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Record W2098803239 · doi:10.1109/lescpe.2002.1020662

Fast acting load control

2003· article· en· W2098803239 on OpenAlexaff
Ke Xiong, William Rosehart

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsElectric power systemComputer scienceGenerator (circuit theory)Stability (learning theory)ElectricityElectric power transmissionControl (management)ImplementationElectricity marketTransmission systemControl systemControl theory (sociology)Transmission (telecommunications)Power (physics)Control engineeringEngineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

As open access market principles are applied to power systems, stability margins have been steadily decreasing. This has lead to events such as rolling black-outs in several power systems, for example, California, and has generally resulted in increased electricity prices. A novel approach to reduce the tension in electrical systems is to establish a fast acting load control program for price and system stability. This differs from traditional approaches where additional transmission lines and generators need to be installed. Using this approach, load can be modeled and controlled similar to a generator, proving spinning reserve. The paper reviews existing implementations of fast acting load control. Furthermore, in this paper, two Lagrangian based approaches are used to determine optimal locations for fast acting load control. The approaches are formulated to consider economic cost and stability limits. The effects of applying these approaches are analyzed using a 30-bus system, which is derived from the IEEE 30-bus test system.

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 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 score0.996
Threshold uncertainty score0.822

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)0.0010.000

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 head0.006
GPT teacher head0.192
Teacher spread0.185 · 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.

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
Published2003
Admission routes1
Has abstractyes

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