A game theoretic approach for plug-in hybrid electrical vehicle load management in the smart grid
Bibliographic record
Abstract
Plug in hybrid electric vehicles (PHEVs) have been recently introduced as environmentally friendly and fuel economic vehicles. It is believed that they will be widely adopted in the coming years, which puts the resilience of the electric grid in question. The large amount of electricity drawn by simultaneously charging multiple PHEVs can cause electric overloads which will either destroy grid components or shorten the equipment's life expectancy. In this paper, we propose a smart PHEVs charging algorithm to efficiently manage random and uncontrolled PHEV charging. The algorithm is based on a modified regret matching procedure that leads to a set of correlated equilibrium. A game theoretic approach is used to formulate the PHEV charging problem into a game where the players are the PHEVs and the strategies are their charging schedules. The proposed algorithm preserves users' privacy in terms of charging schedules and does not incur much overhead on the system. Simulation results show that proposed algorithm achieves high saving in terms of the total energy costs, individual costs, and flattens the overall charging load. The algorithm is also scalable and converges in an acceptable time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".