Optimized Volt/VAR Control for Inverter-Interfaced Distributed Energy Resources in Compliance with IEEE 1547 Standards
Bibliographic record
Abstract
The increasing integration of distributed energy resources (DERs), such as photovoltaic(PV) systems and battery storage, into distribution networks necessitates advanced inverter controls to maintain stable grid operations. IEEE Standard 1547 permits smart inverter functionalities, including Volt/VAR control, enabling DERs to autonomously manage voltage fluctuations caused by varying load and generation conditions. However, configuring these Volt/VAR settings optimally is challenging, as default parameters provided by standards may not ensure optimal performance or dynamic stability. This paper proposes a customized, per-node Volt/VAR control optimization framework for single-phase distribution feeders, adapting inverter control parameters based on expected short-term load and solar generation patterns. Leveraging a projected gradient descent optimization approach, proposed methodology guarantees dynamic stability by approximating feasible operational parameters within a convex polytope and ensuring full compliance with IEEE 1547 voltage and reactive power specifications. Comprehensive numerical evaluations conducted on the IEEE 141-bus distribution system using realistic operational data demonstrate the effectiveness and practicality of the proposed method, highlighting improved voltage stability and regulation compared to conventional approaches.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".