Three‐based level model to determine optimal scheduling of the MG integrated operation using Benders decomposition
Bibliographic record
Abstract
In this study, an optimal scheduling model is proposed to perform energy management in a microgrid (MG) including distributed energy resources in both grid‐connected and islanded modes. To this end, a tri‐level optimization model has been presented, in which Benders method decomposes the original problem into the main problem (the first level), sub‐problem (SP) (the second level) and optimal SP (the third level). The first and second levels model the MG operation in grid‐connected and islanded modes, respectively. Finally, the third level directs the obtained feasible solution of the second level to an optimal solution in the islanded mode. In the proposed model, the effect of an adjacent MG is also considered, and a call‐option contract is used to model the transaction power between networked MG. While it is assumed that they are connected to each other, they only exchange power in the islanded mode to prevent the arbitrage state. The aim of the proposed model is to present a flexible and integrated operation of the MG, which has the ability to work in both grid connected and island modes. Uncertainties of the problem are modelled using the two‐point estimation method (2 m + 1).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".