Optimal WDG planning in active distribution networks based on possibilistic–probabilistic PEVs load modelling
Bibliographic record
Abstract
Distribution network operators and planners usually model the load demand of plug‐in electric vehicles (PEVs) to evaluate their effects on operation and planning procedures. Increasing the PEVs’ load modelling accuracy leads to more precise and reliable operation and planning approaches. This study presents a methodology for possibilistic–probabilistic‐based PEVs’ load modelling in order to be employed in optimal wind distributed generation (WDG) planning. The proposed methodology considers not only the PEVs temporal uncertainty, but also the uncertain spatial effect of PEVs on WDGs as renewable‐based distributed resources. The WDG planning is considered as an optimisation problem which is solved under technical and economic constraints. A hybrid modified particle swarm optimisation/genetic algorithm is proposed for optimisation that is more robust than the conventional algorithms. The effectiveness of the proposed load modelling of PEVs and the proposed algorithm is evaluated in several scenarios.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".