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Record W2125330196 · doi:10.1109/ccece.2011.6030452

Performance evaluation of a hybrid wind-diesel-compressed air energy storage system

2011· article· en· W2125330196 on OpenAlexaff
Hanif Sedighnejad, John E. Quaicoe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCompressed air energy storageEnergy storageRenewable energySizingAutomotive engineeringWind powerPumped-storage hydroelectricityComputer data storageCompressed airElectric power systemHybrid systemDiesel fuelHybrid powerComputer scienceEnvironmental sciencePower (physics)Electrical engineeringDistributed generationEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

One of the main concerns of hybrid power system is the fluctuation of voltage and frequency of the system due to variation in the load and renewable power. A number of different types of energy storage techniques, which are theoretically and operationally available, have been proposed to remedy such fluctuations. One long-term storage option is compressed air. In this paper, the effects of the working pressure and system design of a typical compressed air storage system on the overall performance of a hybrid system is investigated. The model of a hybrid wind-diesel-compressed air generating system is developed and simulated using MATLAB. It is shown that the energy storage system and the proper sizing of the same play a vital role in increasing the renewable energy penetration.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.178
Teacher spread0.164 · 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 source (direct Gemma or distilled Codex), 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

Citations13
Published2011
Admission routes1
Has abstractyes

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