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Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

Author

Listed:
  • Shuai Liu

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Zhong-Kai Feng

    (School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Wen-Jing Niu

    (Bureau of Hydrology, ChangJiang Water Resources Commission, Wuhan 430010, China)

  • Hai-Rong Zhang

    (Department of Water Resources Management, China Yangtze Power Company Limited, Yichang 443000, China)

  • Zhen-Guo Song

    (China Ship Development and Design Center, Wuhan 430064, China)

Abstract

In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

Suggested Citation

  • Shuai Liu & Zhong-Kai Feng & Wen-Jing Niu & Hai-Rong Zhang & Zhen-Guo Song, 2019. "Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation," Energies, MDPI, vol. 12(11), pages 1-24, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2189-:d:238307
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    References listed on IDEAS

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    2. Dorian O. Sidea & Andrei M. Tudose & Irina I. Picioroaga & Constantin Bulac, 2022. "Two-Stage Optimal Active-Reactive Power Coordination for Microgrids with High Renewable Sources Penetration and Electrical Vehicles Based on Improved Sine−Cosine Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-24, December.

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