Reducing the impact of dynamic wireless charging of electric vehicles on the grid through renewable power integration
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
Electrification of roadways using dynamic wireless charging (DWC) technology can provide an effective solution to range anxiety, high battery costs and long charging times of electric vehicles (EVs). With DWC systems installed on roadways, they constitute a charging infrastructure or electrified roads (eRoads) that have many advantages. For instance, the large battery size of heavy-duty EVs can significantly be downsized due to charging-while-driving. However, a high power demand of the DWC system, especially during traffic rush periods, could lead to voltage instability in the grid and undesirable power demand curves. In this paper, a model for the power demand is developed to predict the DWC system's power demand at various levels of EV penetration rate. The DWC power demand profile in the chosen 550 km section of a major highway in Canada is simulated. Solar photovoltaic (PV) panels are integrated with the DWC, and the integrated system is optimized to mitigate the peak power demand on the electrical grid. With solar panels of 55,000 kW rated capacity installed along roadsides in the study region, the peak power demand on the electrical grid is reduced from 167.5 to 136.1 MW or by 18.7 % at an EV penetration rate of 30 % under monthly average daily solar radiation in July. It is evidenced that solar PV power has effectively smoothed the peak power demand on the grid. Moreover, the locally generated renewable power could help ease off expensive grid upgrades and expansions for the eRoad. Also, the economic feasibility of the solar PV integrated DWC system is assessed using cost analysis metrics.
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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 | 0.001 |
| 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