El modelo HRV para expansión óptima de redes de transmisión: una aplicación a la red eléctrica de Ontario [The HRV Model for the Optimal Expansion of Transmission Networks: an Application to the Ontario Electricity Grid]
Bibliographic record
Abstract
This paper presents an application of a mechanism that provides incentives to promote transmission network expansion in the electricity system of the Ontario province. Such a mechanism combines a merchant approach with a regulatory approach. It is based on the rebalancing of a two-part tariff within the framework of a wholesale electricity market with nodal pricing. The expansion of the network is carried out through auctions of financial transmission rights for congested links. The mechanism is tested for a simplified transmission grid with ten interconnected zones, ten nodes, eleven lines and seventy eight generators in the Ontario province. The simulation is carried out for both peak and non-peak scenarios. Considering Laspeyres weights, the results show that that prices converge to the marginal cost of generation, the congestion rent decreases, and the total social welfare increases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".