Combined pool/bilateral dispatch. II. Curtailment of firm and nonfirm contracts
Bibliographic record
Abstract
This paper deals with the dispatch of power networks under mixed pool/bilateral trading. The major questions that are examined are the following: (1) To what degree does the relative level of pool versus bilateral trading influence performance in terms of individual power levels, costs, prices, and revenues? (2) What is the comparative performance of mixed trading with firm and nonfirm bilateral contracts under various curtailment strategies? (3) Is the revenue derived from the pool and bilateral trading consistent with the corresponding unbundled costs? The above questions are sequentially addressed in separate parts of this three-part paper. 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. In Part II, two types of bilateral contracts, firm and nonfirm, are introduced together with their respective curtailment and noncurtailment bids. The optimal power-flow problem from Part I is now modified to accommodate this new type of operation. Technical and economical performance measures defined in Part I, namely, generation revenues from bilateral and pool sales, pool demand payments, plus generation and load expenditures to cover transmission loss and congestion management attributed to bilateral exchanges are also used here together with revenues from contract curtailment and expenditures due to noncurtailment bidding. Simulation results illustrate the effects of firm and nonfirm contracts and their bidding strategies on the relative levels of pool/bilateral trading, as well as on economic performance of market participants.
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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 | 0.003 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".