Optimal Design of V2G Incentives and V2G-Capable Electric Vehicles Parking Lots Considering Cost-Benefit Financial Analysis and User Participation
Why this work is in the frame
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Bibliographic record
Abstract
The deployment of electric vehicles parking lots (EVPLs) has been suggested as a solution that could serve the charging needs of electric vehicles (EVs) parked in public spaces. Due to their nature, EVPLs offer some advantages that public charging stations cannot offer. The availability of numerous EV batteries over a long period in the EVPL means that the storage capacity of EVs in EVPLs could be utilized in vehicle-to-grid (V2G) schemes through the installation of bi-directional chargers (BD) that allow injection of power back into the grid. However, it is imperative to determine the optimal number of BD in an EVPL given their high costs and the lack of clear evidence that many EV users will participate in the provision of V2G programs. Therefore, this article aims to develop a model for the optimal design of a V2G-capable EVPL via which the financial model of V2G services and V2G incentive-participation scheme for EV users are incorporated in the decision of installing different types of chargers. The model integrates a multi-mode objective that allows for the maximization of EVPL owner profit and/or EVPL social responsibility using control parameters incorporated in the model. The model also decouples the intertwined economic dynamics of EV charging and V2G services in the EVPL via revenue models targeting each service separately. This allows optimal pricing of services with respect to the invested capital. Actual survey data and historical operating information are used to verify the validity and feasibility of the model. The results show that the proposed model effectively meets design targets and charging/discharging requirements. By providing V2G services, including DR services to the grid, the profit margin of EVPL owners can increase by up to 12.16%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it