Stochastic programming models for replication of electricity forward contracts for industry
Bibliographic record
Abstract
Abstract Forward contracts for electricity are valuable to consumers (suppliers) that wish to obtain (sell) power at prices that are more stable than those typically seen in electricity markets. Only a limited variety of forward contracts are available on the market so the need is for a “custom” contract that meets a specific profile of electricity requirements (usually uncertain) over time. This paper develops stochastic programming models that can be used by the supplier of a custom contract to design a procurement strategy that minimizes its expected costs of supply in meeting contract obligations. The procurement strategy will consist of a mix of forwards available in the market, and, in each period, blending its own generation with spot purchases of power. The model also integrates spot selling of power. We consider that expected spot prices and forward prices may disagree since electricity is not storable, creating apparent arbitrage opportunities. We bound the transaction amounts to limit effects of apparent arbitrage and for consistency with the assumption of constant variable generation costs and market prices. For sample cases we compute the optimal procurement strategy, demonstrate the magnitude of the saving, and illustrate the sensitivity of this saving to the magnitude of the upper bounds on the allowed forward positions (a proxy for risk). © 2006 Wiley Periodicals, Inc. Naval Research Logistics, 2006
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".