An Adaptive Charging Algorithm for Electric Vehicles in Smart Grids
Bibliographic record
Abstract
Integration of renewable energy sources and Electric Vehicles (EVs) into smart grids comes with significant challenges. The uncertainty of the short-term forecasted energy from renewable sources increases the variability of the net-load in the grid. Also, EVs' charging could exacerbate the load peak in the grid if charging is not coordinated. In this work, firstly, we study the impact of the variability of renewable sources on the short-term forecast of the net-load in the electric grid, and a model of the net-load forecast error is developed. Secondly, a novel online charging algorithm for EVs is proposed not only to shift EVs' load from the system peak period to more desirable period, but also to decrease the variability of the net-load in the grid. Simulation results show that our algorithm outperforms the traditional scheduling algorithms which optimize the overall load in the system based on short-term load forecast.
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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 | 0.000 |
| 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".