Optimization of Hydropower Systems Operation Using Bayesian Stochastic Dynamic Programming

Abstract

This study proposes a Bayesian Stochastic Dynamic Programming (BSDP) model which can employ seasonal flow forecasts. The proposed BSDP model generates monthly operating policies for the Skagit Hydropower System (SHS), which supplies energy to the Seattle metropolitan area in Washington State, USA. The BSDP-derived operating policies for the SHS are simulated using historical monthly inflows as well as seasonal flow forecasts during 60 years. Performance of the BSDP model is compared with alternative stochastic dynamic programming models. To illustrate the potential advantage of using the seasonal flow forecasts as well as other hydrologic information, the sensitivity of SHS operation is evaluated by changing (1) the reservoir capacity, (2) the energy demand, and (3) the energy price. The simulation results demonstrate that the seasonal flow forecast should be included for operating the SHS.