Optimal allocation of renewable DG for reliability improvement and losses reduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper presents a methodology to evaluate the cost of energy losses and the cost of interruption in distribution networks with renewable distributed generation (DG) connections. Moreover, it presents a renewable DG allocation planning algorithm to determine the optimum locations and sizes of DG units in distribution network for reliability improvement and energy losses reduction. The proposed approach takes into consideration customers' sectors, load variation, stochastic nature of renewable DG, and variable hourly cost of energy. The allocation planning problem is defined as mixed integer nonlinear programming problem, where genetic algorithm (GA) is utilized. Using the proposed approach, the distribution companies can determine optimum sizes and locations of DG units in their systems; besides, the expected reduction in the energy losses and outages costs.
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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