Impacts of Electric Vehicle Integration on Transportation and Energy Systems: Case Study in Iran
Bibliographic record
Abstract
Electric vehicles (EVs) have the potential to revolutionize the energy and transportation sectors, yet widespread adoption faces challenges, notably the complexity of managing energy demand. While prior studies have modeled EV impacts in developed economies, little work has analyzed such impacts under the constraints of fossil fuel-dominated, subsidy-heavy systems like Iran. So, this paper investigates the impacts of EV integration on Iran's energy and transportation infrastructure, advocating that EVs are instrumental in decarbonizing and grid balancing. Our focus turns to Iran's energy landscape as a compelling case study for a fossil-fuel-rich country, due to its specific geographical aspects and unique energy sector challenges. The study extensively analyses historical peak demand data and national statistics, underscoring the urgent need for more sustainable energy management practices and the modernization of transportation systems. The analysis emphasizes the critical challenge posed by surging peak power demand in Iran while highlighting the pivotal role that EVs could play in reshaping Iran's transportation and energy sectors. Numerical analysis reveals that managing EV energy through V1G and V2G can help alleviate the peak demands, providing a flexible alternative to traditional network upgrades. Moreover, the calculated estimates of peak power demand for unconstrained charging versus the impact of V1G and V2G can assist decision-makers in assessing future energy flexibility requirements, identifying strategies to overcome potential barriers to EV adoption, and exploring different scenarios.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".