Emission allowances auction for an oligopolistic electricity market operating under cap-and-trade
Bibliographic record
Abstract
The authors consider a yearly auction where electricity generating companies (Gencos) bid to receive yearly green house gas (GHG) emission allowances. Gencos sell electricity in an oligopolistic electricity market that clears on an hourly basis and operates under a cap-and-trade emissions regulation scheme. Gencos strategically self-allocate their yearly allowance into hourly allowances that they then use to take part in the hourly electricity market. If a Genco emits above or below its self-allocated allowance for that hour then, in the first case, the hourly deficit is made up by buying an allowance from an external market, whereas in the second the hourly allowance surplus is sold to the external market. Recognising that the levels of power and emissions produced by the Gencos as well as the associated prices throughout the year will be influenced by both the yearly and hourly allowances, the auction maximises an objective function that is equal not only to the total amount bid by the Gencos to obtain allowances but also includes the yearly social welfare. This study proposes an approach that considers all of the above-mentioned points in a coordinated fashion and can be viewed as a mathematical program (the allowance auction) subject to a Nash equilibrium problem (the distribution by each Genco of its yearly allowance into hourly allowances), which in turn is subject to the Cournot–Nash equilibrium conditions of the hourly oligopolistic electricity market.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".