Assisting Residential Distribution Grids in Overcoming Large-Scale EV Preconditioning Load
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
The repercussion of increased electric vehicle (EV) charging demand is notable at the distribution grid especially during the cold morning, while users tend to precondition their vehicles before leaving their premises. Moreover, due to the price declination, a tendency of installing level 2 chargers in residential premises is anticipated, which should stimulate the appearance of a new peak to the residential load profile. Hence, multiple scenarios of preconditioning are simulated, and the corresponding network’s quality metrics (e.g., voltage level and power losses) are assessed to analyze the impact. And a remarkable consequence is observed. As a consequence, to mitigate the consequences and manage the new peak load, the optimal reconfiguration of network is implemented, and unfortunately, with a larger number of EVs, this technique fails to attain the minimum voltage level. Therefore, leveraging this high number of EVs, instead of relying on the network reconfiguration, power is assumed to be injected from idle EVs through vehicle-to-grid (V2G) energy transmission. An integer linear program is formed to schedule a set of EVs in participating in V2G, and the outcome indicates that V2G alone could not compensate for the disturbance in the network. Accordingly, a hybrid method of V2G and reconfiguration is proposed and evaluated to assist the network in handling the new peak load, and this hybrid solution reduces power losses in the network by 50% on average and maintains the voltage level above the operational threshold of 0.95 p.u.
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 | 0.001 | 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 | 0.001 |
| 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