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Record W2133082743 · doi:10.1049/cp:20020069

Assessing the reactive power support requirements for generators in a competitive electricity market

2002· article· en· W2133082743 on OpenAlexaff
Yijie Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicOptimal Power Flow Distribution
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectricity marketElectricityElectricity generationAC powerElectricity retailingComputer sciencePower (physics)Electrical engineeringEngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

Under the new competitive electricity market structure, the active power provided by the generator has become a commodity, entering the transmission grid with few restrictions. It is known that the transmission of active power needs the support of reactive power and the amount of support is a function of the active power transmitted. Our studies found that each generator does need a certain amount of reactive power to support the transmission of its own active power. The amount needed is a function of the generator location, the amount of active power transmitted and others. Any reactive power beyond this amount is purely used as reactive power support and should be remunerated. The main objective of this paper is to present an investigation on the problem of assessing the reactive power support of the generator. In this paper, we propose the concept of minimum VAr requirement (Q/sub min/) for generators. A practical approach to determine the Q/sub min/ has also been developed and tested in a 5-bus system and a medium-sized system. Promising test results verify the validity of the proposed method.

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)0.003
metaresearch head score (Gemma)0.015
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: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.271
Teacher spread0.247 · 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

Citations2
Published2002
Admission routes1
Has abstractyes

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