Tackling PV-caused overvoltages - synthesis of Monte-Carlo simulation outputs and multi-criteria assessment of mitigation measures
Bibliographic record
Abstract
Due to the rapid increase in residential PV capacity, it gets more and more difficult for a Low Voltage (LV) grid to absorb power at peak hours due to overvoltage issues causing concerns to DSOs and prosumers, who are temporarily unable to inject power into the grid. Multiple mitigation measures exist against PV-caused overvoltages in LV distribution grids, among which the following have been investigated via statistical analyses (Monte-Carlo simulations) in previous papers: larger neutral conductor sizing, PV inverter P-Q control, manual tap change of MV/LV transformer, manual household phase swapping, use of an On-Load Tap Changer (OLTC) of MV/LV transformer, use of a neutral point compensator, use of a line voltage regulator/stabilizer or use of centralized battery. A comparison of those options is proposed in this paper and insights are provided based on M-C simulation results, notably regarding the best implementation practices. The comparative analysis notably highlighted that some technologies can either be very effective or not at all depending on the feeder circumstances, such as the potential role of phase unbalance in the overvoltage issue. A simplified decision-making process for DSOs is also proposed to help in the selection of the most suitable solution on a case-by-case approach.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".