Electric vehicle aggregator/system operator coordination for charging scheduling and services procurement
Bibliographic record
Abstract
Summary form only given. In response to the need for the decarbonization of the transport sector, it is expected that large fleets of electric vehicles (EVs) will constitute an important share of the electricity demand. This evolution is likely to be accompanied by a parallel evolution of the electricity supply business with the deployment of smart grid technologies. As a consequence, it is expected that demand will feature higher potential for communication and control, which will enable its active participation in the daily operational planning of power systems. In particular, EVs being equipped with a battery can both defer their demand or inject electricity back into the system. However, to achieve volumes that can have an impact on the system, these demands need to be aggregated and operated as an ensemble. This paper proposes the necessary adaptations to include the input of EV aggregation to electricity markets. This permits the scheduling of EV charging and services in coordination with the system operator thus enhancing the power system's efficiency and security while reducing its environmental impact. Results show that the EVs penetration levels that the system would be able to absorb without requiring expansion of the supply side, are significantly increased when coordination over their charging schedule is performed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.077 | 0.012 |
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".