Experimental Validation of Hybrid Local and Remote Supervisory Control of Virtual Power Plants Over 5G Cellular Networks
Bibliographic record
Abstract
This paper explores the integration of Distributed Energy Resources (DER) into power systems, emphasizing the critical role of communication. Inverters play an essential role in the energy conversion process. As the penetration of Renewable Energy Resources (RERs) continues to grow, these inverters are increasingly required to dynamically form microgrids or interconnect into larger power systems, with control information likely transmitted over cellular networks. This study introduces a hybrid control approach for multiple inverters, integrating local and supervisory controls to optimize power sharing and ensure grid stability. Unlike traditional Wi-Fi or 4G-based communication methods, 5G offers significant advantages such as ultra-low latency (as low as 1 ms), network slicing for customized Quality of Service (QoS), high device density support, and seamless mobility. These capabilities make it especially suited for real-time control in distributed power systems. In this work, a 5G-enabled supervisory control architecture was developed and experimentally validated using parallel inverters operating in a virtual power plant (VPP) configuration. Real-world tests demonstrated that supervisory commands transmitted over 5G maintained stable inverter synchronization and effective load sharing under dynamic operating conditions. Experimental results validate the effectiveness of the proposed control strategy, with successful validation under both local and supervisory control modes.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".