Distributionally robust multi‐period energy management for CCHP‐based microgrids
Bibliographic record
Abstract
To improve the overall energy utilisation efficiency, the research of combined cooling, heat, and power (CCHP)‐based microgrids has become prevalent recently. However, the increasing penetration of uncertain renewable generation such as wind power brings new challenges to CCHP‐based microgrids energy management. In this study, the authors propose a two‐stage multi‐period distributionally robust energy management model for CCHP‐based microgrids, and this model considers the non‐anticipativity of uncertainty in dispatch process. A second‐order conic representable ambiguity set is designed to capture the uncertainty of wind power. Based on linear decision rule approximation, the proposed problem is transformed into a tractable mixed‐integer second‐order conic programme problem. Case studies and comparison experiments are conducted in the Matlab environment with real‐world data to validate the performance of the proposed approach. Particularly, the proposed method achieves a less conservative solution and smaller cost compared with a robust optimisation method with the same reliability guarantee. In addition, it is more reliable than the deterministic method which does not consider uncertainty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".