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Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm

Author

Listed:
  • Mahmoud A. Ali

    (Department of Electrical Engineering, Faculty of Engineering, Azhar University, Qena 83513, Egypt)

  • Salah Kamel

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Mohamed H. Hassan

    (Department of Electrical Engineering, Faculty of Engineering, Aswan University, Aswan 81542, Egypt)

  • Emad M. Ahmed

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

  • Mohana Alanazi

    (Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia)

Abstract

Modern electrical power systems are becoming increasingly complex and are expanding at an accelerating pace. The power system’s transmission lines are under more strain than ever before. As a result, the power system is experiencing a wide range of issues, including rising power losses, voltage instability, line overloads, and so on. Losses can be minimized and the voltage profile can be improved when energy resources are installed on appropriate buses to optimize real and reactive power. This is especially true in densely congested networks. Optimal power flow (OPF) is a basic tool for the secure and economic operation of power systems. It is a mathematical tool used to find the instantaneous optimal operation of a power system under constraints meeting operation feasibility and security. In this study, a new application algorithm named white shark optimizer (WSO) is proposed to solve the optimal power flow (OPF) problems based on a single objective and considering the minimization of the generation cost. The WSO is used to find the optimal solution for an upgraded power system that includes both traditional thermal power units (TPG) and renewable energy units, including wind (WPG) and solar photovoltaic generators (SPG). Although renewable energy sources such as wind and solar energy represent environmentally friendly sources in line with the United Nations sustainable development goals (UN SDG), they appear as a major challenge for power flow systems due to the problems of discontinuous energy production. For overcoming this problem, probability density functions of Weibull and Lognormal (PDF) have been used to aid in forecasting uncertain output powers from WPG and SPG, respectively. Testing on modified IEEE-30 buses’ systems is used to evaluate the proposed method’s performance. The results of the suggested WSO algorithm are compared to the results of the Northern Goshawk Optimizer (NGO) and two other optimization methods to investigate its effectiveness. The simulation results reveal that WSO is more effective at finding the best solution to the OPF problem when considering total power cost minimization and solution convergence. Moreover, the results of the proposed technique are compared to the other existing method described in the literature, with the results indicating that the suggested method can find better optimal solutions, employ less generated solutions, and save computation time.

Suggested Citation

  • Mahmoud A. Ali & Salah Kamel & Mohamed H. Hassan & Emad M. Ahmed & Mohana Alanazi, 2022. "Optimal Power Flow Solution of Power Systems with Renewable Energy Sources Using White Sharks Algorithm," Sustainability, MDPI, vol. 14(10), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:10:p:6049-:d:816940
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    References listed on IDEAS

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    1. 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.
    2. Panda, Ambarish & Tripathy, M., 2015. "Security constrained optimal power flow solution of wind-thermal generation system using modified bacteria foraging algorithm," Energy, Elsevier, vol. 93(P1), pages 816-827.
    3. Ghasemi, Mojtaba & Ghavidel, Sahand & Ghanbarian, Mohammad Mehdi & Gharibzadeh, Masihallah & Azizi Vahed, Ali, 2014. "Multi-objective optimal power flow considering the cost, emission, voltage deviation and power losses using multi-objective modified imperialist competitive algorithm," Energy, Elsevier, vol. 78(C), pages 276-289.
    4. 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.
    5. Niknam, Taher & Narimani, Mohammad rasoul & Jabbari, Masoud & Malekpour, Ahmad Reza, 2011. "A modified shuffle frog leaping algorithm for multi-objective optimal power flow," Energy, Elsevier, vol. 36(11), pages 6420-6432.
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    Cited by:

    1. Dong Yu & Shan Gao & Xin Zhao & Yu Liu & Sicheng Wang & Tiancheng E. Song, 2023. "Alternating Iterative Power-Flow Algorithm for Hybrid AC–DC Power Grids Incorporating LCCs and VSCs," Sustainability, MDPI, vol. 15(5), pages 1-22, March.
    2. Ehab S. Ali & Sahar. M. Abd Elazim & Sultan H. Hakmi & Mohamed I. Mosaad, 2023. "Optimal Allocation and Size of Renewable Energy Sources as Distributed Generations Using Shark Optimization Algorithm in Radial Distribution Systems," Energies, MDPI, vol. 16(10), pages 1-27, May.
    3. Yude Yang & Yuying Luo & Lizhen Yang, 2022. "Small-Signal Stability Constrained Optimal Power Flow Model Based on BP Neural Network Algorithm," Sustainability, MDPI, vol. 14(20), pages 1-14, October.
    4. Zhongxi Ou & Yuanyuan Lou & Junzhou Wang & Yixin Li & Kun Yang & Sui Peng & Junjie Tang, 2023. "The Effect of Power Flow Entropy on Available Load Supply Capacity under Stochastic Scenarios with Different Control Coefficients of UPFC," Sustainability, MDPI, vol. 15(8), pages 1-22, April.

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