Integration of PV Distributed Generator in Electrical Distribution System with Electric Vehicles Charging Stations Considering Uncertainties using Chaotic SSA Algorithm
Bibliographic record
Abstract
The penetration of renewable energy resource units in the Electrical Distribution System (EDS) has gradually increased. In addition to that, the interest in the electrification of the transport sector has brought about increasingly significant incentives for the integration of Electric Vehicles Charging Station (EVCS). In this regard, the planning of the installation of PV source-based Distributed Generation (DG) units in EDS considering EVCS should be carefully considered to avoid stressing the EDS. This paper applied various Chaotic Salp Swarm Algorithm (CSSA) based various chaotic maps methods with the multi-objective functions that are considered minimizing simultaneous the Active Power Loss (APL), the Annual Losses Cost (ALC), and the Total Voltage Deviation (TVV) in EDS. The proposed algorithms are tested on a standard IEEE 69-bus system that is used to demonstrate the feasibility of the CSSA algorithm in allocating the DG units by considering the uncertainty of the power delivered by the DG as well as the variation of load demand and EVCS in 24 hours. Furthermore, the overall EDS performances are also enhanced with simultaneous placement of both devices.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".