Multi-Objective Optimization for the Operation of an Electric Distribution System With a Large Number of Single Phase Solar Generators
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".