Impact of DG on Voltage Unbalance in Canadian Benchmark Rural Distribution Networks
Bibliographic record
Abstract
Distribution networks are three-phase systems supplying electricity to loads. While, ideally, the load at each point of the network would be equally distributed among the three phases, this is not the case in practice. The three-phase voltages and currents are thus unbalanced due to the different magnitudes of loads at each phase. The integration of single-phase distributed generation (DG), e.g., photovoltaic (PV) units installed at secondary networks, adds more challenges to the voltage unbalance in distribution networks. This paper investigates through simulations the impact of DG on the voltage unbalance in Canadian benchmark rural distribution networks. The maximum penetration levels of DG that can be integrated into distribution networks are determined taking into consideration the standard limits of voltage unbalance, operating voltage limits, and thermal ratings of the feeder. Different configurations of voltage regulators and DG are studied. Simulation results showed that the voltage unbalance factor (VUF) decreases with the integration of three-phase DG especially when high penetration levels of DG are located close to the end of the main feeder. Up to 24 MW of three-phase DG can be connected to the main feeder, which is 154% of the total peak load, without violating any of the limits. It was also found that the maximum size of a single-phase DG can be at least 3 times the peak load of a given node at any single-phase lateral.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".