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An Integrated Start-Up Method for Pumped Storage Units Based on a Novel Artificial Sheep Algorithm

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  • Zanbin Wang

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

  • Chaoshun Li

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

  • Xinjie Lai

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

  • Nan Zhang

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

  • Yanhe Xu

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

  • Jinjiao Hou

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

Abstract

Pumped storage units (PSUs) are an important storage tool for power systems containing large-scale renewable energy, and the merit of rapid start-up enable PSUs to modulate and stabilize the power system. In this paper, PSU start-up strategies have been studied and a new integrated start-up method has been proposed for the purpose of achieving swift and smooth start-up. A two-phase closed-loop startup strategy, composed of switching Proportion Integration (PI) and Proportion Integration Differentiation (PID) controller is designed, and an integrated optimization scheme is proposed for a synchronous optimization of the parameters in the strategy. To enhance the optimization performance, a novel meta-heuristic called Artificial Sheep Algorithm (ASA) is proposed and applied to solve the optimization task after a sufficient verification with seven popular meta-heuristic algorithms and 13 typical benchmark functions. Simulation model has been built for a China’s PSU and comparative experiments are conducted to evaluate the proposed integrated method. Results show that the start-up performance could be significantly improved on both indices on overshoot and start-up, and up to 34%-time consumption has been reduced under different working condition. The significant improvements on start-up of PSU is interesting and meaning for further application on real unit.

Suggested Citation

  • Zanbin Wang & Chaoshun Li & Xinjie Lai & Nan Zhang & Yanhe Xu & Jinjiao Hou, 2018. "An Integrated Start-Up Method for Pumped Storage Units Based on a Novel Artificial Sheep Algorithm," Energies, MDPI, vol. 11(1), pages 1-29, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:151-:d:125940
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    References listed on IDEAS

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    6. Wencheng Guo, 2018. "Nonlinear Disturbance Decoupling Control for Hydro-Turbine Governing System with Sloping Ceiling Tailrace Tunnel Based on Differential Geometry Theory," Energies, MDPI, vol. 11(12), pages 1-21, November.
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    8. Lai, Xinjie & Li, Chaoshun & Zhou, Jianzhong & Zhang, Nan, 2019. "Multi-objective optimization of the closure law of guide vanes for pumped storage units," Renewable Energy, Elsevier, vol. 139(C), pages 302-312.
    9. Tan Ding & Li Chang & Chaoshun Li & Chen Feng & Nan Zhang, 2018. "A Mixed-Strategy-Based Whale Optimization Algorithm for Parameter Identification of Hydraulic Turbine Governing Systems with a Delayed Water Hammer Effect," Energies, MDPI, vol. 11(9), pages 1-29, September.
    10. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
    11. Li, Chaoshun & Wang, Wenxiao & Chen, Deshu, 2019. "Multi-objective complementary scheduling of hydro-thermal-RE power system via a multi-objective hybrid grey wolf optimizer," Energy, Elsevier, vol. 171(C), pages 241-255.
    12. Jinjiao Hou & Chaoshun Li & Ziqin Tian & Yanhe Xu & Xinjie Lai & Nan Zhang & Taoping Zheng & Wei Wu, 2018. "Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units," Energies, MDPI, vol. 11(5), pages 1-19, May.
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