Energy Management of Fast Charging and Ultra-Fast Charging Stations With Distributed Energy Resources
Bibliographic record
Abstract
Dependence on the depleting fossil fuels, particularly in the transportation sector, calls for sustainable, green solutions such as electrification, which address the challenges of providing alternate sources of power at low emissions. Electric vehicles are the centerpiece of the transportation electrification sector. Reducing the time for charging from hours to a few minutes gives an extra sheen to the prospect of transportation electrification. However, massive upscaling of the electric power infrastructure is required to accommodate the demands of large-scale transportation electrification. This article explores a sustainable strategy involving distributed energy resources to meet the elevated power and energy demand due to DC fast charging and ultra-fast charging EV load on the electric power distribution network. The mixed integer quadratic optimization model developed ensures seamless integration of the renewable resources, the EV load, and the distribution grid while adhering to the power quality constraints. This paper demonstrates that it is possible to serve a 155 kW aggregated (peak power load) DC fast charging load with a 100 kW interconnect. Similarly for an ultrafast -charging station a peak reduction factor of 9.11 compared to unmanaged charging is achieved at the interconnection point. The methodology is developed, and results are compared against a rule-based system for multiple scenarios. Additionally, validation based on OPAL RT RT-4500 for a sample scenario is presented.
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.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".