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Record W3125614888 · doi:10.22004/ag.econ.37044

Wind Integration into Various Generation Mixtures

2007· preprint· en· W3125614888 on OpenAlexafffund
Jesse D. Maddaloni, Andrew Rowe, G. Cornelis van Kooten

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEngineering
TopicIntegrated Energy Systems Optimization
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsTonneEnvironmental scienceWind powerElectricity generationRange (aeronautics)Natural gasElectric power systemOperating costWaste managementEngineeringPower (physics)Electrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

A load balance model is used to quantify the economic and environmental effects of integrating wind power into three typical generation mixtures. System operating costs over a specified period are minimized by controlling the operating schedule of existing power generating facilities for a range of wind penetrations. Unlike other studies, variable generator efficiencies, and thus variable fuel costs, are taken into account, as are the ramping constraints on thermal generators. Results indicate that system operating cost will increase by 15% to 110% (pending generation mixture) at a wind penetration of 100% of peak demand. Results also show that some mixtures will exhibit cost reductions on the order of 13% for moderate wind penetrations and high wind farm capacity factors. System emissions also decrease by 13% to 32% (depending on generation mixture) at a wind penetration of 100%. This leads to emission abatement costs in the range of $65 per tonne-CO2e for coal dominated mixtures, but $450 per tonne-CO2e for hydro dominated mixtures. For natural gas dominated mixtures, the introduction of wind power may well be beneficial overall.

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 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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
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 score0.311
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0020.003
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 head0.020
GPT teacher head0.278
Teacher spread0.258 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2007
Admission routes2
Has abstractyes

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