Multiagent Supervisory Control for Power Management in DC Microgrids
Bibliographic record
Abstract
This paper proposes multiagent supervisory control for precise power management in isolated dc microgrids. Two power management aspects are considered: 1) equal power sharing, which is realized via a proposed distributed equal power sharing algorithm; and 2) optimal power dispatch, which is achieved through a proposed distributed equal incremental cost (DEIC) algorithm. Both algorithms offer the additional advantage of the ability to restore the average system voltage to its nominal value. The proposed algorithms are based on the application of the average consensus theory along with voltage sensitivity analysis. Each distributed generation (DG) unit exchanges information with its neighbors, thus locally updating its no-load voltage setting to achieve the supervisory control objectives. The incorporation of DG droop-based control renders the proposed algorithms fully distributed with a reduced number of agents. The stability of the proposed algorithms is addressed, as well as the convergence of the proposed DEIC algorithm. Real-time OPAL-RT simulations demonstrate the effectiveness of the proposed algorithms in a hardware-in-the-loop application.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".