Electricity Markets Cleared by Merit Order—Part II: Strategic Offers and Market Power
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In an electricity market cleared by a merit-order economic dispatch we make use of the mixed-integer linear programming (MILP) scheme derived in Part I to find the market outcomes supported by a pure strategy Nash equilibria (NE). From these NE, we identify offer strategies in terms of gaming or not gaming that best meet the risk/benefit expectations of the participating Gencos. To do this, a number of measures of potential profit gain and loss are developed that quantify the notion of risk/benefit under the possible multiple NE. The NE identification scheme is tested on several systems of up to 30 generating units, each with four incremental cost blocks, also showing how market power is influenced by the number and size of the competing Gencos as well as by the imposed price cap.
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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) | 0.001 | 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