Impact Analysis of Level 2 EV Chargers on Residential Power Distribution Grids
Bibliographic record
Abstract
Large scale Electric Vehicles (EV) penetration is coming with a stupendous energy demand that raises much concerns in the power sector. The impact of this demand is mostly notable at the distribution side as an outcome of EV users home charging preferences. Currently, most EVs charge their batteries through level 1 charger at home. However, the shorter charging times and the declining prices of level 2 chargers favor a switch from level 1 into level 2 chargers at residential premises. As a ramification, this will cause a lamentable peak in the residential load profile, and consequently power utilities will face the impact of elevated number of level 2 chargers with uncontrolled EV charging. To foresee these consequences, using the IEEE-33 Bus radial distribution system, we build a discrete event simulator and present real-life assessment of different EV penetration rates with various level 2 charger adoption rates. We expose that 50% EV penetration along with 50% level 2 chargers deployment may create an undesirable situation on the distribution network. Furthermore, we simulate EV users' charging behavior over different pricing techniques. The collected results show that available pricing techniques cannot maintain the voltage level over minimum desired threshold especially during peak times.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".