Toward Intelligent Power Systems: 5G-Enabled Hybrid Control of Distributed Energy Resources in Microgrids and Virtual Power Plants
Bibliographic record
Abstract
The accelerating global shift toward renewable energy requires resilient and intelligent power systems ca-pable of integrating Distributed Energy Resources (DERs) at scale. This dissertation presents the design, implementation, and validation of a hybrid control framework for Micro-Grids (MGs) and Virtual Power Plants (VPPs), leveraging 5G cellular networks for real-time supervisory communication. The research fo-cuses on the coordinated operation of Grid Forming Inverter (GFMI) and Grid Following Inverters (GFLIs), enabling dynamic transitions between islanded and grid-connected modes while maintaining system stability and resilience. A modular inverter-based microgrid platform was developed using TI DSP controllers, power electronic converters, and a low-latency 5G communication infrastructure employing MQTT protocols. The proposed control strategy combines fast local agents response with remote supervisory agent commands transmitted through Bell’s commercial 5G network in Canada, enhancing flexibility, scalability, and reliability. Labo-ratory experiments validate the system under diverse operational scenarios, including load variations, grid connection, network disconnection, resynchronization, and parallel inverter power-sharing. Feasibility tests conducted over Bell’s 5G network in Canada show end-to-end communication latency in the range of 40–250 ms, which is well suited for supervisory control, system monitoring, and remote setpoint updates. Experimental results confirm stable inverter operation and seamless mode transitions under dynamic conditions. The findings contribute to advancing distributed energy systems by integrating modern communication networks with conventional control architectures in MGs and VPPs.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".