Bibliographic record
Abstract
Negative prices for electricity are a novelty in European power markets. At the German EEX spot market negative hourly prices have since occurred frequently, down to values as extreme as minus several hundred €/MWh. However, in some non-European markets as USA, Australia and Canada, negative prices are a characteristic for a longer period already. Negative prices are in fact natural for electricity spot trading: plant flexibility is limited and costly, thus, incurring a negative price for an hour can nevertheless be economically optimal overall. Negative prices pose a basic problem to stochastic price modelling: going from prices to log-prices is not possible. So far, this has been dealt with by “workarounds”. However, here a thorough approach is advocated, based on the area hyperbolic sine transformation. The transformation is applied to spot modelling of the German EEX, the ERCOT West Texas market and the exemplary valuation of an option. It is concluded that the area hyperbolic sine transform is well and naturally suited as a starting point for modelling negative power prices. It can be integrated in common stochastic price models without adding much complexity. Moreover, this transformation might be in general more appropriate for power prices than the log transformation, considering fundamentals of power price formation. Eventually, a thorough treatment of negative prices is indispensable since they significantly affect business.
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How this classification was reachedexpand
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".