Plug-in Electric Vehicle Planning Toward DDPP Constrained by Electricity Grid Limitation
Bibliographic record
Abstract
Electric vehicle (EV) has alluded as a solution for CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2 </sub> emission reduction in the transportation sector. However, uncontrolled penetration of EV s considering power grid limitation will increase CO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> emission in the electricity sector. Hence, in this paper, a decomposed model of EV s planning is proposed to obtain the optimal penetration of EV s considering associated uncertainties. Moreover, a new bi-level charging/discharging control which considers both desires of the PEV s and the system operator is addressed in this paper. The results demonstrate that it is possible to increase the penetration of EV s up to 30% by 2025 while reducing the total load curtailment by 37% and the total emission by 28% compared to the baseline case with no supervisory EV charging/discharging control. The proposed planning problem is applied to Ontario's grid considering existing and projected plans of transmission and generation expansion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".