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Record W1976765048 · doi:10.1049/iet-gtd.2009.0099

Re-defining the reactive power dispatch problem in the context of competitive electricity markets

2010· article· en· W1976765048 on OpenAlexaffabout
Claudio A. Cañizares, Kankar Bhattacharya, Ismael El-Samahy, Hossein Haghighat, Jiuping Pan, Chenghui Tang

Bibliographic record

VenueIET Generation Transmission & Distribution · 2010
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsIndependent Electricity System OperatorUniversity of Waterloo
Fundersnot available
KeywordsAC powerElectricity marketContext (archaeology)Benchmark (surveying)ElectricityElectric power systemComputer scienceEconomic dispatchScheduleElectricity generationPower (physics)Mathematical optimizationReliability engineeringVoltageEngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

This study proposes a novel reactive power dispatch model that takes into account both the technical and economical aspects associated with reactive power dispatch in the context of the new operating paradigms in competitive electricity markets. The main objective of the proposed model is to minimise the total amount of dollars paid by the system operator to the generators for providing the required reactive power support. The real power generation is decoupled and assumed fixed during the reactive power dispatch procedures; however, because of the effect of reactive power on real power, a re-schedule in the real power generation is allowed within given limits. The 32-bus CIGRE benchmark system is used to illustrate the proposed reactive power dispatch technique. The developed model is generic in nature and designed to be adopted by system operators in any electricity market structure, as demonstrated by its application to Ontario's grid considering its market rules for reactive power payments.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.005
GPT teacher head0.208
Teacher spread0.203 · 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 designTheoretical or conceptual
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

Citations35
Published2010
Admission routes2
Has abstractyes

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