Kriging水动力学代理模型在水库群优化调度中的应用

徐杨; 吕昊; 刘帅; 方威; 覃晖

doi:10.11988/ckyyb.20221303

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长江科学院院报 ›› 2024, Vol. 41 ›› Issue (2) : 7-13. DOI: 10.11988/ckyyb.20221303

流域综合管理

Kriging水动力学代理模型在水库群优化调度中的应用

徐杨¹, 吕昊², 刘帅^3,4, 方威^3,4, 覃晖^3,4

作者信息 +

Application of Kriging-based Hydrodynamic Surrogate Model for Optimal Scheduling of Cascade Reservoirs

XU Yang¹, LÜ Hao², LIU Shuai^3,4, FANG Wei^3,4, QIN Hui^3,4

Author information +

文章历史 +

摘要

为解决传统水动力学模型结合调度模型求解耗时长的难题,使用代理模型模拟水动力学模型以提升求解效率。从金沙江下游—三峡梯级水库调度实际出发,结合水动力学和代理模型相关理论,建立了基于Kriging代理模型的水库群多目标调度模型并采用多目标进化算法对模型进行求解,最后采用投影追踪法对得到的多目标帕累托前沿进行决策。研究结果表明,代理模型的平均模拟误差≤1.5%,能够较为精确地替代原有长耗时水动力学模型并结合调度模型给出具有竞争关系的帕累托前沿及合理的折衷方案,对促进梯级水库综合效益发挥提供理论依据。

Abstract

To address the time-consuming nature of traditional hydrodynamic model combined with scheduling model, we propose a surrogate model as an approximation to enhance efficiency. The research focuses on the practical scheduling of cascade reservoirs from lower Jinsha River to the Three Gorges. By combining hydrodynamics theory with the Kriging surrogate model, we established a multi-objective dispatching model for a multi-reservoir system, and subsequently solved the model using a multi-objective evolutionary algorithm. Ultimately, we identified the multi-objective Pareto front of the model by adopting the projection pursuit method in decision-making. Research findings demonstrate that the surrogate model exhibits an average simulation error of less than 1.5%, effectively replacing the time-consuming hydrodynamic model while generating a competitive Pareto front and providing reasonable compromise solutions. These outcomes lay a theoretical foundation for advancing the comprehensive benefits of cascade reservoirs.

导出引用

徐杨, 吕昊, 刘帅, 方威, 覃晖. Kriging水动力学代理模型在水库群优化调度中的应用[J]. 长江科学院院报. 2024, 41(2): 7-13 https://doi.org/10.11988/ckyyb.20221303

XU Yang, LÜ Hao, LIU Shuai, FANG Wei, QIN Hui. Application of Kriging-based Hydrodynamic Surrogate Model for Optimal Scheduling of Cascade Reservoirs[J]. Journal of Changjiang River Scientific Research Institute. 2024, 41(2): 7-13 https://doi.org/10.11988/ckyyb.20221303

中图分类号： TV697.11

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