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Record W4413472561 · doi:10.1109/tpwrs.2025.3599746

A Decomposition Matheuristic for the Transient Stability Constrained Unit Commitment at Hydro-Quebec

2025· article· en· W4413472561 on OpenAlexafffundabout
El Mehdi Er Raqabi, Abderrahman Bani, Mouad Morabit, Alexandre Blondin Massé, Alexandre Besner, Julien Fournier

Bibliographic record

VenueIEEE Transactions on Power Systems · 2025
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsHydro-QuébecPolytechnique Montréal
FundersMitacs
KeywordsPower system simulationTransient (computer programming)DecompositionStability (learning theory)Unit (ring theory)Benders' decompositionElectric power systemComputer scienceControl theory (sociology)EngineeringMathematical optimizationMathematicsPower (physics)PhysicsChemistryArtificial intelligence

Abstract

fetched live from OpenAlex

This paper tackles a complex variant of the unit commitment problem at Hydro-Quebec, referred to as the transient stability constrained unit commitment (TSCUC) problem. First, we present the TSCUC mathematical formulation. Then, we analyze the TSCUC model to detect sources of complexity. After that, we develop an efficient matheuristic that decomposes the problem based on the temporal dimension. We enhance the matheuristic by effectively linearizing non-linear constraints and tuning the optimization solver's configurations. Finally, we highlight the matheuristic performance on real instances from Hydro-Quebec.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.095
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.249
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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