<scp>Techno‐economic</scp> feasibility study on electric vehicle and renewable energy integration: A case study
Bibliographic record
Abstract
Abstract This work simulated the economic viability of electric vehicles (EVs) and renewable energy charging integration at a university campus. Homer energy simulation software was used to determine the optimal solution for the power loading, energy dispatch, and economic feasibility of each electricity source. Three scenarios were considered: case 1 (C1) is the baseline that provided the electric parameters considering a grid without renewable energy integration; case 2 (C2) is the addition of solar and wind systems supplying power to the grid and EVs; case 3 (C3) is the same conditions as C2, but EVs operate on vehicle‐to‐grid (V2G) scheme supplying energy to grid. Economic output solutions were classified according to the lowest net present value (NPV). The V2G mechanism considered on the C3 offered conditions that decreased the costs of operation of the system, estimating the lowest NPV among the options analyzed. In C2, the simulations recorded higher costs due to the system operation associated with extra local load to power the EVs. In both C2 and C3 scenarios, EVs aided the renewable energy sources' penetration on the system; however, the operation of EVs on a V2G mechanism contributed to the highest rate of renewable energy penetration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".