Plug-in electric vehicle charging demand estimation based on queueing network analysis
Bibliographic record
Abstract
Charging stations are critical infrastructure for the integration of plug-in electric vehicles (PEVs) in the future distribution systems. With a steadily increasing PEV penetration level, the PEV charging demands of charging stations are expected to constitute a significant portion of the total electric power demands. An accurate estimation of PEV charging demands is crucial for the planning and operation of future distribution systems. However, the estimation remains a challenging issue, as the charging demands of nearby charging stations are closely correlated to each other and depend on vehicle drivers' response to charging prices. The evaluation of charging demands is further complicated by the highly dynamic vehicle mobility, which results in random PEV arrivals and departures. In order to address these challenges, a BCMP queueing network model is presented in this paper, in which each charging station is modeled as a service center with multiple servers (chargers) and PEVs are modeled as the customers in the service centers. Based on the stationary distribution of the number of PEVs in each charging station, the statistics of PEV charging demands can be obtained. The analytical model is validated by a case study based on realistic vehicle statistics extracted from 2009 National Household Travel Survey and New York State Transportation Federation Traffic Data Viewer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".