Accommodating High Penetrations of PEVs and Renewable DG Considering Uncertainties in Distribution Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper proposes a multi-year multi-objective planning algorithm for enabling distribution networks to accommodate high penetrations of plug-in electric vehicles (PEVs) in conjunction with renewable distributed generation (DG). The proposed algorithm includes consideration of uncertainties and will help local distribution companies (LDC) better assess the expected impacts of PEVs on their networks and on proposed renewable DG connections. The goal of the proposed algorithm is to minimize greenhouse gas emissions and system costs during the planning horizon. An approach based on a non-dominated sorting genetic algorithm (NDSGA) is utilized to solve the planning problem of determining the optimal level of PEV penetration as well as the location, size, and year of installation of renewable DG units. The planning problem is defined in terms of multi-objective mixed integer nonlinear programming. The outcomes of the planning problem represent the Pareto frontier, which describes the optimal system solutions, from which the LDC can choose the system operating point, based on its preferences.
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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 it