Simulation-based probabilistic-harmonic load flow for the study of DERs integration in a low-voltage distribution network
Bibliographic record
Abstract
The integration of distributed energy resources (DERs) and, therefore, power electronic devices into distribution networks leads to harmonic distortion injection. However, studying harmonic distortion solely through deterministic approaches presents challenges due to the inherent random behavior of DERs. This study introduced a strategy that leverages PowerFactory's harmonic load flow tool. By combining it with Python co-simulation, probabilistic load flows can be developed. These load flows utilize current sources to represent harmonic distortion emitters with predefined harmonic spectra. The proposed strategy was implemented on a real network, where two different capacities of DERs were integrated at various locations within the network. The distributions for the total harmonic distortion of voltage ($ THD_{v} $) and the total harmonic distortion of current ($ THD_{i} $) were obtained 24 hours a day in nodes and lines of the network. The procedure allowed considering the uncertainty associated to the DERs integration in distribution networks in the study of harmonic distortion, which, speaking from a simulation approach, is scarce in the literature.
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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) | 0.001 | 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 | 0.001 |
| 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".