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Record W3035021481 · doi:10.3390/en13123083

Connecting Parking Facilities to the Electric Grid: A Vehicle-to-Grid Feasibility Study in a Railway Station’s Car Park

2020· article· en· W3035021481 on OpenAlexaff
Ruben Garruto, Michela Longo, Wahiba Yaïci, Federica Foiadelli

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsGridCharging stationElectric vehicleParking lotAutomotive engineeringVehicle-to-gridPhotovoltaic systemElectricityParking guidance and informationEngineeringGrid connectionSmart gridEnergy (signal processing)Transport engineeringComputer scienceElectrical engineeringCivil engineeringPower (physics)

Abstract

fetched live from OpenAlex

This study evaluates the impact of energy on the distribution network at the point of connection of an electric plant of a railway car parking facility in which charging points for electric vehicles (EVs) were installed. The objective is to identify a possible load curve of the simulated car park and, based on the principle of vehicle-to-grid (V2G) technology, to develop an appropriate algorithm. Such an algorithm explores the possibility of a two-way energy flow between the connected vehicles and the electricity grid, and performs a peak shaving of the load curve of the plant under examination in order to avoid absorption peaks, which are usually difficult to manage when using the distribution system operator (DSO). The work also presents the coupling with a photovoltaic system designed specifically for the car park. The study results are presented after a summary of the current state of development of electric mobility, describing the various types of EVs, the charging infrastructure, and the possible applications in smart grids (SGs).

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.706
Threshold uncertainty score0.693

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.230
Teacher spread0.212 · 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 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

Citations11
Published2020
Admission routes1
Has abstractyes

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