Design of Optimal Incentives for Smart Charging Considering Utility-Customer Interactions and Distribution Systems Impact
Bibliographic record
Abstract
With increasing environmental concerns the penetration of plug-in electric vehicles (PEVs) is expected to increase in the future. Such electrification of the transportation sector will impact the distribution grid adversely; however, PEV smart charging strategies can help mitigate the impacts. In this paper, a PEV smart charging approach is proposed where the charging loads are controlled and incentivized by the local distribution company (LDC) for every unit of energy controlled. A novel framework is proposed to determine the optimal participation of PEVs in the smart charging program and optimal incentives paid by the LDC to PEV customers, such that both parties are economically benefited. The proposed framework models the relationship between customers' participation and incentives offered by the LDC. The relationship between the expected investment deferral and hence the economic benefits from smart charging participation are considered as well. Monte Carlo simulation is used to simulate the uncertainty of demand, electricity market price, drivers' behavior, PEV market share, and charging level share. The proposed framework is tested on the 33-bus distribution system and the results show that the proposed approach is effective and can economically benefit both LDC and smart charging PEV participants, while also improving distribution system operation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".