Multi-agent Control Strategy for Microgrids using Petri Nets
Bibliographic record
Abstract
Smart Microgrids require innovative methods to provide decentralized multi-layer autonomous control in order to activate their elements to operate efficiently. This paper presents a coordinated control scheme based on multi-agent systems to improve the operation of a microgrid (MG) through Petri Nets (PNs). Two control layers are proposed: in the lower control layer, the switching of loads, storage devices, and distributed generation devices (DGs) are implemented; while in the upper layer, the coordinating agents consider the demand response and price dynamics in order to optimize the operation of the system. The MG includes photo-voltaic (PV) generation, wind power, and a battery storage system. Furthermore, the studied system has two MGs, connected through a DC bus linked to the grid. The coordinating system considers the operation of DGs, the charge and discharge of storage devices, the critical loads, under optimization criteria using energy prices. Switching controls for DG devices and loads are handled as discrete events and will be modeled using PNs.
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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 | 0.000 | 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) | 0.000 | 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".