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Development and Validation of Low Voltage Grid Benchmark System for Charging Control Investigation

2025· article· en· W4414956647 on OpenAlexaff
Artjoms Obushevs, Roger Glarner, Alexander Mächler, Petr Korba, Sony Susan Varghese, Nayeem Ninad, Kai Heussen, Oliver Gehrke, Mazheruddin Syed, L. Pellegrino

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEngineering Applied Research
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsBenchmark (surveying)GridControl systemLow voltageElectric vehicleControl (management)Component (thermodynamics)Photovoltaic system

Abstract

fetched live from OpenAlex

The VEHICLE project (Validation of Benchmark System for Charging Control Investigation) aimed to develop and validate a benchmark system for investigating electric vehicle charging control strategies in real-time environments. The study integrates Simulink-based models of low voltage grids with a high share of electric vehicle charging stations and photovoltaic systems into a real-time digital simulation framework. Conducted through a series of experiments at DTU SYSLAB, the research evaluates various grid topologies and component interactions. The developed system enables real-scenario investigations, reducing reliance on assumptions in traditional simulations and accelerating the validation of EV control algorithms. The study provides a foundation for advancing digital grid modelling and control strategies for modern active distribution networks.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.213
Teacher spread0.205 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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