Bibliographic record
Abstract
In mixed pool/bilateral electricity markets, participants can sign forward bilateral contracts several months in advance of its delivery. In addition, generators may sell to and loads may buy from the pool at the spot price through the day- ahead or balancing markets. Forward bilateral contracts have the advantage of price predictability in comparison with the uncertain spot price. However, the risk is that such a contract commits the partners to a price that may be disadvantageous compared to the spot price. Here, we propose a systematic negotiation scheme through which a generator and load can reach a mutually beneficial and risk tolerable forward bilateral contract, either physical or financial. Under this approach, the generator and load respond rationally to a stream of bilateral bids/counter-bids and offers/counter-offers considering their respective benefits while accounting for the risks incurred by the prediction uncertainty in the pool spot price and other market parameters over the length of the contract. Each negotiating party can choose its own definition of risk which can be influenced by regret, value-at-risk or dispersion from the mean. Numerical tests show that this flexible negotiating approach can be readily put into practice.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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 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".