Distribution system loss minimization using optimal DG mix
Why this work is in the frame
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Bibliographic record
Abstract
In this paper a probabilistic-based model is proposed to determine the optimal mix of different types of renewable distributed generation (DG) units (i.e. wind-based DG and solar DG) to minimize the annual energy losses in the distribution system without violating the system constraints. Beta and Rayleigh probability density functions have been utilized to estimate the random behavior of the solar irradiance and wind speed, respectively; whereas IEEE-RTS system has been applied to describe the load profile. The problem is formulated as a mixed integer non-linear programming (MINLP); with an objective function to minimize the distribution system annual energy losses. This proposed technique has been applied to a typical rural distribution system with different scenarios including all possible combinations of renewable resources. The results show that a significant reduction in the annual energy losses is achieved for all the proposed scenarios.
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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 | 0.000 |
| 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