Challenges in Future Competition of Electric Vehicle Charging Management and Solutions
Bibliographic record
Abstract
In the foreseeable future, power grids will be managed largely with demand-side management (DSM) programs. With the growing population of electric vehicles (EVs) and the emergence of aggregators, DSM will surely introduce more intense competition to the markets. Since EV charging produces a large amount of time-flexible load in power systems, competition of its management could become a major game. This paper first formulates the game of EV charging management to describe this major form of the future DSM competition and then studies three challenges inherent in it: 1) inefficiency of Nash equilibria; 2) the game of chicken; and 3) cheating on private information. It is found that a central regulator is required to prevent these drawbacks. Solutions are proposed and a central governing procedure is also presented. The notion of the game of EV charging management is compatible with DSM programs that are able to schedule load flexibly over multiple time periods.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".