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Multiple Hydropower Reservoirs Operation by Hyperbolic Grey Wolf Optimizer Based on Elitism Selection and Adaptive Mutation

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Abstract

Multiple hydropower reservoirs operation is an effective measure to rationally allocate the limited water resources under uncertainty. With the rapid expansion of water resources system, it becomes much more difficult for traditional methods to quickly yield the reasonable operational policy. Grey wolf optimizer, inspired by the wolves’ hunting behaviors, is a famous metaheuristic method to resolve engineering optimization problems, but still suffers from the local convergence and search stagnation defects. To alleviate this problem, this study proposes a hybrid grey wolf optimizer (HGWO) where the hyperbolic accelerating strategy is introduced to improve the local search ability; the adaptive mutation strategy is used to diversify the swarm; the elitism selection strategy is used to enhance the convergence speed. The experimental results show that the HGWO method can produce better solutions than its original version in several test functions. Then, the HGWO method is applied to resolve the optimal operation of a real-world hydropower system with the goal of maximizing the total generation benefit. The simulations indicate that the HGWO method produces satisfying scheduling schemes than several control methods in terms of all the statistical indicators. Hence, with the merits of superior search ability, rapid convergence rate and gradient information avoidance, HGWO proves to be a promising alternative optimization tool for the complex multireservoir system operation problem.

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Data of Availability

Due to the strict security requirements from the departments, some or all data, models, or code generated or used in the study are proprietary or confidential in nature.

Funding

This paper is supported by the National Key R&D Program of China (2017YFC0405400), National Natural Science Foundation of China (52009012 and 51709119), Natural Science Foundation of Hubei Province (2020CFB340).

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Authors and Affiliations

  1. Bureau of Hydrology, ChangJiang Water Resources Commission, Wuhan, 430010, China

    Wen-jing Niu, Yu-bin Chen, Yin-shan Xu & Jun Zhang

  2. School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Zhong-kai Feng

  3. China Water Resources Beifang Investigation, Design and Research Co. Ltd. (BIDR), Tianjin, 300222, China

    Shuai Liu

Authors
  1. Wen-jing Niu
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  2. Zhong-kai Feng
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  3. Shuai Liu
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  4. Yu-bin Chen
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  5. Yin-shan Xu
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  6. Jun Zhang
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Contributions

Wen-jing Niu and Zhong-kai Feng: Conceptualization, Methodology, Supervision, Writing, Investigation, Funding acquisition. Shuai Liu: Data curation and Programming; Yu-bin Chen: Formal analysis and Visualization. Yin-shan Xu: Visualization and Editing. Jun Zhang: Data curation and Literature review.

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Correspondence to Zhong-kai Feng.

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Niu, Wj., Feng, Zk., Liu, S. et al. Multiple Hydropower Reservoirs Operation by Hyperbolic Grey Wolf Optimizer Based on Elitism Selection and Adaptive Mutation. Water Resour Manage 35, 573–591 (2021). https://doi.org/10.1007/s11269-020-02737-8

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