Utilizing Rooftop Renewable Energy Potential for Electric Vehicle Charging Infrastructure Using Multi-Energy Hub Approach
Bibliographic record
Abstract
Electric vehicles (EV) have the potential to significantly reduce carbon emissions. Yet, the current electric vehicle charging infrastructure utilizes electricity generated from non-renewable sources. In this study, the rooftop area of structures is analyzed to assess electricity that can be generated through solar- and wind-based technologies. Consequently, planning an electric vehicle charging infrastructure that is powered through ‘clean’ energy sources is presented. We developed an optimal modeling framework for the consideration of Renewable Energy Technologies (RET) along with EV infrastructure. After examining the level of technology, a MATLAB image segmentation technique was used to assess the available rooftop area. In this study, two competitive objectives including the economic cost of the system and CO2 emissions are considered. Three scenarios are examined to assess the potential of RET to meet the EV demand along with the Abu Dhabi city one while considering the life-cycle emission of RET and EV systems. When meeting only EV demand through Renewable Energy Technologies (RET), about 187 ktonnes CO2 was reduced annually. On the other hand, the best economic option was still to utilize grid-connected electricity, yielding about 2.24 Mt CO2 annually. In the scenario of meeting both 10% EV demand and all Abu Dhabi city electricity demand using RE, wind-based technology is only able to meet around 3%. Analysis carried out by studying EV penetration demonstrated the preference of using level 2 AC home chargers compared to other ones. When the EV penetration exceeds 25%, preference was observed for level 2 (AC public 3ϕ) chargers.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".