Network reconfiguration in balanced distribution systems with variable load demand and variable renewable resources generation
Bibliographic record
Abstract
There is increasing interest in renewable energy sources interconnections in distribution systems since they are inexhaustible and nonpolluting. Wind and photovoltaic are among most mature renewable energy sources, and their penetration continues to increase. This paper proposes a method for distribution network reconfiguration to minimize annual energy losses by determining the optimal configuration for each season of the year. Uncertainties including the daily time varying load and stochastic power generation of renewable distributed generators (DGs) are modeled and taken into account. The method is based on generating a probabilistic generation-load model that combines all possible operating conditions of the renewable DGs with their probabilities of occurrence, then accommodating this model in the reconfiguration problem. The reconfiguration problem, based on Genetic Algorithm, aims at achieving minimum annual energy losses. The constraints include the voltage limits, line current limits, radial topology, and feeding of all loads. The proposed method has been tested on two systems. Simulation results show significant reductions in the seasonal and annual energy losses for all the studied cases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".