Active Management of Electric Vehicles Acting as Distributed Storage
Bibliographic record
Abstract
Abstract In the European Union (EU), the greenhouse emissions from the transportation sector increased around 36% since 1990, which degraded the environmental quality. This sector, owing to its oil dependency, is responsible for around a quarter of EU greenhouse emissions, and the road transportation represents about 20% of the total CO2emissions in EU. Moreover, concerns such as the dependency on oil supply and the foreseen prices increase during this century have motivated a wide range of policy and technological measures for the transportation sector. A large part of these measures were to incentivize the electric vehicle (EV) adoption, which is one element with great potential to decarbonize the transportation sector and decrease its oil dependency. This chapter describes relevant methodologies to actively manage EV charging/discharging (as distributed storage devices) to achieve different goals, such as avoid grid congestion, the EV participation in primary frequency control, and the coordination of EV charging with renewable generation. To contextualize the methodologies described, a brief state of the art in active management functionalities for EV is provided. Some of the results obtained with the described approaches are also presented to demonstrate their overall performance.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".