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Record W7097522794

40 | Fourth Quarter 2013 Politics of Power in China: Institutional Bottlenecks to Reducing Wind Curtailment Through Improved Transmission

2015· article· en· W7097522794 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerChinaRenewable energyQuarter (Canadian coin)ElectricityNameplate capacityPower grid
DOInot available

Abstract

fetched live from OpenAlex

Grid-connected wind capacity has increased thirty-fold in China in the six years since the Renewable Energy Law was passed. At the end of 2012, China led the world in cumulative wind installations with 63 gigawatts (GW), while approved projects planned or under construction exceeded 44 GW (He, 2013). Despite the lead in capacity, however, China generated 30 % less electricity from wind than the United States, which was a close second in terms of total installations. Reduced capacity factors have been attributed to high amounts of forced curtailment, which reached as high as 50 % in some regions last year. The causes of curtailment are manifold: high penetrations of wind in provinces far from load centers, inflexibility of the coal-heavy generation mix, and institutional barriers owing to incomplete power deregulation. To address these shortfalls and other chronic power challenges, China’s grid companies propose to significantly expand long-distance ultrahigh-voltage (UHV) interconnections as well as strengthen interprovincial and intraprovincial ties. These will report-edly double wind utilization by 2020 (State Grid, 2010). However, institutional hurdles to better integrat-ing wind, ranging from an intense debate within China over the future structure of the grid to inflexible transmission operation and pricing, threaten to delay or derail benefits of interconnection. Overview of Current and Proposed Transmission Network

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)0.000
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 score0.728
Threshold uncertainty score0.594

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 head0.009
GPT teacher head0.223
Teacher spread0.213 · 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 teacher head, 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

Citations0
Published2015
Admission routes1
Has abstractyes

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