Efficient Parallelization of the Stochastic Dual Dynamic Programming Algorithm Applied to Hydropower Scheduling

dc.contributor.author	Helseth, Arild
dc.contributor.author	Braaten, Hallvard
dc.date.accessioned	2016-01-04T14:15:12Z
dc.date.accessioned	2016-01-12T11:24:30Z
dc.date.available	2016-01-04T14:15:12Z
dc.date.available	2016-01-12T11:24:30Z
dc.date.issued	2015
dc.identifier.citation	Energies 2015, 8(12):14287-14297
dc.identifier.issn	1996-1073
dc.identifier.uri	http://hdl.handle.net/11250/2373358
dc.description	-
dc.description.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
dc.language.iso	eng
dc.rights	Navngivelse-Ikkekommersiell-IngenBearbeidelse 3.0 Norge	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/no/	*
dc.title	Efficient Parallelization of the Stochastic Dual Dynamic Programming Algorithm Applied to Hydropower Scheduling
dc.type	Journal article
dc.type	Peer reviewed
dc.date.updated	2016-01-04T14:15:12Z
dc.identifier.doi	10.3390/en81212431
dc.identifier.cristin	1305689
dc.relation.project	Norges forskningsråd: 225873

Except where otherwise noted, this item's license is described as Navngivelse-Ikkekommersiell-IngenBearbeidelse 3.0 Norge