Multi-Objective Optimization for the Operation of an Electric Distribution System With a Large Number of Single Phase Solar Generators
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
The extensive connection of single phase solar generators which are also called microFITs (micro feed-in tariff), to distribution systems may lead to a phase unbalance condition, a problem further complicated due to the widespread use of single phase loads. Energy losses also change significantly when microFITs are implemented. This paper addresses these problems with respect to the connection of a large number of microFITs and single phase loads to three phase distribution systems. In this research, a probabilistic model has been utilized for estimating hourly solar irradiance, and a genetic algorithm has been employed as a means of generating a non-dominated Pareto front for minimizing the current unbalance and energy loss in the distribution system. A decision-making process has been developed in order to determine a single optimum solution from the Pareto front generated. Operational controls, such as voltage drop, transmission limits, and voltage unbalance limits, are taken into consideration in this analysis. In the context of smart grids, the proposed algorithm will facilitate the deployment of small-sized solar generators. The proposed method has been applied on an IEEE 123 bus distribution system in order to demonstrate the validity of the proposed algorithm.
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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