Dynamic pricing model for EV charging-discharging service based on cloud computing scheduling
Bibliographic record
Abstract
Electric Vehicle (EV) and smart grids have gained much popularity in recent years. This has been enabled by the high increase of EVs on roads; however, this may lead to a significant impact on the power grids. In order to keep EVs far from causing peaks in power demand during the day, it is important to perform an intelligent scheduling for EVs charging and discharging by including metrics, such as price and demand-supply curve. In this paper, we propose a dynamic pricing model for EV charging (i.e., grid-to-vehicle, G2V) and discharging (i.e., vehicle-to-grid, V2G) services, in order to reduce the peak load. Our proposed model uses cloud computing architecture to schedule EV requests. We formulate our problem as a linear optimization problem and solve it using new algorithms for charging and discharging. To the best of our knowledge, this is the first paper that proposes a model that tries to solve all the aforementioned issues. The extensive simulations proved that our proposed pricing model, based on cloud computing and EVs interactions, optimizes the energy load during peak hours and satisfies EVs users and micro grid constraints.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".