Optimal Dynamic Pricing and Rewarding for Electric Vehicle Charging Scheme in High Penetration Photovoltaic Microgrid
Bibliographic record
Abstract
Electric vehicle (EV) charging station integrated into the photovoltaic-based microgrid (MG) is emerging as a promising alternative energy storage solution for MG connected to the low-voltage distribution network. However, the lack of commitment from EV owners to share their vehicle storage capability while parking, challenges to ensure the economic operation of the system. In this paper, we propose a dynamic pricing scheme that is constructed by varying charging price and vehicle-to-grid rewards to encourage the participation of EV battery reserve to minimize operating costs for the MG and de-stress the distribution network while satisfying all physical and operating constraints. A PSO technique is applied to search for the optimal dynamic price and reward, then the model is reformulated as a MILP problem. The numerical simulations investigate three different scenarios of the arrival/departure period of the EV fleets to demonstrate the effective impacts of integrating EV into the PV-based MG and distribution network. Furthermore, the proposed method outperforms when scheduling the dynamic pricing and optimal energy trajectory for 24 hours ahead with the 1-hour interval.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".