Adaptive monitoring of the progressive hedging penalty for reservoir systems management

Reservoir systems operations problems are in essence stochastic because of the uncertain nature of natural inflows. This leads to very large stochastic models that may not be easy to handle numerically. In this paper, we revisit the decomposition method developed by Rockafellar and Wets (Math Oper R...

Full description

Saved in:
Bibliographic Details
Published in:Energy systems (Berlin. Periodical) Vol. 5; no. 2; pp. 307 - 322
Main Authors: Zéphyr, Luckny, Lang, Pascal, Lamond, Bernard F.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2014
Springer Nature B.V
Subjects:
Algorithms
Analysis
Computer programming
Decomposition
Economics and Management
Energy
Energy Policy
Energy Systems
Heuristic
Lagrange multiplier
Load
Mathematical analysis
Mathematical models
Monitoring
Operations Research/Decision Theory
Optimization
Original Paper
Random variables
Reservoir management
Reservoirs
Stochastic models
Stochasticity
Strategy
Studies
Systems management
Uncertainty
Water
Progressive hedging algorithm
Stochastic programming models
Decomposition method
Reservoir systems operations
Heuristic
ISSN:1868-3967, 1868-3975
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Reservoir systems operations problems are in essence stochastic because of the uncertain nature of natural inflows. This leads to very large stochastic models that may not be easy to handle numerically. In this paper, we revisit the decomposition method developed by Rockafellar and Wets (Math Oper Res 119–147, 1991 ) by proposing new heuristics to initialize and dynamically adjust the penalty parameter of the augmented Lagrangian function on which this method is based. The heuristics are tested on multi-reservoir problems generated randomly and compared with the traditional strategy of setting the penalty parameter to a fixed value.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:1868-3967
1868-3975
DOI:10.1007/s12667-013-0110-4