Options based reserve procurement strategy for wind generators - Using binomial trees
Bibliographic record
Abstract
Wind and solar PV are the most mature forms of renewable energy and are integral to our clean energy strategy. Their intermittency poses technical and economic challenges. Technical challenges are load balancing, frequency regulation, etc. Economic challenges include providing least costing load balancing (reserves) services to these intermittent generators. This paper considers a future electricity market situation wherein wind generators are required to forecast and bid to supply energy. The future electricity market treats wind generators similar to conventional generators penalizing for underproduction and pays poorly for overproduction. An intra-day ( 24 h) secondary market is proposed in this paper where a wind generator and a reserve provider can bilaterally trade in reserves. Reserves are traded in the market by purchasing options to buy reserves at predetermined strike prices by paying premiums. These reserves include call and put options to address underproduction and overproduction. A binomial tree approach for estimating possible deviation from the forecast value is used. A new optimization formulation is proposed that uses binomial tree option pricing technique to determine optimal values of strike prices and premiums for call and put options. Two examples illustrate the benefits of the proposed idea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| 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".