Scenario reduction in stochastic programming

2003 ◽  
Vol 95 (3) ◽  
pp. 493-511 ◽  
Author(s):  
J. Dupa?ov� ◽  
N. Gr�we-Kuska ◽  
W. R�misch
Keyword(s):  
Stochastic Programming ◽  
Scenario Reduction

scholarly journals Scenario reduction in stochastic programming with respect to discrepancy distances

2007 ◽  
Vol 43 (1) ◽  
pp. 67-93 ◽  
Author(s):  
René Henrion ◽  
Christian Küchler ◽  
Werner Römisch
Keyword(s):  
Stochastic Programming ◽  
Scenario Reduction

Nonlinear Stochastic Programming Involving CVaR in the Objective and Constraints

Informatica ◽  
2015 ◽  
Vol 26 (4) ◽  
pp. 569-591 ◽  
Author(s):  
Valerijonas Dumskis ◽  
Leonidas Sakalauskas
Keyword(s):  
Stochastic Programming

scholarly journals Contracts in electricity markets under EU ETS: A stochastic programming approach

2021 ◽  
pp. 105309
Author(s):  
Arega Getaneh Abate ◽  
Rossana Riccardi ◽  
Carlos Ruiz
Keyword(s):  
Stochastic Programming ◽  
Electricity Markets ◽  
Programming Approach ◽  
Eu Ets

scholarly journals Scenario reduction methodologies under uncertainties for reservoir development purposes: distance-based clustering and metaheuristic algorithm

2021 ◽  
Author(s):  
Seyed Kourosh Mahjour ◽  
Antonio Alberto Souza Santos ◽  
Manuel Gomes Correia ◽  
Denis José Schiozer
Keyword(s):  
Flow Simulation ◽  
Reduction Process ◽  
Computational Time ◽  
Metaheuristic Algorithm ◽  
Dynamic Data ◽  
Scenario Reduction ◽  
Field Development ◽  
Data Usage ◽  
Simulation Process ◽  
Uncertainty Space

AbstractThe simulation process under uncertainty needs numerous reservoir models that can be very time-consuming. Hence, selecting representative models (RMs) that show the uncertainty space of the full ensemble is required. In this work, we compare two scenario reduction techniques: (1) Distance-based Clustering with Simple Matching Coefficient (DCSMC) applied before the simulation process using reservoir static data, and (2) metaheuristic algorithm (RMFinder technique) applied after the simulation process using reservoir dynamic data. We use these two methods as samples to investigate the effect of static and dynamic data usage on the accuracy and rate of the scenario reduction process focusing field development purposes. In this work, a synthetic benchmark case named UNISIM-II-D considering the flow unit modelling is used. The results showed both scenario reduction methods are reliable in selecting the RMs from a specific production strategy. However, the obtained RMs from a defined strategy using the DCSMC method can be applied to other strategies preserving the representativeness of the models, while the role of the strategy types to select the RMs using the metaheuristic method is substantial so that each strategy has its own set of RMs. Due to the field development workflow in which the metaheuristic algorithm is used, the number of required flow simulation models and the computational time are greater than the workflow in which the DCSMC method is applied. Hence, it can be concluded that static reservoir data usage on the scenario reduction process can be more reliable during the field development phase.

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