Energy Efficiency and Grid Flexibility through Building To Vehicle To Buildings Approach: Modelling and Simulation
Bibliographic record
Abstract
This paper focuses on a novel energy management approach, namely Building to Vehicle to Building (V2B2), conceived for cluster of buildings connected in micro-grids, by considering electric vehicles as vector devices for renewable energy exchanges. The main goal behind this concept is to benefit from the potentiality of electric vehicles toward the achievement of the zeroenergy target, extended to the buildings of a virtual microgrid, by exploiting off-site the PV generation. To this aim, a dynamic simulation model is suitably developed for the building energy demands and loads assessment, taking also into account the vehicles energy use. PV electricity is stored in the vehicle batteries and dispatched to other buildings or, at last, to the power grid. In order to show the advantages of the proposed concept and the potentials of the developed simulation tool, implemented in TRNSYS, a suitable case study is conducted. It refers to a non-residential net zero energy building where PV panels are installed for renewable energy production, also supplied to three electric vehicles. These also acts as energy vectors providing stored electricity to two other buildings, where off-site renewable energy is exploited. At the micro-grid level, the match between renewable generation and system demand (buildings and electric vehicles’ needs) is enhanced, reducing the grid operation and boosting the system economic convenience.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.005 | 0.001 |
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".