MétaCan
Menu
Back to cohort
Record W4200493364 · doi:10.1049/gtd2.12269

Mitigation of the mutual dynamic interactions between a direct‐current fast charging station and its host distribution grid

2021· article· en· W4200493364 on OpenAlexaff
Mostafa M. Mahfouz, Reza Iravani

Bibliographic record

VenueIET Generation Transmission & Distribution · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHost (biology)GridCurrent (fluid)Distribution gridComputer scienceDistribution (mathematics)Electrical engineeringEngineeringMathematicsGeologyBiologyEcologyGeodesy

Abstract

fetched live from OpenAlex

Abstract The fast and intermittent power changes in a direct‐current fast charging (DCFC) station impose adverse dynamic impacts on the station's host grid, particularly a weak AC distribution grid. This paper (i) investigates the impacts of the electric vehicle (EV) conventional DCFC station on its host distribution power system, and (ii) shows that the station can be enhanced by a battery energy storage system and an appropriate control strategy to mitigate the mutual dynamic interactions between the station and its host grid. The enhanced DCFC station is based on a variable‐voltage, common DC‐bus architecture which masks the station's internal dynamics, including rapid and intermittent EV charging processes, from the grid. Thus, it can be interfaced to a host grid, regardless of short‐circuit level, X / R ratio, and power capacity. The focus is also on the dynamic immunity of the DCFC facility to potential disturbances in its host weak‐grid. The reported studies are based on detailed time‐domain simulation of two DCFC stations in the PLECS software platform.

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 score0.340
Threshold uncertainty score0.645

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 head0.008
GPT teacher head0.230
Teacher spread0.222 · 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
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueIET Generation Transmission & DistributionSame topicElectric Vehicles and InfrastructureFrench-language works237,207