Management Scheme for Increasing the Connectivity of Small-Scale Renewable DG
Bibliographic record
Abstract
This paper presents a planning model and an active network management (ANM) scheme for increasing small-scale renewable distributed generation (DG) capacity in distribution networks. The capacity of each DG unit is assumed to include two components: 1) unconditional and 2) conditional. Unconditional DG capacity is calculated using an appropriate economic model that ensures adequate profit for DG investors. For all online distribution system conditions, a DG unit whose capacity is less than or equal to the unconditional DG capacity is granted permission to inject power into the system without curtailment. The first phase of this work involved the development of a proposed planning model that maximizes the number of DG units installed based on the calculated unconditional capacity. Any capacity higher than the unconditional DG capacity is considered conditional capacity. The second phase of this work is focused on an ANM scheme for minimizing the curtailment of conditional DG capacity using a novel scalable optimization model. The simulation results show that the proposed planning model with the ANM scheme significantly increases the photovoltaic (PV) DG capacity that can be installed. The simulation results indicate that online operation of the proposed ANM scheme would provide a favorable outcome and enhanced performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".