On the Quantification of the Network Capacity Deferral Value of Distributed Generation
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Bibliographic record
Abstract
This paper presents an approach to the quantification of the distribution network capacity deferral value of distributed generation (DG). Besides different technical benefits such as reliability and power quality improvement, there are a number of economic benefits related to DG, the most important of which being the end-user electricity bill reduction capability. However, since the onset of the implementation of these technologies, the potential of DG to defer investments on distribution wires and transformers was soon realized, to the point that "non-wire solutions" are now considered as an alternative to network upgrades. In this work, a first approximation to the capacity deferral benefits brought about by DG is obtained. Such approach can be the starting point towards the development of a framework of credits to the owners of DG that fully and fairly recognize the deferral benefits provided to the utility. The financial performance of investments on these important technologies can be then improved, thus broadening DG as a viable market alternative for customers and utilities
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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