Battery Scheduling and Smart Inverter Volt/Var Control for Voltage Stability in Microgrids
Bibliographic record
Abstract
As distribution grids decarbonize and electrify, maintaining voltage quality under integrated inverter-based distributed resources has become challenging due to the localized voltage sensitivities. These dynamics call for adaptive, data-driven coordination of battery storage and smart inverters that can act on fast timescales while respecting network limits. This paper presents a microgrid control and optimization framework that coordinates battery energy storage with smart-inverter volt/var functionality to improve voltage regulation and operating costs in a building-centric microgrid (MG). The test system adapts the IEEE 13-node feeder to represent building load profiles, integrating solar photovoltaic (PV), smart inverters, and grid-connected battery energy storage. The proposed method schedules battery charging/discharging while enabling autonomous volt/var support at PV nodes, thereby flattening voltage profiles, reducing technical losses, and shifting energy procurement away from pricey periods. Case studies on the modified IEEE 13-node feeder show that coordinated battery dispatch and volt/var control maintain bus voltages within acceptable limits under variable load and solar PV generation, while lowering total energy costs relative to baseline system operation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".