Model Predictive Control for Grid-Tied Multi-Port System With Integrated PV and Battery Storage
Bibliographic record
Abstract
This paper proposes a unified model predictive control (MPC) scheme for the integrated photovoltaic (PV) and battery storage system, where both of them are directly connected to the utility grid with high conversion efficiency through a multi-level neutral-point-clamped (NPC) inverter based multi-port interface. In such a system, the individual/unequal input voltage from each DC port raises control challenges, resulting in asymmetric voltage vector distribution and increased modulation complexity in the AC side. In this case, the finite-control-set MPC (FCS-MPC) scheme is proposed to make the power management be liberated from the modulator design thanks to the natural advantage, i.e., the direct control property without using a modulator. The multivariable-based cost function is designed for the AC side to regulate the injected grid current well to meet the IEEE 519–2014 standard. On the other hand, to proceed with the normal DC-side power flow, the capacitor voltage of each port is modeled, predicted, and also regulated through the unified cost function in the MPC framework. As a result, each PV array can work at the individual maximum power point (MPP) and the battery can be automatically charged/discharged to compensate for the power difference. The five-level four-port inverter-based simulation and three-level dual-port inverter-based experiment are conducted to verify the multi-mode operation of the integrated system and the advantages of the proposed controller.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".