A Generic Convex Model for a Chance-Constrained Look-Ahead Economic Dispatch Problem Incorporating an Efficient Wind Power Distribution Modeling
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Bibliographic record
Abstract
Power systems with high penetration of wind resources must cope with significant uncertainties originated from wind power prediction error. This uncertainty might lead to wind power curtailment and load shedding events in the system as a big challenge. Efficient modeling and incorporation of wind power uncertainty in generation and reserve scheduling can prevent these events. This paper presents a new framework for wind power cumulative distribution function (CDF) modeling and its incorporation in a new chance-constrained economic dispatch (CCED) problem. The proposed CDF modeling uses few moments of wind power random samples. To validly capture the actual features of the wind power distribution such as main mass, high skewness, tails, and especially boundaries from the moments, an efficient moment problem is presented and solved using the beta kernel density representation (BKDR) technique. Importantly, a new polynomial cost function for efficient modeling of wind power misestimation costs is proposed for the CCED problem that eliminates the need for an analytical CDF and enables the use of an accurate piecewise linearization technique. Using this technique, the non-linear CCED problem is converted to a mixed-integer linear programming (MILP)-based problem that is convex with respect to the continuous variables of the problem. Therefore, it is solved via off-the-shelf mathematical programming solvers to reach more optimal results. Numerical simulations using the IEEE 118-bus test system show that compared with conventional approaches, the proposed MILP-based model leads to lower power system total cost, and thereby is suggested for practical applications.
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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) | 0.001 | 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 | 0.000 |
| 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