Assessing the Yearly Impact of Wind Power Through a New Hybrid Deterministic/Stochastic Unit Commitment
Why this work is in the frame
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Bibliographic record
Abstract
This paper proposes a new unit commitment (UC) formulation for a power system with significant levels of wind generation. The proposed scheme departs from existing unit commitments in that it explicitly models the day-ahead predicted residual demand probability density function (PDF) including the effect of wind power curtailment. This PDF is then used to define a constraint on the probability of the residual demand exceeding the scheduled reserve, which is imposed in addition to the standard N-1 deterministic security criterion. This hybrid probabilistic/deterministic form maintains the mixed-integer linear structure that makes the proposed UC compatible with highly efficient commercially available solvers. Numerical examples illustrate the economical and technical benefits obtained by systematically including wind curtailment as decisions variables in the UC. In addition, the paper computes the hourly day-ahead UC schedule over the course of one year for a typical power system to illustrate the impact of wind power penetration on measures such as operation costs, incremental costs, emission levels, on/off unit switching operations, and reserve levels.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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