Assessing the Yearly Impact of Wind Power Through a New Hybrid Deterministic/Stochastic Unit Commitment
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".