Efficient parallelization of the stochastic dual dynamic programming algorithm applied to hydropower scheduling

Abstract

Stochastic dual dynamic programming (SDDP) has become a popular algorithm used in practical long-term scheduling of hydropower systems. The SDDP algorithm is computationally demanding, but can be designed to take advantage of parallel processing. This paper presents a novel parallel scheme for the SDDP algorithm, where the stage-wise synchronization point traditionally used in the backward iteration of the SDDP algorithm is partially relaxed. The proposed scheme was tested on a realistic model of a Norwegian water course, proving that the synchronization point relaxation significantly improves parallel efficiency.