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Record W2905907081 · doi:10.1109/pesgm.2018.8585844

Practical Modeling of Flywheel Energy Storage for Primary Frequency Control in Power Grids

2018· article· en· W2905907081 on OpenAlexaff
Dario Peralta, Claudio A. Cañizares, Kankar Bhattacharya

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAutomatic frequency controlFlywheel energy storageElectric power systemEnergy storageFlywheelFlexibility (engineering)Frequency regulationRenewable energyWind powerComputer sciencePower (physics)Power controlEngineeringControl engineeringAutomotive engineeringElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

With significant integration of renewable energy sources (RES), particularly wind power, there is a need for fast regulation to counteract the effect of frequency variation through primary frequency control (PFC). Flywheel energy storage (FES) technologies are particularly important, because of their fast response capability and high flexibility of operation. However, not much work has been carried out on properly modeling the FES to analyze its performance at the power system level. Therefore, the focus of this paper is on developing a dynamic model for frequency regulation studies, practical and useful for system operators, of a FES system connected to a power system to provide PFC, and study its effects on system stability. The proposed practical model is tested using a commercial simulation package, and compared with respect to a previously reported test system equipped with a high-capacity wind generator, which creates significant frequency and voltage fluctuations, demonstrating its application for solving PFC problems in power networks.

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)0.000
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: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.008
GPT teacher head0.217
Teacher spread0.209 · 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

Citations19
Published2018
Admission routes1
Has abstractyes

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