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Record W2168145151 · doi:10.1080/15325008.2012.694969

Stochastic Unit Commitment with Wind Generation Penetration

2012· article· en· W2168145151 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueElectric Power Components and Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPower system simulationWind powerClearanceElectricity marketElectricityElectric power systemElectricity generationUnit (ring theory)Monte Carlo methodEconomic dispatchMathematical optimizationWind speedComputer scienceOperations researchEngineeringPower (physics)MathematicsElectrical engineeringMeteorology

Abstract

fetched live from OpenAlex

Abstract This article presents a new algorithm for analyzing the effects of wind generation uncertainties on short-term power system operation. Monte Carlo simulation is used to obtain a set of wind generation scenarios, and then the scenario reduction algorithm is applied to obtain a reduced set of scenarios. These reduced scenarios are then incorporated into the unit-commitment problem formulation with a locational marginal price based electricity market settlement and a dispatch model to examine the effects of wind generation on electricity market prices, load cleared, social welfare, and system capacity.

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.

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 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: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.754

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 communication0.0000.000
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 head0.022
GPT teacher head0.206
Teacher spread0.184 · 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