Improving LV networks hosting capacity via the use of batteries – a Monte-Carlo analysis
Bibliographic record
Abstract
The rapid increase in residential PV capacity is causing significant overvoltage issues, making it challenging for the grid t o absorb power during peak hours. This triggers inverter stalls as a security measure, causing concerns to Belgian DSOs and preventing prosumers from injecting power into the grid. In collaboration with Belgian DSOs and via the use of metered consumption and injection data, dedicated Monte-Carlo analyses were performed to investigate mitigation measures, via parametric simulations focused on the issue at hand. The focus in this paper is put on the use of batteries to improve LV networks hosting capacity. This approach was implemented to study different questions arising from the use of BESS: e.g., optimal ratings and location, use of P-Q capability of the BESS,… Results from thousands of simulations highlighted that a single three-phase 45 kWh/15 kVA/phase BESS installed at the end of the feeder can regulate voltage mainly by implementing phase-balancing, i.e. absorbing power on a phase and injecting it on another, with very limited storage (< 1.5 kWh) in most of the simulated cases. With these ratings, a simple Q control algorithm also allows to regulate voltage in > 99% of the cases. The upside is the net-zero energy exchange limiting the battery energy capacity requirements. However, this is achieved at the expense of higher kVA requirements due to the high R/X ratio of distribution networks compared to the transmission level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".