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

Abstract

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.