Cooperation Search Algorithm for Power Generation Production Operation Optimization of Cascade Hydropower Reservoirs

Wen-jing Niu, Zhong-kai Feng (), Yu-rong Li and Shuai Liu
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Wen-jing Niu: ChangJiang Water Resources Commission
Zhong-kai Feng: Hohai University
Yu-rong Li: ChangJiang Water Resources Commission
Shuai Liu: Design and Research Co. Ltd. (BIDR)

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2021, vol. 35, issue 8, No 11, 2465-2485

Abstract: Abstract Over the past decades, the reservoirs operation plays an increasingly important role in coping with the serious water, food and energy crisis. However, the curse of dimensionality poses huge challenges for operators because the computation cost often grows exponentially with the expansion of hydropower system. With strong search performance and high execution efficiency, metaheuristic search algorithms become the research hotspot in the reservoir operation field. Here, cooperation search algorithm (CSA) inspired by team cooperation behaviors in modern enterprise is introduced for power generation operation of cascade hydropower reservoirs. In CSA, the team communication operator is designed to determine promising search areas, and then the reflective learning operator is adopted to enhance the local search, while the internal competition operator is used to retain the elite individuals. The feasibility of the CSA method in numerical optimization problems is fully proved by the simulation results of several test functions. Then, the CSA method is used to solve the power generation operation of cascade hydropower reservoirs. The results show that the CSA method outperforms several traditional methods in both convergence rate and search precision. Thus, an effective tool with strong reliability and robustness is provided for the complex reservoir operation problem.

Keywords: Cascade hydropower reservoirs; Optimal operation; Cooperation search algorithm; Metaheuristic evolutionary algorithm (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11269-021-02842-2

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