水科学进展2026，Vol.37Issue(2)：272-285,14.DOI:10.14042/j.cnki.32.1309.2026.02.007
基于离散微分动态规划和机器学习的水库群调度
Reservoir group operation based on discrete differential dynamic programming and machine learning
摘要
Abstract
To address the"curse of dimensionality"and computational efficiency bottlenecks associated with applying traditional Discrete Differential Dynamic Programming(DDDP)to reservoir group operation,this paper proposes an improved method(IDDDP)that integrates DDDP with machine learning.This method employs a Bidirectional Long Short-Term Memory network with an attention mechanism to establish a direct mapping relationship between"inflow-initial/final water level"and reservoir power output,replacing the traditional recursive calculations and thereby significantly reducing the computational burden.Taking the cascade reservoir system in the Wujiang River Basin as a case study,comparative experiments were conducted for two typical scenarios—power generation scheduling and grid peak-shaving—under varying discretization precision and system scales.The results indicate that the scheduling schemes obtained by IDDDP are highly consistent with DDDP results in key performance indicators such as power generation and load rate,with relative deviations kept within an allowable engineering range.The computational time is reduced by one to two orders of magnitude,and the method maintains stable performance under both wet and dry typical hydrological year types.The proposed method significantly enhances computational efficiency while ensuring scheduling accuracy,providing a reliable and efficient new approach for the optimal operation of large-scale reservoir groups.关键词
水库群调度/维数灾/双向长短期记忆网络/离散微分动态规划Key words
multireservoir scheduling/dimension disaster/bidirectional long short-term memory network/discrete differential dynamic programming分类
建筑与水利引用本文复制引用
关铁生,尹鑫,罗涛,冯仲恺,张海滨,马昱斐..基于离散微分动态规划和机器学习的水库群调度[J].水科学进展,2026,37(2):272-285,14.基金项目
国家自然科学基金项目(52394234 （）
52379009) The study is financially supported by the National Natural Science Foundation of China(No.52394234 （）
No.52379009). （）
