Bibliographic record
Abstract
Modern electricity markets are characterized by increasing shares of intermittent production which has almost zero marginal costs. The effect of introducing large amounts of cheap power into the system is known as the merit order effect – a shift of a supply curve to the right which delivers lower equilibrium prices. The lower prices and the fact that fossil-fuel generators are used less often exacerbate adequacy problems – there is a threat that not enough generating capacity will be available in the system since generators´ revenues are low and investment needs are not met. This and the fact that energy markets are often capped in order to prevent market power leads to the so called “missing money problem” (Teirila and Ritz, 2018, Bublitz et al., 2019). One possible remedy is to supplement the energy only markets with capacity markets (Newbery, 2016; Cramton et al, 2013; Joskow, 2007). Recently the electricity market on the island of Ireland has been completely restructured, a change that affected also the capacity mechanism, transforming it from an administrative decision-based to a market-based mechanism, an auction. The move however has not been a smooth one, with a supply of Dublin put at risk as one of the main suppliers in the area wanted to withdraw from the market as a result of not being able to successfully secure the operation of its two units. Since Irish electricity demand is forecast to grow by between 15% and 47% over the next ten years, with over a quarter of all electricity consumed by data centres, many of which will be in the Dublin region (EirGrid, 2018a), the threat of losing one of the suppliers become even more serious. In this case study we show how even with considerable analysis and preparation, the introduction of an auction system is not without risk.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".