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Using big data computing framework and parallelized PSO algorithm to construct the reservoir dispatching rule optimization

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The paper aims to study how to realize the rational allocation and efficient utilization of water resources among reservoirs and coordinate the balanced optimization of benefit among dispatching objectives under the premise of ensuring flood control safety. A multi-objective optimal dispatching system for reservoirs in Jinsha River basin based on the Spark big data computing framework and parallelized particle swarm optimization (PSO) is proposed. The characteristics of multiple objectives of water resources optimal dispatching system in Jinsha River basin are analyzed. The multiple objectives have been transformed into single objectives, and the solving model of the problem is obtained. Secondly, the parallel algorithm programming model, the PSO algorithm and its parallel strategy for solving optimization problems, and the parallel method of PSO based on Spark big data computing framework are studied. The results show that the research work provides a scientific theoretical basis and a feasible optimization method for the management and dispatch of cascade hydropower stations. Therefore, this study plays a decisive role in promoting the efficient operation of water resources optimal dispatching system and has good reference value for the development and application of big data parallel programming based on Spark platform.

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Authors and Affiliations

  1. College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang, China

    Wenjuan Zhang & Yingping Huang

  2. Engineering Research Center of Eco-Environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang, China

    Wenjuan Zhang & Yingping Huang

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  1. Wenjuan Zhang
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  2. Yingping Huang
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Correspondence to Yingping Huang.

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Communicated by Mu-Yen Chen.

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Zhang, W., Huang, Y. Using big data computing framework and parallelized PSO algorithm to construct the reservoir dispatching rule optimization. Soft Comput 24, 8113–8124 (2020). https://doi.org/10.1007/s00500-019-04188-9

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