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Record W3046117817 · doi:10.1109/icc40277.2020.9148718

Decentralized Game-Theoretic Approach for D-EVSE based on Renewable Energy in Smart Cities

2020· article· en· W3046117817 on OpenAlexaff
Turki G. Alghamdi, Dhaou Said, Hussein T. Mouftah

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRenewable energyGame theoryComputer sciencePhotovoltaic systemDecentralised systemPower (physics)Distributed computingEnergy supplyStability (learning theory)Control (management)Energy (signal processing)EngineeringElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

In this paper, we address a decentralized management system based on noncooperative Game Theory (GT) for Electric Vehicles' (EVs') interplay with a Decentralized Electric Vehicle Supply Equipment (D-EVSE) located at the public supply station. Renewable energy production, such as photovoltaic energy (PV), is considered as the only power source for our D-EVSE. We propose a decentralized GT (D-GT) model aiming to optimize the EVs' interaction with the D-EVSE considering both EVs' satisfaction as well as the D-EVSEs' stability. Also, the D-GT model is used to choose the optimal available solution for EV charging or discharging processes that fulfill predefined constraints. Simulation results indicate that the proposed model can manage and control the interaction between EVs and D-EVSEs efficiently and effectively.

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: none
Teacher disagreement score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.183
Teacher spread0.174 · 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

Citations13
Published2020
Admission routes1
Has abstractyes

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