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A Scenario Tree-Based Decomposition for Solving Multistage Stochastic ProgramsComputational Results

A Scenario Tree-Based Decomposition for Solving Multistage Stochastic Programs: Computational... [In this chapter, we present a series of computational results for the solution of the D-OPGen and S-OPGen problem with the aim of documenting the performance of the solution approaches presented in the previous chapters. In Section 8.1, we start with a description of the problem instances considered for the computations by specifying the power generation system, followed by the presentation of the generated scenario trees. Subsequently, we computationally investigate the incorporation of facets determined for the stochastic switching polytope of Chapter 4 as cutting planes to a branchand-cut algorithm. The main focus of this chapter lies on the investigation on the computational behavior of the SD-BB algorithm whose development and implementation has been described in the previous two chapters. In this context, we perform a systematic calibration of the applied methods and parameters with the aim of obtaining general suggestions for the setting. On this basis the algorithm is applied to instances of larger size where we scale the basic characteristics which define a problem instance of the S-OPGen problem.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Scenario Tree-Based Decomposition for Solving Multistage Stochastic ProgramsComputational Results

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Publisher
Vieweg+Teubner
Copyright
© Vieweg+Teubner Verlag | Springer Fachmedien Wiesbaden GmbH, Wiesbaden 2011
ISBN
978-3-8348-1409-8
Pages
125 –169
DOI
10.1007/978-3-8348-9829-6_8
Publisher site
See Chapter on Publisher Site

Abstract

[In this chapter, we present a series of computational results for the solution of the D-OPGen and S-OPGen problem with the aim of documenting the performance of the solution approaches presented in the previous chapters. In Section 8.1, we start with a description of the problem instances considered for the computations by specifying the power generation system, followed by the presentation of the generated scenario trees. Subsequently, we computationally investigate the incorporation of facets determined for the stochastic switching polytope of Chapter 4 as cutting planes to a branchand-cut algorithm. The main focus of this chapter lies on the investigation on the computational behavior of the SD-BB algorithm whose development and implementation has been described in the previous two chapters. In this context, we perform a systematic calibration of the applied methods and parameters with the aim of obtaining general suggestions for the setting. On this basis the algorithm is applied to instances of larger size where we scale the basic characteristics which define a problem instance of the S-OPGen problem.]

Published: Jan 20, 2011

Keywords: Wind Power; Planning Horizon; Test Instance; Lagrangian Relax; Scenario Tree

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