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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".