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Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty

Author

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  • Mohamed S. Hashish

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

  • Hany M. Hasanien

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt
    Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Zia Ullah

    (School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Abdulaziz Alkuhayli

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Ahmed O. Badr

    (Electrical Power and Machines Department, Faculty of Engineering, Ain Shams University, Cairo 11517, Egypt)

Abstract

In this study, the Giant Trevally Optimizer (GTO) is employed to solve the probabilistic optimum power flow (P-OPF) issue, considering Renewable Energy Source (RES) uncertainties, achieving notable cost reduction. The objective function is established to minimize the overall generation cost, including the RES cost, which significantly surpassing existing solutions. The uncertain nature of the RES is represented through the employment of a Monte Carlo Simulation (MCS), strengthened by the K-means Clustering approach and the Elbow technique. Various cases are investigated, including various combinations of PV systems, WE systems, and both fixed and fluctuating loads. The study demonstrates that while considering the costs of solar, wind, or both might slightly increase the total generation cost, the cumulative generation cost remains significantly less than the scenario that does not consider the cost of RESs. The superior outcomes presented in this research underline the importance of considering RES costs, providing a more accurate representation of real-world system dynamics and enabling more effective decision making.

Suggested Citation

  • Mohamed S. Hashish & Hany M. Hasanien & Zia Ullah & Abdulaziz Alkuhayli & Ahmed O. Badr, 2023. "Giant Trevally Optimization Approach for Probabilistic Optimal Power Flow of Power Systems Including Renewable Energy Systems Uncertainty," Sustainability, MDPI, vol. 15(18), pages 1-27, September.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13283-:d:1232945
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    References listed on IDEAS

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    Cited by:

    1. Othman A. M. Omar & Ahmed O. Badr & Ibrahim Mohamed Diaaeldin, 2023. "Novel Fractional Order and Stochastic Formulations for the Precise Prediction of Commercial Photovoltaic Curves," Mathematics, MDPI, vol. 11(21), pages 1-19, October.
    2. Bozhen Jiang & Qin Wang & Shengyu Wu & Yidi Wang & Gang Lu, 2024. "Advancements and Future Directions in the Application of Machine Learning to AC Optimal Power Flow: A Critical Review," Energies, MDPI, vol. 17(6), pages 1-17, March.

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