Graph‐theoretic reliability index for assessing the impact of capital and operating cost constraints in designing reliable energy hubs
Bibliographic record
Abstract
Abstract Component failures in integrated heating and cooling networks impact reliable energy supply. This work studies reliability of heating and cooling systems constrained either by the annualized capital cost (ACC) or by the annualized total cost (ATC). A novel approach for assessing the supply reliability, representing energy networks as stochastic graph networks, is introduced, thereby enabling imposition of operational uncertainties and constraints as well as budgetary limitations as the graph networks' features. The reliability index is defined as the average percentage of the demands that are met. Single or multiple component failures may occur anytime; the reliability evaluation method is not restricted to one failure at a time. Examples of two different energy supply networks, maximizing the reliability under ACC or ATC constraints, are presented to illustrate the methodology. It is shown that, under ATC or ACC constraints, the 100% reliability and maximum consecutive loss of load hours (MCLOLH) equal to zero can be attained via a wide range of equipment sizes provided the thermal storage is sized correctly. Maps representing relative costs of heating, cooling, and storage, for the given system structure, help decision makers to determine equipment sizes if scaling up/down the system, while maintaining a high level of reliability.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".