Optimal Decentralized Microgrid Coordination via the Schur’s Complement and S-Procedure
Bibliographic record
Abstract
The evolving landscape of the electricity sector along with increasing environmental concerns necessitate modern power grids to be more efficient, sustainable, and adaptive. Microgrids are typically composed of distributed energy sources which have great potential for enabling energy independence, sustainability, and flexibility. However, practical difficulties that deter the widespread deployment of microgrids include the unpredictability of local generation sources (e.g., renewables) and the lack of inertia that is naturally present in systems containing bulk synchronous plants. In this paper, we propose a near real-time microgrid coordination algorithm that allows actuating components to adapt to changing system conditions. We account for the electrical dependencies and limits in microgrid systems by constructing voltage/current balance relations in the dq0 frame and applying strategic decompositions to invoke the Schur's complement and S-procedure that allow for zero duality gap. We highlight the convergence, feasibility, and scalability features of the proposed decentralized algorithm via theoretical and comparative practical simulation studies.
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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".