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...

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Vydané v:Energy systems (Berlin. Periodical) Ročník 5; číslo 2; s. 307 - 322
Hlavní autori: Zéphyr, Luckny, Lang, Pascal, Lamond, Bernard F.
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2014
Springer Nature B.V
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ISSN:1868-3967, 1868-3975
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Abstract 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.
AbstractList 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.
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.
Issue Title: Managing Uncertainty in Energy Markets (pp 209-369) 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.[PUBLICATION ABSTRACT]
Author Lamond, Bernard F.
Zéphyr, Luckny
Lang, Pascal
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Cites_doi 10.1007/BF01582895
10.1007/BF02204860
10.1029/WR021i006p00779
10.1016/j.ijepes.2009.03.032
10.1007/s10107-002-0331-0
10.1109/TPWRS.2005.846073
10.1007/BF01386316
10.1023/A:1018965626303
10.1080/1055678021000033946
10.2139/ssrn.1268385
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Stochastic programming models
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Reservoir systems operations
Heuristic
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Snippet Reservoir systems operations problems are in essence stochastic because of the uncertain nature of natural inflows. This leads to very large stochastic models...
Issue Title: Managing Uncertainty in Energy Markets (pp 209-369) Reservoir systems operations problems are in essence stochastic because of the uncertain...
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StartPage 307
SubjectTerms 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
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Title Adaptive monitoring of the progressive hedging penalty for reservoir systems management
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