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Record W2130126778 · doi:10.1109/tpwrs.2003.821443

Unit Commitment With Dual Variable Constraints

2004· article· en· W2130126778 on OpenAlexaff
A.L. Motto, F.D. Galiana

Bibliographic record

VenueIEEE Transactions on Power Systems · 2004
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsPower system simulationUnit (ring theory)Electricity marketDual (grammatical number)ElectricityMargin (machine learning)Variable (mathematics)Economic dispatchEconomicsComputer scienceMicroeconomicsProduction (economics)Operations researchUnit priceMarginal costMathematical optimizationElectric power systemPower (physics)EngineeringMathematics

Abstract

fetched live from OpenAlex

A new unit commitment model is proposed for market economies with some form of indivisibilities (nonconvexities). Coordination in electricity pool auction markets with unit commitment is a conspicuous example. This presentation applies to a single-time period unit commitment with no network. The new formulation includes a market coordinator who collects the sale and purchase bids, and determines the optimal solution that balances the total generation with the forecast demand. A solution to the new commitment model is said to be optimal if it maximizes the (monetary) margin of every accepted production unit, while minimizing the sum of the margins of the unaccepted units that would achieve positive margins at the prevailing market price. The new unit commitment model produces a market price that is the system-wide marginal (bid) cost. Some preliminary results are reported, considering the single-period unit commitment problem, which provide some insights into the adequacy of the proposed approach as well as its economic implications as a tool for managing restructured energy systems.

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 score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0030.002
Open science0.0020.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.009
GPT teacher head0.193
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

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

Citations13
Published2004
Admission routes1
Has abstractyes

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