Problem-driven scenario clustering in stochastic optimization

Keutchayan, Julien; Ortmann, Janosch; Rei, Walter

doi:10.1007/s10287-023-00446-2

Cited by 16 publications

(2 citation statements)

References 42 publications

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“…The number of PV generation and demand scenarios were each reduced to five (i.e., the most representative ones) using the K-means algorithm. This reduction enhances computational efficiency and solution quality by eliminating scenarios that can introduce noise in the optimization process [36,37]. Figure 6 illustrates the 1000 predictions and the reduced PV generation or demand scenarios.…”

Section: Stochastic Analysismentioning

confidence: 99%

Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses

Montoya,

Grisales-Noreña,

Gil-González

2024

Sustainability

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The techno–environmental analysis of distributed energy resources in electrical distribution networks is a complex optimization task due to the non-convexities of its nonlinear programming formulation. This research employs convex optimization to address this issue while minimizing the expected carbon dioxide emissions and daily energy losses of a distribution grid via the optimal dispatch of battery energy storage units (BESUs) and renewable energy units (REUs). The exact non-convex model is approximated via semi-definite programming in the complex variable domain. The optimal Pareto front is constructed using a weighting-based optimization approach. Numerical results using an IEEE 69-bus grid confirm the effectiveness of our proposal when considering unitary and variable power factor operation for the BESUs and the REUs. All numerical simulations were carried out using MATLAB software (version 2022b), a convex disciplined tool (CVX), and the semi-definite programming solvers SEDEUMI and SDPT3.

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Section: Stochastic Analysismentioning

confidence: 99%

Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses

Montoya,

Grisales-Noreña,

Gil-González

2024

Sustainability

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“…By actively participating in the problem-solving process, students have improved their clinical thinking and teamwork abilities. This study aims to explore the potential and application of problem driven learning in obstetrics and gynecology education [2].…”

Section: Introductionmentioning

confidence: 99%

Exploration and Implementation of Problem Driven Learning Model in Obstetrics and Gynecology Education

2023

aetp

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This study aims to explore the application of problem driven learning mode in obstetrics and gynecology education. Traditional medical education methods have limitations in cultivating students' clinical thinking and problem-solving abilities. Problem driven learning is a problem-based educational method that promotes deep learning and practical skill development by actively engaging students in solving problems in real-world clinical cases. This study provides a detailed introduction to the principles of problem driven learning and its application in medical education, and proposes methods for designing and implementing problem driven learning in obstetrics and gynecology education. The study also emphasizes the importance of student participation and feedback to improve teaching quality. The problem driven learning model provides an innovative educational method for cultivating future obstetricians and gynecology doctors, which is expected to improve their clinical practice ability and patient care quality.

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Uncertainty in Optimization

King,

Wallace

2024

Springer Series in Operations Research and Financial Engineering

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Problem-driven scenario clustering in stochastic optimization

Cited by 16 publications

References 42 publications

Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses

Multi-Objective Battery Coordination in Distribution Networks to Simultaneously Minimize CO2 Emissions and Energy Losses

Exploration and Implementation of Problem Driven Learning Model in Obstetrics and Gynecology Education

Uncertainty in Optimization

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