization problems in different fields, except the MOOPF problem, while it outperforms various algorithms. Thus, this paper proposes solving MOOPF problems based on SMA considering cost, emission, and transmission line loss as part of the objective functions in a power system. The IEEE 30-, 57-, and 118-bus systems are used to investigate the performance of the SMA on solving MOOPF problems. The objective values generated by SMA are compared with those of other algorithms in the literature. The simulation results show that SMA provides better solutions than many other algorithms in the literature, and the Pareto fronts presenting multi-objective solutions can be efficiently obtained.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7448-:d:587718
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    References listed on IDEAS

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    Cited by:

    1. Lenin Kanagasabai, 2023. "Real power loss reduction by extreme learning machine based Panthera leo, chaotic based Jungle search and Quantum based Chipmunk search optimization algorithms," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 55-78, March.
    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. Mohamed Farhat & Salah Kamel & Ahmed M. Atallah & Mohamed H. Hassan & Ahmed M. Agwa, 2022. "ESMA-OPF: Enhanced Slime Mould Algorithm for Solving Optimal Power Flow Problem," Sustainability, MDPI, vol. 14(4), pages 1-33, February.
    4. Muhammad Riaz & Aamir Hanif & Haris Masood & Muhammad Attique Khan & Kamran Afaq & Byeong-Gwon Kang & Yunyoung Nam, 2021. "An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-12, December.
    5. Lenin Kanagasabai, 2022. "Real power loss dwindling and voltage reliability enrichment by gradient based optimization algorithm," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(5), pages 2727-2742, October.
    6. 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).
    7. 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.
    8. 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.
    9. Slim Abid & Ali M. El-Rifaie & Mostafa Elshahed & Ahmed R. Ginidi & Abdullah M. Shaheen & Ghareeb Moustafa & Mohamed A. Tolba, 2023. "Development of Slime Mold Optimizer with Application for Tuning Cascaded PD-PI Controller to Enhance Frequency Stability in Power Systems," Mathematics, MDPI, vol. 11(8), pages 1-32, April.
    10. 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.

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