Optimal Allocation of RDG in Distribution System Considering the Seasonal Uncertainties of Load Demand and Solar-Wind Generation Systems
Bibliographic record
Abstract
Recently, the installation of Renewable Distributed Generation (RDG) into the Electrical Distribution System (EDS) became one of the best solutions that guarantee the balance between electric energy consumption and production, also show various advantages. In addition to delivering clean energy, they contribute to minimizing power losses, as well as enhancing the voltage profiles. In this paper, the metaheuristic optimization algorithm of the Grey Wolf Optimizer (GWO) is utilized to optimally allocate the RDG based multiple PV and WT units into EDS considering the uncertainty of electrical output energy from the RDGs as well as load demand variation during all seasons. The Multi-Objective Functions (MOF) developed in this paper is considered to minimize simultaneous the indices of the total of Active Power Loss Index (APLI), the Reactive Power Loss Index (RPLI), the Voltage Deviation Index (VDI), the Operation Time Index (OTI) of the overcurrent relay (OCR), and enhance the Coordination Time Interval Index (CTII) of the overcurrent relays installed in the test system which is the IEEE 33-bus EDS.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".