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Record W2921760002 · doi:10.1109/tpwrs.2019.2905782

Recovery Risk Mitigation of Wind Integrated Bulk Power System With Flywheel Energy Storage

2019· article· en· W2921760002 on OpenAlexafffund
Saket Adhikari, Rajesh Karki, Prasanna Piya

Bibliographic record

VenueIEEE Transactions on Power Systems · 2019
Typearticle
Languageen
FieldEngineering
TopicPower System Reliability and Maintenance
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsReliability engineeringElectric power systemWind powerEnergy storageRenewable energyFlywheelEngineeringAutomotive engineeringComputer scienceEnvironmental sciencePower (physics)Electrical engineering

Abstract

fetched live from OpenAlex

With the increased uncertainty in the power system operation due to growing penetration of highly intermittent energy sources such as wind power, the need for the impact assessment of the renewable penetration on system operating risk and the quantification of benefits of using energy storage technologies is more than ever. A recovery-risk-analysis-based analytical framework for operating risk assessment of wind-integrated bulk power system following a major contingency disturbance is presented in this paper. Two new indices that quantify the recovery risk profile of the bulk power system and its load delivery points following major disturbances are introduced in this paper. The indices obtained from the proposed framework quantify the impact of increasing wind penetration on the system operating risk and the reliability benefits of using fast-responding energy storage system such as flywheel energy storage systems. The proposed methodology is illustrated through several case studies carried out in a test 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 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)0.001
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 score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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 head0.003
GPT teacher head0.159
Teacher spread0.156 · 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

Citations32
Published2019
Admission routes2
Has abstractyes

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