Smart Operation of Electric Vehicles With Four-Quadrant Chargers Considering Uncertainties
Bibliographic record
Abstract
Given the expected impact of electric vehicle (EV) charging on power grids, this paper presents a novel two-step approach for the smart operation of EVs with four-quadrant chargers in a primary distribution feeder, accounting for the uncertainties associated with EVs, and considering the perspectives of both the utility and the EV owners. In the first step of the proposed approach, the mean daily feeder peak demand and corresponding hourly feeder control schedules, such as taps and switched capacitor setpoints, considering the bidirectional active and reactive power transactions between EVs and the grid, are determined. A nonparametric bootstrap technique is used, in conjunction with a genetic algorithm-based optimization model, to account for EV uncertainties and discrete variables. In the second step, the maximum possible power that can be given to connected EVs at each node, while providing active and/or reactive power to maintain the peak demand value and corresponding feeder dispatch schedules defined in the first step, is computed every few minutes in a way which is fair to the EVs. The proposed approach is validated using the distribution feeder model of a real primary feeder in Ontario, Canada, considering significant EV penetration levels. The results show that the proposed approach could be implemented in practice to properly operate EVs, satisfying feeder, and peak demand constraints, which would be better than the business-as-usual practice or a popular heuristic method in terms of number of tap operations, system peak demand, and voltage regulation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".