Automatic Formulation of Stochastic Programs Via an Algebraic Modeling Language

Thénie, Julien; Delft, Christian Van; Vial, Jean–Philippe

doi:10.1007/s10287-006-0022-z

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…Combining the uncertainty on the two parameters we end up with a stochastic event tree with four scenarios (from SC1 to SC4) that are represented in Figure 20. To generate the stochastic formulation we use the DET2STO 7 tool (Dubois, Thénié and Vial, 2005;Thénié, van Delft, and Vial, 2007). This software automatically formulates a stochastic version of a dynamic deterministic linear program when the uncertainty is described by a simple event tree.…”

Section: Uncertain Parameters and Stochastic Formulationmentioning

confidence: 99%

Assessing the Future of Renewable and Smart Grid Technologies in Regional Energy Systems

Babonneau

Haurie

Tarel

et al. 2012

Swiss J Economics Statistics

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Summary In this paper we present the regional techno-economic model ETEM, designed for the analysis of regional energy/environment systems and we show how it can be used to explore the possible penetration of new technologies in a region corresponding roughly to the canton of Geneva. We investigate three scenarios with different constraints on CO2 emissions and electricity imports and show the essential role played by new technologies, linked through a smart grid, in the effort toward a sustainable energy system. We strengthen our conclusion with a stochastic approach dealing with uncertainty in future electricity prices and electric car technology penetration.

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Section: Uncertain Parameters and Stochastic Formulationmentioning

confidence: 99%

Assessing the Future of Renewable and Smart Grid Technologies in Regional Energy Systems

Babonneau

Haurie

Tarel

et al. 2012

Swiss J Economics Statistics

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“…This produces a new problem, the so-called deterministic equivalent, whose formulation involves many more variables and constraints than the deterministic version. This operation is formal but tedious; it can be made easier by tools like the one described in [23]. The optimal solution of the deterministic equivalent problem provides a prediction of the optimal value of the original problem.…”

Section: Phase 2: Formulating and Solving The Deterministic Equivalentmentioning

confidence: 99%

Step decision rules for multistage stochastic programming: A heuristic approach

Thénie

Vial

2008

Automatica

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Stochastic programming with step decision rules, SPSDR, is an attempt to overcome the curse of computational complexity of multistage stochastic programming problems. SPSDR combines several techniques. The first idea is to work with independent experts. Each expert is confronted with a sample of scenarios drawn at random from the original stochastic process. The second idea is to have each expert work with step decision rules. The optimal decision rules of the individual experts are then averaged to form the final decision rule. The final solution is tested on a very large sample of scenarios. SPSDR is then tested against two alternative methods: regular stochastic programming on a problem with 3 stages and 2 recourses; robust optimization with affinely adjustable recourses on a 12-stage model. The performance of the new method turns out to be competitive on those examples, while it permits a tighter control on computational complexity than standard stochastic programming.

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“…One can easily define the deterministic equivalent of a stochastic problem, especially if this problem is a linear programming problem. This step can even be automatized, as in [34].…”

Section: Introductionmentioning

confidence: 97%

The Benefits of Cooperation Under Uncertainty: the Case of Climate Change

Bréchet

Thénie

Zeimes

et al. 2011

Environ Model Assess

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This article presents an analysis of the behaviour of countries defining their climate policies in an uncertain context. The analysis is made using the S-CWS model, a stochastic version of an integrated assessment growth model. The model includes a stochastic definition of the climate sensitivity parameter. We show that the impact of uncertainty on policy design critically depends on the shape of the damage function. We also examine the benefits of cooperation in the context of uncertainty: We highlight the existence of an additional benefit of cooperation, namely risk reduction.

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Automatic Formulation of Stochastic Programs Via an Algebraic Modeling Language

Cited by 14 publications

References 11 publications

Assessing the Future of Renewable and Smart Grid Technologies in Regional Energy Systems

Assessing the Future of Renewable and Smart Grid Technologies in Regional Energy Systems

Step decision rules for multistage stochastic programming: A heuristic approach

The Benefits of Cooperation Under Uncertainty: the Case of Climate Change

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