Network-constrained multiperiod auction for a pool-based electricity market
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents a multiperiod electricity auction market tool that explicitly takes into account transmission congestion and losses as well as intertemporal operating constraints such as start-up costs, ramp rates, and minimum up and down times that may be included in any generating unit's composite bid. This approach, which requires only existing mixed-integer linear solvers, provides the market operator with a valuable tool for scheduling participants in a competitive market where transparency, fairness, and confidentiality of participants' data are of paramount concern. Indeed, under this framework, only network data are of public domain; producers are not required to reveal corporate data, and they have more flexibility in specifying the structure of their composite bid. This paper demonstrates and illustrates, through numerical studies using test systems, that an efficient and fair competitive electricity market can be implemented, taking into account network constraints and losses.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it