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Record W2114404029 · doi:10.1109/pes.2007.385800

Wind Based Distributed Generation; Uncertainties and Planning Obstacles

2007· article· en· W2114404029 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

VenueIEEE Power Engineering Society General Meeting · 2007
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWind powerWind speedComputer scienceElectricity generationEnergy storageDistributed generationElectric power systemEnergy (signal processing)Reliability engineeringRenewable energyEnvironmental scienceMeteorologyPower (physics)EngineeringElectrical engineering

Abstract

fetched live from OpenAlex

As more wind energy is connected to utility systems, it becomes important to understand and manage the impact of wind generation on system operations. Recent studies and simulations provide a better understanding of these impacts, and with this knowledge, progress is now being made in developing the tools and methods to minimize costs and operate reliably with higher levels of wind generation and lower level of uncertainty. In order to reduce the uncertainty in the wind generation, and facilitate the introduction of wind energy in the utility system as power capacity instead of energy source, this paper proposes a novel integrated wind DG with energy storage system. Rayleigh probability density function is used to model the wind speed during each month in the year, from which the average power and capacity factor can be estimated, which is an indication of the uncertainty of the system. Depending on this information during the year, the appropriate energy storage system can be selected.

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 categoriesMeta-epidemiology (narrow)
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 score0.381
Threshold uncertainty score1.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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