A New Framework for Plug-In Electric Vehicle Charging Models Supported by Solar Photovoltaic Energy Resources Un nouveau cadre pour les modèles de recharge du véhicule électrique rechargeable supporté par des ressources d’énergie solaire photovoltaïque
Bibliographic record
Abstract
This article proposes a new framework for modeling plug-in electric vehicle (PEV) charging demand supported by solar photovoltaic (PV) energy resources in a distribution system. The proposed work focuses on modeling the stochastic nature of both PEV loads and PV generation while considering the effect of the temporal-spatial characteristics of the driver's behavior, as well as solar irradiation and temperature. A trip chain, based on the Markov Chain Monte Carlo process, is developed to properly model PEV daily driving activities and the PV uncertainty. Charging facilities are assumed available at home, work, and fast-charging stations, having charging levels of 3.7, 6.6, and 50 kW, respectively. The proposed framework is examined, considering the National Household Travel Survey global data, as well as the city of Buffalo and New York state. The impact of varying the penetration levels of PEV and PV resources is also investigated. This work strengthens the proposed models in the literature by integrating the temporal-spatial characteristics of PEV charging demand into PV stochastic models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".