Resiliency Analysis of Hybrid Energy Systems within Interconnected Infrastructures
Bibliographic record
Abstract
There are world tendencies to implement interconnected infrastructures of energy-water-waste-transportation-food-health-social systems to enhance the overall performance in normal and emergency situations where there are multiple interactions among them with possible conversions and improved efficiencies. Hybrid energy systems are core elements within interconnected infrastructures with possible conversions among electricity, thermal, gas, hydrogen, waste, and transportation networks. This could be improved with storage systems and intelligent control systems. It is important to study resiliency of hybrid energy systems within interconnected infrastructures to ensure reduced risks and improved performance. This paper presents framework for the analysis of resiliency layers as related to protection layers. Case study of hybrid energy system as integrated with water, waste, and transportation infrastructures is presented where different resiliency and protection layers are assessed. Performance measures are modeled and evaluated for possible interconnection scenarios with internal and external factors that led to resiliency demands. Resiliency layers could trigger protection layers under certain conditions, which are evaluated to achieve high performance hybrid energy systems within interconnected infrastructures. The proposed approach will support urban, small, and remote communities to achieve high performance interconnected infrastructures for normal and emergency situations.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".