Voltage Regulation and System Loss Minimization in Practical Distribution Networks using Advanced DER Controls
Bibliographic record
Abstract
The increasing penetration of distributed energy resources (DERs) in distribution networks introduces operational challenges, particularly in voltage regulation. IEEE Std 1547-2018 defines advanced grid-support functions such as volt-var, watt-var, and volt-watt control to enhance voltage stability and reactive power management. However, their practical deployment remains limited, necessitating detailed modeling and assessment. This study evaluates volt-var, wattvar, fixed power factor (PF), and unity PF modes for voltage regulation and power loss reduction in a practical distribution feeder under varying operational conditions. A time-series analysis is conducted using a modeled feeder with DERs placed at different locations-near the substation, mid-feeder, and feeder endpoints-to assess the impact of location and $X / R$ ratio. Results indicate that volt-var mode is most effective near substations and mid-feeder locations, while fixed $\mathbf{P F}=\mathbf{- 0. 9}$ for inverter-based DERs offers improved voltage control at feeder endpoints. However, voltage deviations remain outside acceptable limits, requiring further evaluation. These findings provide insights into optimal DER control strategies for enhanced voltage regulation and grid efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".