Scenario Reduction Algorithms in Stochastic Programming

By Holger Heitsch, Werner Römisch. Computational optimization and applications, 2003

1,021 citations recorded by the source corpus.

Subfields: Electrical and Electronic Engineering, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/17454c31d43b1ec9792b4c3940ae24e23dd5d533

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In Electrical and Electronic Engineering

In Management Science and Operations Research

In Ocean Engineering

Citations in the corpus

Multistage Stochastic Programming: A Scenario Tree Based Approach to Planning under Uncertainty (2011, background): “…that we have described in the context of two-stage stochastic programming, was initially believed to be easily extended to the multistage case (Heitsch & Römisch, 2003); but then it turned out that more elaborated measures of probability distances, integrating the intertemporal aspect of…”
Pricing Electricity in Pools With Wind Producers (2012, methodology): “An original set of 10 000 samples is first generated and subsequently reduced to 100 by applying the scenario reduction technique proposed in [22] and [23].”
Clearing and Pricing for Coordinated Gas and Electricity Day-Ahead Markets Considering Wind Power Uncertainty (2016, methodology): “To simulate the RT scenarios, 5,000 samples are generated following the estimated distribution and are reduced to 30 using the fast forward selection method [33].”
Coordinated Trading of Wind and Thermal Energy (2011, methodology): “The forward selection algorithm proposed in [20] is used in this paper to reduce wind, energy price, and imbalance price scenarios.”
Risk Assessment of Virtual Power Plants Offering in Energy and Reserve Markets (2016, methodology): “In this case, the scenario-reduction techniques proposed in [38], [39] should be used to reduce the computational burden.”
Approximations for Probability Distributions and Stochastic Optimization Problems (2011, methodology): “This concept of filtration distance for scenario generation has been developed by Heitsch and Roemisch (2003) and Heitsch et al. (2006).”
A Fast Solution Method for Stochastic Transmission Expansion Planning (2017, methodology): “Existing scenario reduction methods include clustering algorithms such as K-means [15], backward and forward selection algorithms [11], [12], and their variation or extension [11], [16], heuristic-based methods [14], [17], etc.”
A multi-stage stochastic supply network design problem with financial decisions and risk management (2012, neutral): “The reader can refer to Heitsch and Römisch [14,15], Hoyland and Wallace [18] and Möller et al.”
Scenario Reduction for Futures Market Trading in Electricity Markets (2009, methodology): “the forward scenario reduction algorithm [16].”
Scenario tree modeling for multistage stochastic programs (2009, background): “These bounds allow to control the process of recursive scenario reduction [13] and branching.”
Stochastic Model Predictive Control: An Overview and Perspectives for Future Research (2016, neutral): “In these approaches, scenario-reduction methods can be used to significantly reduce the number of scenarios [104].”
Stochastic model predictive control for economic/environmental operation management of microgrids: An experimental case study (2016, methodology): “The Backward Reduction Algorithm has been proved to provide very good performances in the two-stage mixed integer stochastic programming framework [72,21].”