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Record W2018237604 · doi:10.1109/iwcmc.2012.6314321

A two-way communication scheme for vehicles charging control in the smart grid

2012· article· en· W2018237604 on OpenAlexafffund
Jihene Rezgui, Soumaya Cherkaoui, Dhaou Said

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversité de Sherbrooke
FundersNational Research Council Canada
KeywordsSmart gridReservationComputer scienceElectricityElectric vehicleGridWirelessVehicle-to-gridScheme (mathematics)Mains electricityProcess (computing)Computer networkTelecommunicationsEngineeringElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

The smart grid is a new concept of electricity supply operation and management that will enable consumers and utilities to better control the electricity usage. This is possible because of the two way electricity and information communication between all nodes in the grid. For Electric Vehicles (EVs) travelling on the road, and because of the necessary battery charging times, there is a need for wireless communication between the EVs and the Electric Vehicle Supply Equipment (EVSEs) (charging stations) to discover the availability and make pre-reservations of charging time slots. In this paper, we introduce a new communication protocol between EVs and EVSEs that allows a reliable reservation process. The scheme, called Reliable Broadcast for EV Charging Assignment (REBECA) processes information about electricity usage in EVSEs and allows to reserve charging time slots for vehicles. REBECA also takes into account balancing energy usage between EVSEs while minimizing the latency time of EVs. Simulations results show the effectiveness of REBECA scheme.

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 score0.709
Threshold uncertainty score0.215

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 head0.009
GPT teacher head0.222
Teacher spread0.213 · 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

Citations21
Published2012
Admission routes2
Has abstractyes

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