Bibliographic record
Abstract
ABSTRACT Negative prices for electricity are a recent development in European power markets. Negative hourly prices have been permitted at Germany's European Energy Exchange (EEX) spot market since autumn 2008 and have occurred frequently since, reaching values as low as €-500/MWh. However, in some non-European markets such as those in the US, Australia and Canada, negative prices have been observed over longer periods. Negative prices are, in fact, natural in electricity spot trading. Plant flexibility is limited and costly, and, therefore, incurring a negative price for an hour can actually be economically optimal overall. Negative prices pose a basic problem for stochastic price modeling in that going from prices to log prices is not possible. So far, this has been dealt with using "workarounds". In this paper, a thorough approach is advocated, based on the area hyperbolic sine transformation. The transformation is applied to spot modeling of the German EEX and the Electric Reliability Council of Texas's West Texas (ERCOT West) market, and an exemplary valuation of an option is carried out. It is concluded that the area hyperbolic sine transform is a natural starting point for modeling negative power prices. It can be integrated into common stochastic price models without adding much complexity. Moreover, this transformation might, in general, be more appropriate for power prices than the log transformation, considering the fundamentals of power price formation. Negative prices can significantly affect a business and a thorough treatment is therefore indispensable.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".