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OPF of Modern Power Systems Comprising Renewable Energy Sources Using Improved CHGS Optimization Algorithm

Author

Listed:
  • Mohamed A. M. Shaheen

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Hany M. Hasanien

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

  • Rania A. Turky

    (Electrical Engineering Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Martin Ćalasan

    (Faculty of Electrical Engineering, University of Montenegro, Dzordza Vasingtona, 81000 Podgorica, Montenegro)

  • Ahmed F. Zobaa

    (Electronic and Electrical Engineering Department, Brunel University London, Uxbridge UB83 PH, UK)

  • Shady H. E. Abdel Aleem

    (Electrical Engineering Department, Valley Higher Institute of Engineering and Technology, Science Valley Academy, Qalyubia 44971, Egypt)

Abstract

This article introduces an application of the recently developed hunger games search (HGS) optimization algorithm. The HGS is combined with chaotic maps to propose a new Chaotic Hunger Games search (CHGS). It is applied to solve the optimal power flow (OPF) problem. The OPF is solved to minimize the generation costs while satisfying the systems’ constraints. Moreover, the article presents optimal siting for mixed renewable energy sources, photovoltaics, and wind farms. Furthermore, the effect of adding renewable energy sources on the overall generation costs value is investigated. The exploration field of the optimization problem is the active output power of each generator in each studied system. The CHGS also obtains the best candidate design variables, which corresponds to the minimum possible cost function value. The robustness of the introduced CHGS algorithm is verified by performing the simulation 20 independent times for two standard IEEE systems—IEEE 57-bus and 118-bus systems. The results obtained are presented and analyzed. The CHGS-based OPF was found to be competitive and superior to other optimization algorithms applied to solve the same optimization problem in the literature. The contribution of this article is to test the improvement done to the proposed method when applied to the OPF problem, as well as the study of the addition of renewable energy sources on the introduced objective function.

Suggested Citation

  • Mohamed A. M. Shaheen & Hany M. Hasanien & Rania A. Turky & Martin Ćalasan & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2021. "OPF of Modern Power Systems Comprising Renewable Energy Sources Using Improved CHGS Optimization Algorithm," Energies, MDPI, vol. 14(21), pages 1-21, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:6962-:d:662845
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    References listed on IDEAS

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    1. Okpokparoro, Salem & Sriramula, Srinivas, 2021. "Uncertainty modeling in reliability analysis of floating wind turbine support structures," Renewable Energy, Elsevier, vol. 165(P1), pages 88-108.
    2. 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.
    3. Iris, Çağatay & Lam, Jasmine Siu Lee, 2021. "Optimal energy management and operations planning in seaports with smart grid while harnessing renewable energy under uncertainty," Omega, Elsevier, vol. 103(C).
    4. Li, Yang & Li, Yahui & Li, Guoqing & Zhao, Dongbo & Chen, Chen, 2018. "Two-stage multi-objective OPF for AC/DC grids with VSC-HVDC: Incorporating decisions analysis into optimization process," Energy, Elsevier, vol. 147(C), pages 286-296.
    5. Reddy, S. Surender, 2017. "Optimal scheduling of thermal-wind-solar power system with storage," Renewable Energy, Elsevier, vol. 101(C), pages 1357-1368.
    6. Seleem, Sameh I. & Hasanien, Hany M. & El-Fergany, Attia A., 2021. "Equilibrium optimizer for parameter extraction of a fuel cell dynamic model," Renewable Energy, Elsevier, vol. 169(C), pages 117-128.
    7. Li, Shuijia & Gong, Wenyin & Wang, Ling & Yan, Xuesong & Hu, Chengyu, 2020. "Optimal power flow by means of improved adaptive differential evolution," Energy, Elsevier, vol. 198(C).
    8. Mohamed A. M. Shaheen & Dalia Yousri & Ahmed Fathy & Hany M. Hasanien & Abdulaziz Alkuhayli & S. M. Muyeen, 2020. "A Novel Application of Improved Marine Predators Algorithm and Particle Swarm Optimization for Solving the ORPD Problem," Energies, MDPI, vol. 13(21), pages 1-23, October.
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    Cited by:

    1. Ćalasan, Martin & Abdel Aleem, Shady H.E. & Hasanien, Hany M. & Alaas, Zuhair M. & Ali, Ziad M., 2023. "An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative Lambert W function," Energy, Elsevier, vol. 264(C).
    2. Mahmoud El-Dabah & Mohamed A. Ebrahim & Ragab A. El-Sehiemy & Z. Alaas & M. M. Ramadan, 2022. "A Modified Whale Optimizer for Single- and Multi-Objective OPF Frameworks," Energies, MDPI, vol. 15(7), pages 1-18, March.

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