Seasonal Energy Storage Operations with Limited Flexibility: The Price-Adjusted Rolling Intrinsic Policy
Bibliographic record
Abstract
The value of seasonal energy storage depends on how the firm operates storage to capture seasonal price spreads. Energy storage operations typically face limited operational flexibility characterized by the speed of storing and releasing energy, which makes the optimal policy, in general, difficult to compute. A widely used practice-based heuristic, the rolling intrinsic (RI) policy, generally performs well compared with an optimal policy but can significantly underperform in some cases. In this paper, we aim to understand the gap between the RI policy and the optimal policy and leverage the resulting insights to improve the RI policy. A new heuristic policy, the price-adjusted rolling intrinsic (PARI) policy, is developed based on theoretical analysis of storage options. This heuristic adjusts certain prices before applying the RI policy to provide the RI policy with estimates of the values of various storage options. We evaluate the performance of the RI and PARI polices using actual data from the natural gas industry. Our results show that, on average, the PARI policy recovers about 67% of the value loss of the RI policy. Furthermore, when the value loss of the RI policy is larger, the PARI policy tends to recover a higher fraction of that value loss.
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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 | 0.014 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".