Models for Quantifying the Economic Benefits of Distributed Generation
Why this work is in the frame
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Bibliographic record
Abstract
We examine some of the most important economic benefits brought about by distributed generation technologies to the distribution utility and the power system. Models are developed that allow the quantification of those benefits in economic terms. In some cases, industry regulators or utilities charge connection fees to the owners of distributed generators, even if they are saving the local utility considerable amounts of money every year in deferred network upgrades, reduced losses, avoided wholesale market purchases and others. Efficient economic systems dictate that a proper share of the indirect benefits created by a given economic activity leads to overall optimal independent decision-making by its participants. Quantifying and allocating the benefits of distributed generation to the owners improves the economic performance of their investments and encourages the implementation of those distributed generation applications most valuable to the system.
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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