PEVs modeling and impacts mitigation in distribution networks
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 novel model to estimate the electric energy consumption of light duty fleet of plug-in electric vehicles (PEVs). This model can be used to evaluate the impacts of plugging such loads in distribution networks. Both vehicles users' habits and diversity of usage are considered in the presented model, as well as different electric ranges and ambient temperature effect. Moreover, the paper proposes a method to optimally allocate distributed generation (DG) units in the distribution network to mitigate the impacts of high penetration of PEVs. The proposed model shall help the local distribution companies (LDC) to better assess the expected effects of PEVs on their networks and evaluate the required upgrades. Furthermore, the proposed DG allocation methodology helps to identify the optimal buses on which to connect these DG units in the presence of high PEVs penetration. A genetic based approach is utilized for the planning problem of determining the optimal locations and sizes of DG units, which is defined as a multi-objective mixed integer programming.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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