The Potential of Vehicle-to-Home Integration for Residential Prosumers: A Case Study
Bibliographic record
Abstract
Abstract The transition of the transport sector to e-mobility poses various challenges but also provides great flexible load and supply potential and thus enables a stronger coupling of the transport sector with other sectors. If emerging opportunities such as bidirectional charging in the context of Vehicle-to-Home and Vehicle-to-Grid applications are utilised, a previously unimagined load management and storage potential can be tapped. This can transform e-mobility from an additional burden to the grid to a grid-supporting factor that enables greater integration of renewable energies and reduces additional investments in infrastructure like grid expansion and stationary storage systems. In order to investigate this potential, within this work we examine simulation based various Vehicle-to-Home (PV self-consumption, load shifting due to flexible electricity tariff) and Vehicle-to-Grid (secondary reserve) scenarios for different driving profiles for a residential building with heat pump, PV system and optionally a small wind turbine. In addition, a charge load optimisation is carried out using a genetic algorithm. The energy quantities, saving potential and additional number of battery cycles are quantified. The results show that, despite additional battery degradation, significant financial incentives can be achieved.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".