Integrated V2G, G2V, and Renewable Energy Sources Coordination Over a Converged Fiber-Wireless Broadband Access Network
Bibliographic record
Abstract
In this paper, an integrated vehicle-to-grid, grid-to-vehicle, and renewable energy sources (IntVGR) coordination algorithm is proposed. The focus of this work is to provide a multidisciplinary study on implementing the proposed IntVGR scheme over a broadband fiber-wireless communications infrastructure by co-simulating both power and communications perspectives. For the power systems perspective, results show that the scheme is able to achieve a 21% reduction in peak demand compared to uncontrolled charging, and a better performance in flattening the overall demand profile and maintaining network constraints in comparison to a benchmark scenario. The scheme has also been demonstrated to successfully coordinate PEVs to take maximum utilization of local renewable energy. For the communications perspective, the measured upstream traffic for executing the proposed IntVGR scheme on a residential area of 342 households is found to be 1-2 Mbps with an end-to-end latency level of 1 ms. The scheme has also been validated from both perspectives in a sensitivity analysis with a higher PEV adoption rate.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".