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Record W2807828564 · doi:10.3934/energy.2018.3.505

Bulk system reliability impacts of forced wind energy curtailment

2018· article· en· W2807828564 on OpenAlexaff
Malhar Padhee, Rajesh Karki

Bibliographic record

VenueAIMS energy · 2018
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsWind powerReliability (semiconductor)Wind speedElectric power systemReliability engineeringSpillageEnvironmental scienceMarine engineeringAutomotive engineeringEngineeringComputer scienceMeteorologyPower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

With rapid growth of wind power in power systems, it becomes important to accurately model the behavior of wind, its interaction with conventional sources and also with other wind resources in order to conduct a realistic assessment of system reliability and benefits from wind energy utilization. At low wind penetration levels, all the wind energy generated is utilized to serve the load. However, at higher penetration levels, wind energy is spilled due to limitations in the ramping capability of the scheduled generating units and transfer capability of transmission lines. The benefits from wind energy are reduced as its spillage increases. Hence, accurate wind models should be developed to include forced wind energy curtailment in the reliability modelling, considering factors such as the system load level, unit dispatch order, ramp rates of the generating units and wind profile diversity between multiple wind farms. A new technique is proposed in this paper to create a comprehensive wind absorption capability model, and embed it in the composite generation and transmission system reliability model. The presented methodology to evaluate bulk system adequacy and wind energy benefits considering wind curtailment due to both the generation and transmission constraints is illustrated on an example system.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.714
Threshold uncertainty score0.850

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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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 designTheoretical or conceptual
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

Citations1
Published2018
Admission routes1
Has abstractyes

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