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Record W1535288697 · doi:10.5772/8354

Wind Power: Integrating Wind Turbine Generators (WTG’s) with Energy Storage

2010· book-chapter· en· W1535288697 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInTech eBooks · 2010
Typebook-chapter
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerTurbineEnvironmental scienceSteam turbinePumped-storage hydroelectricityWind hybrid power systemsEnergy storagePower (physics)Marine engineeringRenewable energyElectrical engineeringEngineeringAerospace engineeringDistributed generationPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Energy Storage is the missing link between wind driven power generation and delivering power in a sustainable manner that can be dispatched at times of high demand from the grid. Transmission systems that cover large territories such as in North America are particularly vulnerable, requires additional dedicated transmission and readily dispatcheable backup power systems. The installed capacity of Wind Turbine Generators (WTG’s) in the US and worldwide, while impressive, suffers from a low capacity factor of 30% or less due to the variability and intermittency of wind as the motive force. In 2007 the global installed capacity was 94 GW with a predicted capacity of 136 GW by 2010, 55% would be installed in Europe and 23 % (31 GW) in North America, these numbers could be exceeded, as the US already has over 29 GW installed capacity with 99 GW in planning in the next 10 years. The demand for electricity has considerable daily and seasonal variations and the maximum demand may only last for a few hours each year. As a result, some power plants are required to operate for short periods each year – an inefficient use of expensive plants. Without any additional storage above the present 2.5%, mainly PHS, of the installed base load in the USA, base loaded plants are being detrimentally cycled at higher frequency and the situation is further exacerbated by the latest growing demand for renewable energy such as wind energy. In the US, this capacity has now reached in excess of 29,000 MW [Fig 1] summarized by the American Wind Energy Association (AWEA) projects; in Canada the current 2800 MW projects under consideration or contract will grow to 7400 MW to meet energy objectives set for 2015. Installing larger wind farms, to cover the deficiency of a higher capacity factor, results in high costs per delivered kW/hr. This requires continued tax incentives to deliver “green” energy to the consumers. The full capability of the WTG is never realized, as at high wind speeds, some of the wind energy has to be “spilled” to maintain a smooth delivery profile. Technology improvements have not overcome the “wasted” capacity of these modern marvels except where Hydro or Pumped Hydro Storage (PHS) facilities are utilized. The Hydro power station can compensate for wind variability and intermittency while PHS provides energy storage and delivers power during high demand periods. Wind Energy Storage results in a much higher capacity factor, in effect reducing the cost of delivered kW/hrs., PHS amounts to less than 2.3 % of the current installed 1000 GW generating capacity and will decrease with the increasing addition of wind generation.

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), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.752
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0020.002
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 head0.009
GPT teacher head0.202
Teacher spread0.193 · 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