A Novel Online Charging Algorithm for Electric Vehicles Under Stochastic Net-Load
Bibliographic record
Abstract
In this paper, we propose a novel charging algorithm for electric vehicles (EVs) in smart grids. Unlike traditional charging methods, this algorithm is designed to exploit the flexibility of the EVs' load to absorb the unforeseen fluctuations in the net-load caused mainly by the intermittency of the renewable energy sources (RES) (wind energy). In this paper, we first formulate the problem with traditional charging algorithms in the presence of RESs. Second, we show that the overall energy consumed by the overall load in the system can be estimated ahead of time despite the stochastic behavior of the net-load. Third, a detailed description of our online algorithm shows how EVs' charging decision is taken by the utility server in real time - not ahead of time - according to the current situation of the net-load. Also, unlike most of the charging algorithms in literature, our proposed algorithm considers keeping the charging current constant, thus, imposing less technical and engineering requirements and complexity on the EVs' charging infrastructure. Finally, to test the performance of our online charging algorithm, a tool has been developed in Java to simulate our algorithm. A comprehensive performance evaluation of our algorithm against a traditional ahead-of-time charging algorithm shows clear improvements achieved by our algorithm in absorbing the unexpected fluctuations in the net-load caused mainly by the stochastic behavior of the produced wind power.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".