Impact of the Electrification of Vehicles and Integration of Solar Photovoltaic Systems on Low-voltage Distribution Networks
Bibliographic record
Abstract
The increasing integration of electric vehicles (EVs) and solar photovoltaic (PV) systems may impose challenges for the operation of power distribution networks such as network overloading and voltage issues. On the contrary, operating solutions with these low-carbon technologies may provide grid support and help mitigate grid congestion. This paper presents a study on the impact of the integration of EVs and solar PV generation on voltage issues, network loading and power losses in a low-voltage (LV) power distribution network. Quasistatic power-flow analysis is carried out for different levels of electrification of vehicles and solar PV systems penetration. A Monte Carlo simulation is used to capture uncertainty and variability both in load and generation. Case studies in a three-phase four-wire 18-bus LV distribution network demonstrate the grid impact for varying levels of EV integration with level 1 (L1) and level 2 (L2) charging, and solar PV system penetration levels. Switching from L1 to L2 charging lead to a 30 % increase in power losses during the summer and a 36 % increase during the winter, assuming 100 % EV penetration. Higher solar PV system penetration levels resulted in more significant reductions in active power losses during the summer season. Additionally, results also highlighted the significant impact of L2 charging on transformer loading and voltage.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".