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Record W2036205453 · doi:10.1109/pesmg.2013.6672726

Quantifying operational flexibility of VSC-HVDC lines and SVCs

2013· article· en· W2036205453 on OpenAlexaff
Amritanshu Pandey, Gabriela Hug

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsFlexibility (engineering)Electric power systemReliability engineeringReliability (semiconductor)Economic dispatchMonte Carlo methodComputer scienceMarkov chainPower flowPower (physics)Control theory (sociology)Control engineeringEngineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

Introducing power flow control capabilities into the electric power system increases the operational flexibility of the overall system. Quantifying such flexibility is an important aspect in the determination of the value which such devices add to the system. In this paper, Monte Carlo Markov Chain simulations are employed to study the benefits achieved by HVDC lines in combination with Static Var Compensators. In a first approach, the objective is to maximize the achievable power transfer whereas in a second approach, an economic dispatch is determined for fixed loading scenarios. Simulations are carried out for the IEEE Reliability Test System showing that power flow control significantly enlarges the space of feasible generation settings and thereby reduces generation dispatch costs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.662
Threshold uncertainty score0.246

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 head0.028
GPT teacher head0.245
Teacher spread0.217 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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