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A Multiobjective Artificial-Hummingbird-Algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources

Author

Listed:
  • Umar Waleed

    (Faculty of Electrical Engineering, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi 23460, Pakistan
    These authors contributed equally to this work.)

  • Abdul Haseeb

    (Department of Electrical Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Mansoor Ashraf

    (Department of Electrical Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
    These authors contributed equally to this work.)

  • Faisal Siddiq

    (Department of Electrical Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Rafiq

    (Department of Electrical Engineering, University of Engineering and Technology, Taxila 47050, Pakistan
    These authors contributed equally to this work.)

  • Muhammad Shafique

    (Department of Civil and Environmental Engineering, Brunel University London, Uxbridge UB8 3PH, UK
    These authors contributed equally to this work.)

Abstract

This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:9250-:d:995384
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    References listed on IDEAS

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    1. Mohamed Ebeed & Ayman Alhejji & Salah Kamel & Francisco Jurado, 2020. "Solving the Optimal Reactive Power Dispatch Using Marine Predators Algorithm Considering the Uncertainties in Load and Wind-Solar Generation Systems," Energies, MDPI, vol. 13(17), pages 1-19, August.
    2. 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.
    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. Bin Zhou & Xiaodong Shen & Caimei Pan & Yuanbao Bai & Tian Wu, 2022. "Optimal Reactive Power Dispatch under Transmission and Distribution Coordination Based on an Accelerated Augmented Lagrangian Algorithm," Energies, MDPI, vol. 15(11), pages 1-22, May.
    5. Ahmed M. Abd-El Wahab & Salah Kamel & Mohamed H. Hassan & Mohamed I. Mosaad & Tarek A. AbdulFattah, 2022. "Optimal Reactive Power Dispatch Using a Chaotic Turbulent Flow of Water-Based Optimization Algorithm," Mathematics, MDPI, vol. 10(3), pages 1-26, January.
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    Cited by:

    1. Mauro Jurado & Eduardo Salazar & Mauricio Samper & Rodolfo Rosés & Diego Ojeda Esteybar, 2023. "Day-Ahead Operational Planning for DisCos Based on Demand Response Flexibility and Volt/Var Control," Energies, MDPI, vol. 16(20), pages 1-20, October.
    2. Abdul Haseeb & Umar Waleed & Muhammad Mansoor Ashraf & Faisal Siddiq & Muhammad Rafiq & Muhammad Shafique, 2023. "Hybrid Weighted Least Square Multi-Verse Optimizer (WLS–MVO) Framework for Real-Time Estimation of Harmonics in Non-Linear Loads," Energies, MDPI, vol. 16(2), pages 1-15, January.

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