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Record W2786490471 · doi:10.1109/epec.2017.8286188

A linear programing model for coordinated low-carbon generation and transmission expansion planning

2017· article· en· W2786490471 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsRenewable energyMATLABLinear programmingElectricity generationCarbon fibersComputer scienceReduction (mathematics)Transmission (telecommunications)Mathematical optimizationEnergy (signal processing)Electric power systemProcess (computing)Transmission systemPower (physics)EngineeringAlgorithmTelecommunicationsElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

This paper presents a linear programming model for coordinated generation and transmission expansion planning to move towards a low-carbon power system. Uncertainties of renewable energy resources have been considered in the developed model. Moreover, different carbon emission reduction policies have been taken into consideration and their effect on the system planning has been investigated. The effects of possible energy trades, as well as energy resources expansion limitations, on the planning process and the anticipated emission reductions have also been investigated. Numerical results have been developed in MATLAB environment to test the performance of the proposed model.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.419

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 head0.263
Teacher spread0.235 · 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

Quick stats

Citations11
Published2017
Admission routes1
Has abstractyes

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