Dynamic Planning of Water Resource and Electric Power Systems under Uncertainty
Bibliographic record
Abstract
Hydropower plays an important role in electric power systems. It not only interacts with many non-hydropower generation activities but also competes with other water users (industrial, commercial, residential, and agricultural) for limited water resources. Therefore, the objective of this study is to investigate an optimized water allocation scheme within a water resource and electric power management system through developing an inexact water resource and electric power systems planning model (WPEM). WPEM is based on the interval-parameter programming and mixed-integer programming techniques; thus, it can deal with dynamics of capacity expansion and uncertainties associated with system management. The developed method is then applied to a power system with water shortage issues in the future. The results of scenario analysis indicate that WPEM could help get insights into the tradeoff between system benefit and water utilization as well as that between system benefit and environmental concern. Thus, the modeling solutions could be used by water managers for supporting the effective allocation of water among power production, industrial process, agricultural irrigation, commercial activity, and residential use in case of water shortage.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".