Adaptive Self-Adequate Microgrids Using Dynamic Boundaries
Bibliographic record
Abstract
Intensive research is being directed at microgrids because of their numerous benefits, such as their ability to enhance the reliability of a power system and reduce its environmental impact. Past research has focused on microgrids that have predefined boundaries. However, a recently suggested methodology enables the determination of fictitious boundaries that divide existing bulky grids into smaller microgrids, thereby facilitating the use of a smart grid paradigm in large-scale systems. These boundaries are fixed and do not change with the power system operating conditions. In this paper, we propose a new microgrid concept that incorporates flexible fictitious boundaries: “dynamic microgrids.” The proposed method is based on the allocation and coordination of agents in order to achieve boundary mobility. The stochastic behavior of loads and renewable-based generators are considered, and a novel model that represents wind, solar, and load power based on historical data has been developed. The PG&E 69-bus system has been used for testing and validating the proposed concept. Compared with the fixed boundary microgrids, our results show the superior effectiveness of the dynamic microgrid concept for addressing the self-adequacy of microgrids in the presence of stochastically varying loads and generation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".