Improving LV networks hosting capacity via manual phase switching of households – a Monte-Carlo analysis
Bibliographic record
Abstract
Due to the rapid increase in residential PV capacity, it gets more and more difficult for a Low Voltage 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. Monte-Carlo simulations were performed to statistically assess the benefits of using manual phase switching of households as a mitigation measure. The best implementation approach was also investigated. Based on typical LV feeder scenarios found in Belgium, it was shown that a single 1ph switch can already provide a significant gain in voltage reduction . However, 3-wire networks are by nature less prone to unbalance-related issues and stand thus less to benefit from phase switching. A concrete decision-making criterion for the relevancy of a 1ph switch was found: should the unbalance between each phase’s maximum yearly voltage be larger than 6% or should the average of the phases’ maximum yearly voltage be smaller than 1.08pu, a targeted 1ph switch would be useful and advisable. The smaller the average voltage, the higher the chance that a single 1ph switch will solve the overvoltage issue altogether. The best choice for a 1ph switch is a household from the phase with the highest maximum voltage to the phase with the lowest maximum voltage, the farthest along the feeder as possible.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".