Impact Analysis of EV Preconditioning on the Residential Distribution Network
Bibliographic record
Abstract
Electric Vehicles (EV) are coming with a stupendous load demand that raises enough concerns for the power sector. The backlash of such increased demand is notable at the distribution side with different aspects of EV usage. During winter, EV users favor preconditioning their vehicles before leaving their houses, such as heating the cabin and battery compartment to make the operation of EVs more comfortable. Consequently, such behavior along with a higher penetration of level 2 smart chargers prompt the presence of a new peak in the residential load profile. This new unexpected peak that operators have to face can disturb the performance of the network. To forsee the impact of preconditioning, we simulate multiple scenarios to assess the network's quality metrics (voltage level and power losses). We expose that preconditioning poses risks on the network in its current state. Furthermore, we evaluate the competencies of network reconfiguration to handle the new imposed preconditioning demand. We find out that reconfiguration will be able to aid the performance of the network to an average EV penetration rate.
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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) | 0.001 | 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 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".