A Regional Approach to Market Monitoring in the West
Bibliographic record
Abstract
Market monitoring involves the systematic analysis of pricesand behavior in wholesale power markets to determine when and whetherpotentially anti-competitive behavior is occurring. Regional TransmissionOrganizations (RTOs) typically have a market monitoring function. Becausethe West does not have active RTOs outside of California, it does nothave the market monitoring that RTOs have. In addition, because the Westoutside of California does not have RTOs that perform centralized unitcommitment and dispatch, the rich data that are typically available tomarket monitors in RTO markets are not available in the West outside ofCalifornia. This paper examines the feasibility of market monitoring inthe West outside of California given readily available data. We developsimple econometric models of wholesale power prices in the West thatmight be used for market monitoring. In addition, we examine whetherproduction cost simulations that have been developed for long-runplanning might be useful for market monitoring. We find that simpleeconometric models go a long ways towards explaining wholesale powerprices in the West and might be used to identify potentially anomalousprices. In contrast, we find that the simulated prices from a specificset of production cost simulations exhibit characteristics that aresufficiently different from observed prices that we question theirusefulness for explaining price formation in the West and hence theirusefulness as a market monitoring tool.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".