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Record W1549726720 · doi:10.1002/etep.153

Assessing mid‐continent area power pool capacity adequacy including transmission limitations

2007· article· en· W1549726720 on OpenAlexaff
A.A. Chowdhury, B.P. Glover, L.E. Brusseau, S. Hebert, F. Jarvenpaa, A. Jensen, K. Stradley, H. Turanli, G.E. Haringa

Bibliographic record

VenueEuropean Transactions on Electrical Power · 2007
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsManitoba Hydro
Fundersnot available
KeywordsReliability engineeringReliability (semiconductor)Electric power systemEnvironmental scienceTransmission (telecommunications)Margin (machine learning)Transmission systemComputer sciencePower (physics)EngineeringTelecommunications

Abstract

fetched live from OpenAlex

Abstract This paper presents the results of a multi‐area generating capacity adequacy assessment performed by the Composite System Reliability Working Group (CSRWG) for the Mid‐Continent Area Power Pool's (MAPP's) United States (US) thermal system. The impact of transmission resource limitations within the MAPP region on the system reserve margin has been studied. In addition, the generating unit forced outage rate uncertainty, extreme hot summer loading conditions and the load forecast uncertainty are explicitly modelled in the study. The basic objective of this study was to determine the Reserve Capacity Obligation (RCO) for the MAPP‐US thermal system for the years 2003, 2006, 2009 and 2012. Similar studies have been performed in 1991 and 1994, and one of the purposes of this study was to determine whether or not the recommendations from the previous studies were still valid. The results of the study confirmed the current RCO level for the MAPP‐US thermal system. Copyright © 2007 John Wiley & Sons, Ltd.

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)0.004
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 score0.017
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.040
GPT teacher head0.249
Teacher spread0.209 · 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

Citations10
Published2007
Admission routes1
Has abstractyes

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