An Incentive-Based Mechanism to Enhance Energy Trading among Microgrids, EVs, and Grid
Bibliographic record
Abstract
The growing penetration of electric vehicles (EVs) introduces both opportunities and challenges for power grid operators. Incentivization is considered a viable option to tempt EV owners to participate in supporting the grid during peak load intervals while receiving compensation for their services. Therefore, this study proposes a two-step incentive mechanism to reduce the peak load of the grid by enabling power trading among the microgrid, EVs and the utility grid. In the first step, an incentive price is determined for EVs considering the grid-loading conditions during different hours of the day. In the second step, a multi-objective optimization problem is formulated to optimize trading among different entities, such as EVs, the microgrid and the utility grid. The two objectives considered in this study are the operation cost of the microgrid and the revenue of EVs. Monte Carlo simulations are used to deal with uncertainties associated with EVs. Simulations are conducted to analyze the impact of different weight parameters on the energy-trading amount and operation cost of EVs and MG. In addition, a sensitivity analysis is conducted to analyze the impact of changes in the EV fleet size on the energy-trading amount and operation cost.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".