Evaluating the Impact of V2G on Frequency Regulation in the Colombian Electricity Grid
Bibliographic record
Abstract
This study focuses on addressing the challenges of frequency regulation in the Colombian electrical grid by evaluating the impact of Vehicle-to-Grid (V2G) technology as a potential solution. The main objective is to determine how the implementation of V2G technology can influence the electrical grid’s capacity to regulate frequency efficiently and reliably. To achieve this, a methodology is employed that includes the development of a computational tool to simulate scenarios using battery models representing electric vehicles in the Colombian grid. Preliminary results indicate that the adoption of V2G technology can significantly improve the electrical grid’s capacity to regulate frequency, reducing dependence on traditional energy generation resources. This study contributes to the field by providing empirical evidence of the potential benefits of V2G technology in frequency regulation, thus filling a gap in existing literature, especially in the Colombian context. The practical implications of these findings are significant, as they suggest that the implementation of V2G technology could enhance the stability and reliability of the electrical supply, as well as promote the integration of renewable energy sources into the electrical grid.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".