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Record W2153618882 · doi:10.1109/tpwrs.2009.2039652

Application of a Joint Deterministic-Probabilistic Criterion to Wind Integrated Bulk Power System Planning

2010· article· en· W2153618882 on OpenAlexaff
R. Billinton, Yi Gao, Rajesh Karki

Bibliographic record

VenueIEEE Transactions on Power Systems · 2010
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsProbabilistic logicElectric power systemWind powerReliability engineeringReliability (semiconductor)Mathematical optimizationComputer scienceEngineeringOperations researchPower (physics)MathematicsElectrical engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The basic objective of bulk power system planning is to develop the system as economically as possible while maintaining an acceptable level of service reliability. The traditional methods used by system planners to maintain acceptable bulk power system reliability are challenged in the present move to incorporate higher wind power penetration levels. Combining deterministic considerations with probabilistic assessment in order to evaluate the quantitative system risk and conduct bulk power system planning has therefore become increasingly necessary and important in recent years. This paper examines the capacity value of wind generation using various approaches and the utilization of this value under the deterministic N-1 criterion. The application of a joint deterministic-probabilistic criterion for bulk system expansion planning in wind integrated systems is presented. The application of the conventional deterministic N-1, the basic probabilistic and the joint deterministic-probabilistic criteria is illustrated in a wind integrated test system in this paper.

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)0.007
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.010
GPT teacher head0.220
Teacher spread0.210 · 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

Citations43
Published2010
Admission routes1
Has abstractyes

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