PV ENABLED NET ZERO EV CHARGING STATION: SYSTEM DESIGN AND SIMULATION STUDY
Bibliographic record
Abstract
A paradigm shift in the transportation sector is being witnessed due to resurgence of electric vehicles (EVs). They are ideally considered to be non-polluting and eco-friendly, however it has its own demerits of overloading existing grid infrastructure and, could significantly contribute towards carbon emissions depending on the source used for charging them. The ideal solution to counteract the critical shortcomings is by developing a charging infrastructure integrated with renewable energy technology. \nThe main aim of this thesis is to design such a charging station coupled with solar energy for urban cities. Simplified EV load models are developed by considering most popular commercial EV in the market. The designed solar powered charging station is tested with the developed EV load models and, would be located in selected urban cities within Ontario. \n \nFirstly, literature review on effects of EV charging directly from grid, benefits of EV charging with renewables, and amalgamation of EV charging with Net Zero (NZ) concepts is introduced. Later, three types of system architectures are studied for solar powered charging station. Selection of architecture for this work is done considering the economics of installation, and operation. Optimization in design of solar powered charging station is presented by varying the power ratio and, obtaining the annual energy yield for different types of orientation considering all EV load models. Then, NZ Photovoltaic (PV) enabled charging station is designed and, is tested with selected load models and, energy economic analysis is done for all designs. Finally, recommendations are made encompassing the selection of net-zero based charging stations along with economic considerations and its short and long term effects on environment.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".