Integration of risk, flexibility, and resilience in the optimization of water–energy nexus
Bibliographic record
Abstract
Abstract Co‐location of power plants and desalination systems allows for a reduction in operational expense through energy integration. Furthermore, augmenting fossil‐based power plants with solar energy provides a means of reducing the carbon footprint of electricity generation. It is also critical to protect the combined energy–water system against internal and external risk factors to maintain a reliable supply of both electricity and water. Therefore, a systematic approach for assessing and mitigating risks is needed. Because of the complex water–energy interactions, a superstructure representation is created and a quantitative risk assessment is conducted to show potential risk factors that target specific sub‐systems. A surrogate model of the flexibility index analysis is built in order to optimize the superstructure for both cost and flexibility objectives. Finally, the generated design is simulated against disruption scenarios to obtain its resilience against various risk factors. This approach is applied to a case study on the Kuwait water–energy plant to show how the developed approach can help decision‐makers create operational strategies to protect against risk in a cost‐effective manner.
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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".