A Novel Unscented Transformation-Based Framework for Distribution Network Expansion Planning Considering Smart EV Parking Lots
Bibliographic record
Abstract
Public parking lots equipped with electric vehicle (EV) charging facilities place huge power demands on distribution networks. These huge demands, if not carefully considered at the planning stage, can create several operational problems. To address this issue, this paper proposes a novel distribution network expansion planning framework, which gives full consideration to the charging power demands of large EV parking lots. This framework provides several alternatives for construction/reinforcement of feeders and substations, while taking all the necessary constraints into account. Furthermore, the unscented transformation (UT) method is employed to model the uncertainties of load demands and EV parking lot demands. The ability of the UT method to accurately model correlated uncertain parameters makes it highly applicable in the context of distribution network expansion planning, where considerable correlated uncertainties exist. The proposed UT-based framework is formulated as a mixed-integer linear programming (MILP) problem, which can be solved using off-the-shelf mathematical programming solvers that guarantee convergence to the global optimal solution. A 24-node distribution system is used to verify the effectiveness of the proposed methodology.
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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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".