Deregulated Power Prices: Changes Over Time
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
We examine price by season and by year in 14 deregulated markets, by looking at diurnal price patterns and price volatility. Power price volatility is measured by price velocity, the daily average of the absolute value of price change per hour, which is broken into a component expected from the average diurnal pattern, and an unexpected component. Deregulated markets can be categorized into three groups: stable markets, markets that experienced one bad period or season of high price excursion, and chaotic markets. Britain, Spain and Scandinavia show consistent price patterns and low unexpected price velocity; a thoughtful consumer could reasonably implement demand side management (DSM) by shaping consumption patterns to reflect price. California, New Zealand and Alberta are examples of markets that had a period of very high price excursion; we discuss factors affecting public reaction to this. Australian power markets have inconsistent price patterns from season to season and year to year, and very high unexpected price velocity. Planned DSM in these markets would be very difficult. We offer four policy considerations for markets considering deregulation in the future.
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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) | 0.000 | 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) | 0.001 | 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 it