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Mixing Dynamic Programming and Spatial Decomposition Methods Carpentier, Pierre
Description
We consider a stochastic optimization problem in which different units are connected together via a network. Each unit is a (small) control system, located at a node. Each unit state evolution is affected by uncertainties and controls of the neighboring nodes transmitted through edges. Static constraints couple all units at each time. We formulate the associated global stochastic optimization problem. We propose two decomposition methods, whether we decouple the constraints by prices or by resources. We show that the optimal value of the global problem can be bounded above by a sum of resource-decomposed nodal value, and below by a sum of price-decomposed nodal value. We provide conditions under which these nodal values can be computed by dynamic programming. We illustrate these results with numerical studies that tackle the optimization of urban micro-grids of large size. Finally, we introduce two different information structures for these microgrids, namely the centralized and the decentralized ones, and we analyse the lower and upper bounds when considering these information structures.
Item Metadata
Title |
Mixing Dynamic Programming and Spatial Decomposition Methods
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2019-09-24T17:41
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Description |
We consider a stochastic optimization problem in which different units
are connected together via a network. Each unit is a (small) control
system, located at a node. Each unit state evolution is affected by
uncertainties and controls of the neighboring nodes transmitted through
edges. Static constraints couple all units at each time. We formulate
the associated global stochastic optimization problem. We propose two
decomposition methods, whether we decouple the constraints by prices
or by resources. We show that the optimal value of the global problem
can be bounded above by a sum of resource-decomposed nodal value,
and below by a sum of price-decomposed nodal value. We provide
conditions under which these nodal values can be computed by dynamic
programming. We illustrate these results with numerical studies that
tackle the optimization of urban micro-grids of large size. Finally,
we introduce two different information structures for these microgrids,
namely the centralized and the decentralized ones, and we analyse the
lower and upper bounds when considering these information structures.
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Extent |
36.0 minutes
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: ENSTA ParisTech
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Series | |
Date Available |
2020-03-23
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0389613
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Faculty
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International