El modelo HRV para la expansión óptima de redes de transmisión: Una aplicación a la red eléctrica de Ontario
Bibliographic record
Abstract
Este documento presenta la aplicación de un mecanismo que provee incentivos para la inversión en expansión de redes en el sistema eléctrico de la provincia de Ontario, Canadá. Tal mecanismo combina tanto un enfoque de mercado como uno regulatorio. Se basa en el rebalanceo de una tarifa en dos partes dentro de un contexto de mercado eléctrico mayorista, a la par de la fijación de precios nodales. La expansión de la red se lleva a cabo a través de subastas de derechos financieros de transmisión para las líneas congestionadas. El mecanismo se prueba para una red de transmisión simplificada de diez zonas eléctricas interconectadas, diez nodos, once líneas y setenta y ocho generadores en la provincia de Ontario. La simulación se realiza en escenarios tanto de hora pico como de hora no pico. Al considerar ponderadores de Laspeyres, los resultados muestran que los precios convergen al costo marginal de generación, la renta de congestión disminuye y el bienestar social se incrementa.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".