Self-Scheduling Models of a CAES Facility Under Uncertainties
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Bibliographic record
Abstract
This paper presents two mathematical formulations to represent uncertainties in self-scheduling models of a price-taker Compressed Air Energy Storage (CAES) facility. The proposed model is from the point of view of the plant owner participating in the energy, spinning, and idle reserve markets. The first described formulation is based on Robust Optimization (RO) and the second one is based on Affine Arithmetic (AA) techniques, which are both range arithmetic methodologies, and consider the thermodynamic characteristics of the CAES facility for a more realistic representation. The implementation of both methods are tested, validated and compared with each other and with Monte Carlo Simulations (MCS) using prices from the Ontario market. From the simulation results, it can be observed that both methods have some similarities, presenting lower computational burden compared with MCS, and demonstrate the advantage of applying the proposed models for CAES plant owners to hedge against price uncertainties.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it