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Record W3173789861 · doi:10.24018/ejece.2021.5.3.329

Dynamic Modelling of a Solar Energy System with Vehicle to Home and Vehicle to Grid Option for Newfoundland Conditions

2021· article· en· W3173789861 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueEuropean Journal of Electrical Engineering and Computer Science · 2021
Typearticle
Languageen
FieldEngineering
TopicElectric Vehicles and Infrastructure
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsVehicle-to-gridGridAutomotive engineeringBattery (electricity)EngineeringSmart gridMode (computer interface)Photovoltaic systemMATLABElectrical engineeringPower (physics)Electric vehicleComputer science

Abstract

fetched live from OpenAlex

The dynamic modelling of a solar energy system with vehicle to home (V2H) and vehicle to grid (V2G) options for Newfoundland conditions is discussed in this paper. A site (13 Polina Road) was chosen in St. John's, Newfoundland, Canada. An optimized system was built for the chosen site using BEopt, Homer, and MATLAB software’s to meet the house's energy demand. Furthermore, smart current sensors installed in the house are used to incorporate the V2H and V2G concepts. The Nissan Leaf's battery is used to supply household loads in V2H operation mode when the power supplied by the PV panel and the storage energy in the inhouse battery is less than the load's energy demand. In V2G mode, the vehicle is only linked to grid. Along with the simulation results, detailed system dynamic modelling is also presented. There are nine different system control modes that are proposed and simulated.

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.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.542
Threshold uncertainty score0.352

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 head0.004
GPT teacher head0.168
Teacher spread0.164 · 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