Bidding wind power in short-term electricity market based on multiple-objective fuzzy optimization
Why this work is in the frame
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Bibliographic record
Abstract
Wind energy is promising with no fuel cost and zero greenhouse gas emissions; however, its intermittent and volatile nature has added much to operation burdens and thus a low penetration level in short-term or spot market. On the one hand, the power system operator is facing increased spinning reserve and generation uncertainty; on the other hand, the wind independent power producer (IPP) is subject to imbalance penalties in the balancing market. Previous literatures solely focused on maximizing the profit for a wind IPP formulating optimal bidding strategies without the consideration of operator side. This paper proposes a multiple-objective optimal bidding strategy to achieve both wind IPP’s maximum profit and less challenge for the operator. The strategy is formulated as a mixed-integer linear programming (MILP) problem with fuzzy optimization techniques. Analytic and numerical solutions will be given with discussion on risk control.
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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 | 0.001 | 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 | 0.001 |
| 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