A Novel Capacity Market Model With Energy Storage
Bibliographic record
Abstract
Electricity jurisdictions procure generation capacity requirements competitively which maintains investment flows into the electricity system infrastructure. The capacity market auction is one of such competitive capacity procurement methods. It is challenging to consider energy storage (ES) capacity offers in capacity markets due to complex capacity contribution characteristics and lack of explicit mechanisms to integrate them. However, ES capacity can be used to manage the system peak demand. ES can substitute for peaker plants, especially if the demand curve is kurtosis. This paper proposes novel ES capacity contribution formulas and a comprehensive capacity auction model which is designed to consider capacity offers including energy-limited technologies such as ES. After all offers are converted to unforced capacity (UCAP), their energy limitations do not affect bid selection in the market. The proposed novel ES UCAP computation formulas consider power capacity, energy capacity and operational attributes. This paper also presents the results of a case study with three capacity supply offers and a case study based on actual Ontario system data. A detailed sensitivity analysis is also included in the paper to show the validity of the proposed ES UCAP formulas in the auction model. The proposed formulas provide significant benefits in successfully procuring ES capacity offers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".