Ecological multi-objective joint optimized scheduling of cascade hydropower plants based on improved marine predators algorithm

水力发电 级联 调度(生产过程) 计算机科学 环境科学 水资源 生态学 数学优化 工程类 数学 化学工程 生物
作者
Fannie Kong,Yiwen Zhuo,Cheng Song
出处
期刊:Journal of Renewable and Sustainable Energy [American Institute of Physics]
卷期号:14 (4) 被引量:4
标识
DOI:10.1063/5.0091309
摘要

The joint operation of large cascade hydropower plants changes the natural hydrological regime of the river, thereby reducing the stability of the basin ecosystem. To coordinate the power generation of cascade hydropower plants' demand and ecological environment demand, this paper establishes an ecological multi-objective optimized scheduling model (EMOOSM) for cascade hydropower plants, aiming at the maximum power generation of cascade hydropower plants and the minimum inappropriate ecological water volume. To solve the complex EMOOSM, a marine predators algorithm was introduced, which was improved and extended to the multi-objective solution level. Multi-objective improved marine predators algorithm (MOIMPA) based on Cauchy variation preserves non-dominated solutions by adding an external archive set and maintaining them with a crowdedness-based fast sorting strategy. The optimal dispatching results of a cascade hydropower plant in China's Pearl River system show that MOIMPA can effectively deal with conflicting power generation and ecological goals. The recommended scheme determined according to the fuzzy set theory and the principle of maximum satisfaction can adapt to the change law of ecological demand and reasonably adjust the inappropriate ecological water volume in the stage according to the different ecological sensitivity in the dispatching period. The recommended scheme in the normal year reduces the inappropriate ecological water volume caused by the operation of the cascade reservoir by 1.7156 × 105 m3 at the expense of only 1.12% of the power generation, effectively balancing the power generation and ecological benefits of the cascade hydropower plant operation. Compared with the results of other algorithms, the Pareto solution set obtained by MOIMPA has a better diversity metric (DM) indicator, maximum spread (MS) indicator, and hypervolume (HV) indicator. The research results provide a theoretical basis and reference for the ecological operation research of cascade hydropower plants.

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