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Record W2098153837 · doi:10.1109/tdc.2004.1432514

Resource adequacy assessment considering transmission and generation via market simulations

2005· article· en· W2098153837 on OpenAlexaff
Huawei Chao, Fangxing Li

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsABB (Canada)
Fundersnot available
KeywordsUnavailabilityReliability (semiconductor)Transmission (telecommunications)Electricity marketResource (disambiguation)Computer scienceReliability engineeringEnvironmental economicsTransmission systemElectric power systemInvestment (military)Energy marketElectricityPower (physics)TelecommunicationsEngineeringEconomicsComputer network

Abstract

fetched live from OpenAlex

Growth in electricity demand with lack of investment in new transmission facilities has led to the formation of load pockets with long congestion hours over transmission bottlenecks. When assessing resource adequacy with reference to the load pockets, one needs to include both generation resources and power delivery systems to capture their impacts on system reliability and market economics at the delivery points. This paper presents a resource adequacy analysis approach to evaluate reliability as well as economic impact in a competitive energy market. Unavailability of generating units and transmission facilities is considered in market simulations to assess the overall adequacy of load-serving ability in a load pocket. Effects of transmission are illustrated through a market simulation analysis of the NYCA system based on publicly available transmission, generation, and demand data.

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)0.009
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 score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.012
GPT teacher head0.232
Teacher spread0.221 · 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

Citations1
Published2005
Admission routes1
Has abstractyes

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