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Record W7098013587

Integrating the Energy Markets in North-America: Conditions Helping Large-scale Integration of Wind Power? By

2011· article· en· W7098013587 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Power System Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerElectric power systemIntermittencyRevenuePower (physics)Resource (disambiguation)Electricity generationOrder (exchange)Base load power plant
DOInot available

Abstract

fetched live from OpenAlex

The intermittency of wind resources may appear as an important handicap for integrating large-scale wind power in existing grids. However, in Canada and the United States, studies 1,2,3,4,5,6 show that coordination between interconnected power systems can be economically beneficial for both the wind power and electric utility industries if some power system management and market rules are applied. In order to avoid power shortage, our results show that the primary condition helping large-scale integration of wind power is related to changes in market structure rules. If the system is operated in an optimal coordinated manner, on a daily basis for example, the addition of wind power can better optimize the economic operation of the overall system, thereby hopefully increasing revenues from wind power projects, and decreasing costs for the utility and its rate payers. This paper first presents the main elements of the generation optimization model used to compute the benefits of integrating wind power in: a) a hydro-based system (using the largest power system in Canada as a test case; and b) a thermal-based system (using Vermont as a test case). Further, the authors discuss the conditions and rules needed in the generation model to alleviate operational limitations and constraints frequently associated with the intermittent nature of wind. These conditions include market rules, utility management practices, physical constraints like transmission capacities, load profiles, storage characteristics, wind resource patterns, wind power forecasting, and many other aspects.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.828
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.189
Teacher spread0.183 · 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
Published2011
Admission routes1
Has abstractyes

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