Intelligent Agent-Based Energy Management System for Islanded AC–DC Microgrids
Bibliographic record
Abstract
This article proposes an advanced solution for the energy management of the islanded ac-dc microgrids using an intelligent agent approach. This approach provides islanded ac-dc microgrids with three main operating agents (i.e., ac microgrid, dc microgrid, and system operator agents) communicating with each other at each operating time interval to increase system levels of performance, efficiency, and reliability. Bidirectional communication allows data collection and control command flow between ac microgrid, dc microgrid, and system operator to not only minimize ac and dc operational costs but also minimize ac-dc conversion costs, perform optimal demand shifting and minimize load shedding using a progressive strategy. For the optimization purpose, this article uses an advanced multiobjective particle swarm optimization engine to effectively solve the problem of each agent. To test the precision and operational performance of the proposed solution, a 33-node islanded ac-dc microgrid with diverse ac-dc generating resources and loads is studied.
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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 | 0.001 | 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".