Nodal Pricing and Transmissions Losses. An Application to a Hydroelectric Power System
Bibliographic record
Abstract
Since January 1st, 1997, the wholesale electricity market in the U.S. has been open to competition through FERC Order 888. In order to satisfy the reciprocity requirements which were imposed by FERC to foreign utilities, Hydro-Québec made her transmission grid accessible to third parties. A single flat rate is applied to account for transmission losses; location and time of use play no role. Hydro-Québec is a hydro based utility and it has very long linear high voltage power lines which link hydro power sites in the north to consumption centres in the south. In this paper, we compare three different methods of incorporating transmission losses into nodal prices for a simpplified model of Hydro-Québec electric network: flat rate, linear power loss rates, and quadratic power loss rates. The latter two vary by node and time of use. We estimate that nodal price differences between the flat rate and the quadratic power loss rates can be as large as 27.8% on the producer side and 32.7% on the consumer side. The implications of such price differences for the location of economic activity over the service area could be significant.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".