Resiliency constraint proactive scheduling against hurricane in multiple energy carrier microgrid
Bibliographic record
Abstract
Abstract Hurricane, as one of the most frequent natural events, causes damage to the infrastructure of the upstream networks of multiple energy carrier microgrids (MECMs). Thus, the supply continuity of critical loads is threatened due to sudden islanding of MECMs from the upstream networks. Therefore, providing a framework to enhance the resilience of MECMs against this threat is necessary. In response to this issue, this paper presents a proactive optimal operation scheduling approach that is constrained to the feasible islanding of MECM from the upstream networks as well as the continuous suppling of the critical loads until the return of the upstream networks. Proposed resiliency constraint proactive scheduling is formulated as a mixed integer linear programming (MILP) that considers the interdependence between all three electric, gas and heat networks. The demand responsibility of all three electric, thermal and gas loads is used to consider the satisfaction of the consumer beside economic and resilient operation. Furthermore, both normal operation and contingency‐based uncertainties are taken into account and captured using a robust optimization method. Then in order to evaluate the resilience of the MECM, feasible islanding and preparedness indices are proposed. Finally, the effectiveness of proposed approach is illustrated using an MECM test.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".