Optimal Hourly Scheduling of PV Sources in EDS Considering the Power Variability of Load Demand and DG Using MOGWO Algorithm
Bibliographic record
Abstract
This article presents a novel Multi-Objective Optimization (MOO) technique to allocate multiple Distributed Generation (DG) units based on solar photovoltaic source optimally in radial EDS. The optimization process is performed to simultaneously minimize the Active Power Loss (APL), Total Voltage Variation (TVV), and maximize the Short-Circuit Current (SCI) of the EDS. A new metaheuristic, namely Multi-Objective Grey Wolf Optimizer (MOGWO) algorithm is proposed to solve this optimal DG allocation problem. The applied MOGWO algorithm is equipped with a hierarchical nondominated sorting technique which is superior to the existing fast non-dominated sorting strategy in terms of computational complexity. The proposed methodology is tested on standard IEEE systems are used to demonstrate the feasibility of the MOGWO algorithm in allocating the DG units by taking into account the uncertainty of the power delivered by the DG as well as the variation of load demand in 24 hours.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".