Real-Time Optimal Dispatch and Economic Viability of Cryogenic Energy Storage Exploiting Arbitrage Opportunities in an Electricity Market
Bibliographic record
Abstract
In this paper, the economic viability and profitability of a newly emerging storage technology, i.e., cryogenic energy storage (CES), is investigated. A real-time optimal dispatching algorithm is proposed and developed to optimally dispatch a privately owned CES unit to generate revenue by exploiting arbitrage opportunities in the day-ahead/week-ahead electricity market. Due to its special characteristics, CES can provide significantly more financial and technical benefits in a weekly scheduling compared with common daily scheduling. The electricity price modulation is proposed as a new approach to competitively offer subsidy by the utility regulator to CES owners to fill the gap between current and a stable expected rate of return. Using real-world price data from the Ontario wholesale electricity market, the method is validated. The results reveal significant benefits of weekly usage as compared to daily usage of CES. The efficacy and feasibility of the proposed approach to subsidize CES owners are validated through simulation studies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".