Combined Pool/Bilateral Dispatch-Part 1: Performance of Mixed Trading Strategies
Bibliographic record
Abstract
This three-paper series deals with the dispatch of power networks under mixed pool/bilateral trading. The major questions examined are: (i) To what degree does the relative level of pool versus bilateral trading influence performance in terms of individual power levels, costs, prices, revenues, and expenditures? (ii) What is the comparative performance of mixed trading with firm and nonfirm bilateral contracts under various curtailment strategies? (iii) Is the revenue derived from the pool and bilateral trading consistent with the corresponding unbundled costs? These issues are sequentially addressed in each of the three parts. The eventual goal of these results is to help generator and load-serving entities choose appropriate relative levels of pool versus bilateral trades while considering risk, economic performance, and physical constraints. This paper proposes a one-step optimal power flow model that dispatches the pool in combination with the privately negotiated bilateral contracts while minimizing cost and accounting for both losses and congestion. In Part I notions of pool/bilateral demand and generation, as well as a number of technical and economic performance measures for each competing entity, are defined. This dissection of total and individual financial measures according to pool or bilateral trading allows the market participant to evaluate the profitability of each component of its chosen pool/bilateral mix. A number of simulation results illustrate the effect of varying relative levels of pool/bilateral trading on values of individual performance measures.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".