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Record W4406354569 · doi:10.1109/tpwrs.2025.3528864

Generation Planning and Operation Under Power Stability Constraints: A Hydro-Quebec Use Case

2025· article· en· W4406354569 on OpenAlexaffabout
Alexandre Besner, Alexandre Blondin Massé, Abderrahman Bani, Mouad Morabit, François Berthaut, Luc Charest, David Ialongo, Yves Mbeutcha, Simon Couture-Gagnon, Julien Fournier

Bibliographic record

VenueIEEE Transactions on Power Systems · 2025
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsElectric power systemStability (learning theory)Electricity generationPower (physics)Control theory (sociology)Computer scienceEngineeringOperations researchReliability engineeringControl engineeringMathematical optimizationMathematicsControl (management)

Abstract

fetched live from OpenAlex

Hydro-Quebec (HQ) is a vertically integrated utility that produces, transmits, and distributes most of the electricity in the province of Quebec. The power grid it operates has a particular architecture created by large hydroelectric dams located far north and the extensive 735 kV transmission grid that allows the generated power to reach the majority of the load located thousands of kilometers away in the southern region of Quebec. The specificity of the grid has led HQ to develop monitoring tools responsible for generating so-called stability limits. Those stability limits take into account several nonlinear phenomena such as angular stability, frequency stability, or voltage stability. Since generation planning and operation tools rely mostly on mixed integer linear programming formulation, HQ had to adapt its tools to integrate stability limits into them. This paper presents the challenges it faced, especially considering its reserve monitoring tool and unit commitment tool.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.849
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.242
Teacher spread0.216 · 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 teacher head, not a consensus.

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

Citations1
Published2025
Admission routes2
Has abstractyes

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