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Record W2985730928 · doi:10.1109/isgt-la.2019.8895470

On Effects of PEVs in Islanded Microgrids Resilience

2019· article· en· W2985730928 on OpenAlexaff
Alesssandro G. Fiorese, Yuri R. Rodrigues, Antônio Carlos Zambroni de Souza, Maurício Campos Passaro

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of British ColumbiaUniversity of British Columbia Hospital
Fundersnot available
KeywordsMicrogridLoad SheddingResilience (materials science)Computer scienceGridDistributed generationMode (computer interface)Control (management)Plug-inControl engineeringReliability engineeringElectric power systemRisk analysis (engineering)EngineeringBusinessPower (physics)Renewable energy

Abstract

fetched live from OpenAlex

The continuous developments toward active distribution systems have been providing the necessary conditions for islanded microgrids operation. In this mode, the islanded regions are expected to sustain the system operation within adequate limits, locally performing several control actions previously assumed by the main grid. Among these controls, the guarantee of generation/demand balance is one of the most critical aspects, which due to the limited amount of generation capacity can lead to massive load shedding. In this perspective, this paper seeks to evaluate the effects of plug-in electric vehicles (PEVs) in the improvement of islanded microgrids resilience. For this, a holistic methodology is proposed to determine whether the implementation of sophisticated controls for the use of PEVs as flexible resources render actual benefit for the islanded network. Simulations are held in the IEEE 34-bus test system considering modifications to represent a microgrid environment. The results indicate that the application of PEVs as flexible resources can significantly enhance microgrids resilience in the mitigation of load shedding.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.170

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 head0.001
GPT teacher head0.161
Teacher spread0.161 · 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 designBench or experimental
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

Citations2
Published2019
Admission routes1
Has abstractyes

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