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Record W2024825567 · doi:10.1109/tpwrs.2012.2205022

Adequacy Assessment Considerations in Wind Integrated Power Systems

2012· article· en· W2024825567 on OpenAlexaff
R. Billinton, Rajesh Karki, Yi Gao, Dange Huang, Po Hu, Wijarn Wangdee

Bibliographic record

VenueIEEE Transactions on Power Systems · 2012
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsBC Hydro (Canada)Manitoba HydroUniversity of Saskatchewan
Fundersnot available
KeywordsWind powerElectric power systemReliability engineeringElectricity generationWind speedEngineeringElectric power transmissionRange (aeronautics)Computer sciencePower (physics)Electrical engineeringMeteorologyAerospace engineering

Abstract

fetched live from OpenAlex

There is a wide range of possible data representations, models and solution techniques available when conducting adequacy assessments of wind integrated generation or composite generation and transmission systems. This paper presents some of the basic factors and procedures that need to be considered when conducting wind integrated system adequacy assessment. Focus is placed on possible wind speed data models, wind energy conversion system models and their application in generation and bulk system adequacy evaluation. A series of studies is presented using two published test systems, the RBTS and the IEEE-RTS. These studies illustrate the effects of wind farm correlation on the determination of wind power capacity credit indices and on general adequacy assessment in generating and bulk electric systems. The impacts on the system well-being indices of adding wind power to a bulk electric system and the effects on the adequacy of a wind integrated system from energy storage obtained using hydro generation are examined by application to the IEEE-RTS.

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)0.004
metaresearch head score (Gemma)0.025
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 score0.006
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.014
GPT teacher head0.238
Teacher spread0.223 · 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

Citations113
Published2012
Admission routes1
Has abstractyes

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