Two‐stage robust optimal scheduling of cooperative microgrids based on expected scenarios
Bibliographic record
Abstract
Cooperative microgrids (CMGs) can effectively solve the energy interaction between microgrids (MGs) while increasing the penetration rate of renewable energy systems (RESs) and reducing the interaction frequency with the grid. However, the uncertainty of RESs will bring new challenges to the energy management and economic dispatch of CMGs, especially in increasing the number of MGs connected to the grid. Taking into account this uncertainty, it is extremely unlikely that the forecast uncertainty information will be at the worst values in every period, and this forecast uncertainty information is near the expected values in most cases. Therefore, a two‐stage robust optimal model under expected scenarios for CMGs is proposed in this study to improve the conservatism of traditional models and minimise the daily cost. In this model, the first‐stage decision results (FDRs) are determined by minimising the daily cost of CMGs under the expected scenarios. The proposed model is transformed based on two‐stage zero‐sum game theory and dual theory, a column and constraint generation algorithm is first used to test the robust feasibility of the FDR, and the second‐stage decision results can be obtained without changing the FDR. Case studies verify that the proposed model can effectively solve energy transactions between MGs while mitigating the uncertainty disturbances in the operation of CMGs.
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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".