Mixed Spatial and Temporal Decompositions for Large Scale Multistage Stochastic Optimization Problems - Archive ouverte HAL Access content directly
Journal Articles Journal of Optimization Theory and Applications Year : 2020

Mixed Spatial and Temporal Decompositions for Large Scale Multistage Stochastic Optimization Problems

(1) , (2) , (2) , (2)
1
2

Abstract

We consider multistage stochastic optimization problems involving multiple units. Each unit is a (small) control system. Static constraints couple units at each stage. We present a mix of spatial and temporal decompositions to tackle such large scale problems. More precisely, we obtain theoretical bounds and policies by means of two methods, depending whether the coupling constraints are handled by prices or by resources. We study both centralized and decentralized information structures. We report the results of numerical experiments on the management of urban microgrids. It appears that decomposition methods are much faster and give better results than the standard Stochastic Dual Dynamic Programming method, both in terms of bounds and of policy performance.
Fichier principal
Vignette du fichier
preprint_Mixed_Spatial_and_Temporal_Decompositions_v1.pdf (482.34 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-02420480 , version 1 (19-12-2019)
hal-02420480 , version 2 (14-06-2021)

Identifiers

Cite

Pierre Carpentier, Jean-Philippe Chancelier, Michel de Lara, François Pacaud. Mixed Spatial and Temporal Decompositions for Large Scale Multistage Stochastic Optimization Problems. Journal of Optimization Theory and Applications, 2020, 186 (3), pp.985-1005. ⟨10.1007/s10957-020-01733-7⟩. ⟨hal-02420480v2⟩
114 View
88 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More