Data-driven EV charging infrastructure with uncertainty based on a spatial–temporal flow-driven (STFD) models considering batteries
Bibliographic record
Abstract
Electric vehicles (EVs) play a vital role in meeting the United Nations Sustainable Development Goals by 2030, primarily by reducing emissions and enhancing air quality. Properly positioning EV charging infrastructure within urban settings is essential to facilitate a shift toward sustainable transportation. As EV adoption grows, microgrids (MGs) face new challenges, including increased power losses, deterioration of voltage profiles, and voltage stability issues. Incorporating energy storage systems (ESS) can help address these challenges by improving overall system efficiency. This research proposes a comprehensive planning methodology that optimizes the placement of EV charging stations by considering traffic flow patterns over space and time. Additionally, a stochastic modeling approach is introduced to identify the optimal locations for ESS, taking into account uncertainties such as variable electrical loads and the intermittent nature of renewable energy sources (RESs). The ESS placement is modeled as a multi-objective optimization problem, aiming to enhance voltage stability, reduce power losses, and improve voltage profiles. The developed mathematical frameworks and algorithms facilitate the optimal sizing and siting of both EV charging stations and ESS. An economic evaluation of the MG, including the costs associated with ESS integration, is also incorporated. The effectiveness of this integrated planning approach is demonstrated through a case study on a representative transportation network, showing its capacity to mitigate the adverse impacts impacts of EV integration on microgrid performance. Furthermore, the study employs a stochastic framework to simulate and analyze the uncertainties inherent to electrical loads and renewable energy generation. The obtained results show that integrating ESS significantly improved voltage stability, with the minimum voltage stability index (VSI) value increasing from 0.5848 to 0.8631. It also reduced power losses by 33.34%, decreased transformer loading by 19.5%, and enhanced economic efficiency. Additionally, Sodium-Nickel Chloride (Na-NiCl2) offered the highest savings of 6.99%, demonstrating significant technical and financial benefits for the MG.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".