Scenario reduction and scenario tree generation for stochastic programming using Sinkhorn distance

Kammammettu, Sanjula; Li, Zukui

doi:10.1016/j.compchemeng.2022.108122

Cited by 16 publications

(4 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Figure 1 demonstrates a general scenario reduction algorithm based on information from sources (Kammammettu and Li, 2023;Peredo and Herrero, 2022;Dvorkin et al, 2014). Scenario reduction, as outlined by Tarasov (2016), encompasses techniques aimed at filtering out potentially unrepresentative elements from the initial set of scenarios to form an optimal one.…”

Section: Resultsmentioning

confidence: 99%

“…With the apparent obviousness of the described problem and the urgency of the need for its solution, the methods of scenario reduction are still described quite rarely (Kammammettu and Li, 2023;Peredo and Herrero, 2022;Dvorkin et al, 2014). Interestingly, researchers often do not focus on optimization opportunities to reduce the load on computing power while noting the dependence of multi-criteria optimization on the computing capabilities of computers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multiparametric Optimization of Complex System Management Scenarios Based on Simulation Models

Pospelov,

Burlutskaya,

Gintciak

et al. 2023

IJTech

View full text Add to dashboard Cite

This work is devoted to the development of a multiparametric optimization module for a digital management decision support tool based on simulation models. It is noted that the optimization of simulation models of complex socioeconomic and sociotechnical systems involves the generation of multiple scenarios of system development, their calculation, and further comparison, which imposes additional requirements on the optimization algorithms used. Moreover, complex socioeconomic and sociotechnical systems are characterized by a multiplicity of goals, which leads to multiparametric optimization. The result of the work is the algorithm for solving the problem of optimization of multiparametric scenario calculations using the example of a two-parameter optimization problem. The scope of the calculation optimization problem is to form the optimal set of scenarios that will ensure satisfactory computing time and, at the same time, give a representative scenario calculation result. Thus, the contribution of the current research is to formalize the processes of optimizing the parameters of simulation models of complex systems. In the course of the study, existing approaches to process optimization are considered. Based on the analysis of existing approaches to the formation of an optimal set of scenarios, ways to improve the algorithm type using approaches to scenario reduction or the introduction of genetic algorithms for the formation of an optimal set of scenarios are proposed. This work is carried out within a project to develop a digital tool to support managerial decision-making in sociotechnical and socioeconomic systems.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multiparametric Optimization of Complex System Management Scenarios Based on Simulation Models

Pospelov,

Burlutskaya,

Gintciak

et al. 2023

IJTech

View full text Add to dashboard Cite

show abstract

“…As it can be seen in the above literature, robust optimization and stochastic programming are two common optimization methods under uncertainty. Due to the intractability and higher costs of robust optimization 33 and the assumption that the probability distribution of uncertain parameters is known in stochastic programming as proposed by Dantzig (1955) 34 , the more tractable stochastic programming method specifically designed to deal with modeling problems involving uncertainty was chosen for this paper 35 . In this paper, a fuzzy chance-constrained programming model in stochastic programming is introduced.…”

Section: Methodsmentioning

confidence: 99%

A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions

Yan,

Wang,

Liu

et al. 2024

Sci Rep

View full text Add to dashboard Cite

With the development of the electric vehicle industry, the number of power batteries has increased dramatically. Establishing a recycling EOL (end-of-life) battery network for secondary use is an effective way to solve resource shortage and environmental pollution. However, existing networks are challenging due to the high uncertainty of EOL batteries, e.g., quantity and quality, resulting in a low recycling rate of the recovery network. To fill this gap, this paper proposes a stochastic programming approach for recovery network design under uncertain conditions of EOL batteries. Firstly, a multi-objective model for battery recovery network is established, considering carbon emissions and economic benefits. Secondly, a stochastic programming approach is proposed to clarify the model. Subsequently, the genetic algorithm is employed to solve the proposed model. Finally, a recovery network case of Region T is given to verify the credibility and superiority of the proposed method. The results demonstrate that the proposed model reduces carbon emissions by 20 metric tons and increases overall economic benefits by 10 million yuan in Region T compared to the deterministic model. Furthermore, the two portions affecting the optimization results are also discussed to provide a reference for reducing carbon emissions and improving economic efficiency in recycling networks.

show abstract

“…This paper adopts LHS to generate 100 sets of scenarios and utilizes a backward scenario reduction technique based on Kantorovich distance to reduce the number of scenarios, resulting in 10 typical scenarios [30,31]. Based on these scenarios, the economic risk is transformed into costs using CVaR theory.…”

Section: Solution Methods For the Proposed Modelmentioning

confidence: 99%

Pricing Mechanism and Trading Strategy Optimization for Microgrid Cluster Based on CVaR Theory

Chen,

Zhang,

Chen

et al. 2023

Electronics

View full text Add to dashboard Cite

With the increasing penetration rate of renewable energy generation, the uncertainty of renewable energy output in microgrid cluster (MGC) leads to significant fluctuations in transaction volume, which may lead to the risk of transaction default. This paper proposes a day-ahead two layer trading model for microgrid cluster based on price trading mechanism and Conditional value-at-risk (CVaR) theory. Firstly, the upper-layer establishes an objective to minimize the overall power fluctuation of the microgrid cluster using Demand response (DR) with a penalty mechanism. The microgrid cluster adopts an internal pricing mechanism and adjusts transaction prices based on internal supply-demand conditions to guide microgrids’ participation in intracluster trading, thereby encouraging the microgrid to use the flexible resources to reduce power fluctuation. Secondly, the lower-layer optimization establishes an optimization model with the objective of minimizing the comprehensive operating cost of the microgrid cluster. The model employs backward scenario reduction techniques to obtain multiple sets of typical scenarios for renewable energy generation, and the CVaR theory is introduced to quantify the potential risk of transaction default. Finally, the effectiveness of the proposed models is verified through case studies considering various application scenarios.

show abstract

Scenario reduction and scenario tree generation for stochastic programming using Sinkhorn distance

Cited by 16 publications

References 26 publications

Multiparametric Optimization of Complex System Management Scenarios Based on Simulation Models

Multiparametric Optimization of Complex System Management Scenarios Based on Simulation Models

A stochastic programming approach for EOL electric vehicle batteries recovery network design under uncertain conditions

Pricing Mechanism and Trading Strategy Optimization for Microgrid Cluster Based on CVaR Theory

Contact Info

Product

Resources

About