Cooperative microgrid networks for remote and rural areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Microgrids (MGs) with renewable energy resources have shown competitive operational advantages for smart grid integration. In the wake of recent blackout events, more interest has been directed on the deployment of MGs in rural and remote areas where main grid connectivity may not be possible. This work focuses on the development of an autonomous distributed framework for cooperation amongst a set of MGs without support from a traditional centralized grid. This work proposes a distributed algorithm that supports autonomous operation, enhances cost efficiency, and increases reliability of the overall system. This paper uses a complete information game theoretic approach via coalition formation formulation. The effects of rate policies on the dynamics of cooperation are also investigated in this work. The proposed distributed algorithm empowers the MGs to coordinate and match significant part of energy demand and supply with cooperating MGs. Results of this work show cooperation gains for the microgrid network after implementing the proposed algorithm.
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.
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 it