Uncertainties of EV Charging and Effects on Well-Being Analysis of Generating Systems
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.
Bibliographic record
Abstract
Power systems can make use of electric vehicle (EV) charging to improve system reliability. EV charging can serve the grid with charging interruption and vehicle-to-grid (V2G) capacity-injecting energy back into the system during the outage. However, the contribution of EVs is uncertain because they serve both the power system and the transportation sector. Scheduled EV charging can be affected either by failures of components such as charging facilities, or by human errors such as punctuality, rounding of time and errors in forecast of energy consumption. Moreover, with the introduction of the aggregator, the realization of EVs' grid services also plays an important role. This paper examines uncertainties of EV charging that can affect the EVs' capability to improve the system reliability. Methods are proposed to incorporate these uncertainties into generating system well-being analysis. Results show that the uncertainties identified directly affect EVs' contribution in the system well-being enhancement.
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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 it