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Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization

Author

Listed:
  • Tawfiq M. Aljohani

    (Energy Systems Research Laboratory, Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA
    Department of Electrical and Computer Engineering, Taibah University, Medina 42353, Saudi Arabia)

  • Ahmed F. Ebrahim

    (Energy Systems Research Laboratory, Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

  • Osama Mohammed

    (Energy Systems Research Laboratory, Department of Electrical and Computer Engineering, Florida International University, Miami, FL 33174, USA)

Abstract

The optimal reactive power dispatch (ORPD) problem represents a noncontinuous, nonlinear, highly constrained optimization problem that has recently attracted wide research investigation. This paper presents a new hybridization technique for solving the ORPD problem based on the integration of particle swarm optimization (PSO) with artificial physics optimization (APO). This hybridized algorithm is tested and verified on the IEEE 30, IEEE 57, and IEEE 118 bus test systems to solve both single and multiobjective ORPD problems, considering three main aspects. These aspects include active power loss minimization, voltage deviation minimization, and voltage stability improvement. The results prove that the algorithm is effective and displays great consistency and robustness in solving both the single and multiobjective functions while improving the convergence performance of the PSO. It also shows superiority when compared with results obtained from previously reported literature for solving the ORPD problem.

Suggested Citation

  • Tawfiq M. Aljohani & Ahmed F. Ebrahim & Osama Mohammed, 2019. "Single and Multiobjective Optimal Reactive Power Dispatch Based on Hybrid Artificial Physics–Particle Swarm Optimization," Energies, MDPI, vol. 12(12), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:12:p:2333-:d:240899
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    Citations

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    Cited by:

    1. Faraz Bhurt & Aamir Ali & Muhammad U. Keerio & Ghulam Abbas & Zahoor Ahmed & Noor H. Mugheri & Yun-Su Kim, 2023. "Stochastic Multi-Objective Optimal Reactive Power Dispatch with the Integration of Wind and Solar Generation," Energies, MDPI, vol. 16(13), pages 1-22, June.
    2. Lenin Kanagasabai, 2022. "Real power loss reduction by North American sapsucker 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(1), pages 143-153, February.
    3. Salah K. ElSayed & Ehab E. Elattar, 2021. "Slime Mold Algorithm for Optimal Reactive Power Dispatch Combining with Renewable Energy Sources," Sustainability, MDPI, vol. 13(11), pages 1-25, May.
    4. S. N. V. S. K. Chaitanya & R. Ashok Bakkiyaraj & B. Venkateswara Rao, 2023. "Multi objective optimal reactive power dispatch for enrichment of power system behavior using modified ant lion optimizer," 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 133-142, March.
    5. Mahmoud Hemeida & Tomonobu Senjyu & Salem Alkhalaf & Asmaa Fawzy & Mahrous Ahmed & Dina Osheba, 2022. "Reactive Power Management Based Hybrid GAEO," Sustainability, MDPI, vol. 14(11), pages 1-17, June.
    6. Héctor Migallón & Akram Belazi & José-Luis Sánchez-Romero & Héctor Rico & Antonio Jimeno-Morenilla, 2020. "Settings-Free Hybrid Metaheuristic General Optimization Methods," Mathematics, MDPI, vol. 8(7), pages 1-25, July.
    7. Tawfiq M. Aljohani & Ahmed F. Ebrahim & Osama Mohammed, 2020. "Hybrid Microgrid Energy Management and Control Based on Metaheuristic-Driven Vector-Decoupled Algorithm Considering Intermittent Renewable Sources and Electric Vehicles Charging Lot," Energies, MDPI, vol. 13(13), pages 1-19, July.
    8. 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).
    9. Umar Waleed & Abdul Haseeb & Muhammad Mansoor Ashraf & Faisal Siddiq & Muhammad Rafiq & Muhammad Shafique, 2022. "A Multiobjective Artificial-Hummingbird-Algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources," Energies, MDPI, vol. 15(23), pages 1-23, December.
    10. Mini Vishnu & Sunil Kumar T. K., 2020. "An Improved Solution for Reactive Power Dispatch Problem Using Diversity-Enhanced Particle Swarm Optimization," Energies, MDPI, vol. 13(11), pages 1-21, June.

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