An aggregation-decomposition bayesian stochastic optimization model for cascade hydropower reservoirs using medium-range precipitation forecasts

2nd Annual International Conference on Information System and Artificial Intelligence

物理学;计算机科学

Peng, Yong^1 ; Xu, Wei^2 ; Zhang, Xiaoli^3

School of Hydraulic Engineering, Dalian University of Technology, Dalian, China^1

College of River and Ocean Engineering, Chongqing Jiaotong University, Chongqing, China^2

School of Water Conservancy, North China University of Water Resources and Electric, Zhengzhou, China^3

关键词: Efficiency and reliability;    Global forecast systems;    Hydro-power generation;    Precipitation forecast;    Quantitative precipitation forecast;    Stochastic dynamic programming;    Stochastic optimization model;    Utilization efficiency;   

Others  :  https://iopscience.iop.org/article/10.1088/1742-6596/887/1/012005/pdf
DOI  :  10.1088/1742-6596/887/1/012005

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The forecast information is essential to improve the utilization efficiency of hydropower resources. To address the uncertainties of forecasting inflow, the Aggregation-Decomposition Bayesian Stochastic Dynamic Programming (AD-BSDP) model is presented in the present paper by using the 10-days precipitation value of the Quantitative Precipitation Forecasts from Global Forecast System (GFS-QPFs). The application in China's Hun River cascade hydropower reservoirs shows that the GFS-QPFs are beneficial for hydropower generation and the performance of AD-BSDP is more efficiency and reliability than the others models.