Improving LV networks hosting capacity via the use of PV and BESS inverter controls – a Monte-Carlo analysis
Bibliographic record
Abstract
Due to the rapid increase in residential PV capacity, it gets more and more difficult for an 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. 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. The focus in this paper is the use of P-Q control strategies of residential PV inverters, which includes:constant tan(φ), cos(φ) = f(P), Q(U) and P(U). Even though only some countries already have a clearly defined regulatory framework for such use of residential PV inverters, it is worthy to analyse the potential benefits of this approach. A parametrized LV feeder was modelled in an electrical modelling tool and Monte-Carlo simulations were performed, with variations of different input parameters and randomly assigned consumer/prosumer load and injection profiles based on an existing set of households’ data, as well as different strategies of P-Q inverter control. The risk for overvoltage occurrences was assessed (mostly focusing on periods with maximum irradiance). Besides the impact of different parameters such as the feeder’s length or the number of houses with PV on the feeder, a specific attention was paid to the beneficial impact of using the different P-Q control strategies. Dedicated KPI’s regarding the impact on prosumer households were analysed and the different P-Q control strategies were discussed.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".