Fuzzy constrained optimization of eco‐friendly reservoir operation using self‐adaptive genetic algorithm: a case study of a cascade reservoir system in the Yalong River, China
Bibliographic record
Abstract
ABSTRACT With the increasing demands for hydropower development in China, it is particularly important to implement eco‐friendly reservoir operation in consideration of the vulnerability of downstream aquatic environment. In this paper, a fuzzy constrained nonlinear programming model is presented. Fuzzy programming is used to deal with the inherent imprecision and vagueness in constraints, and a self‐adaptive genetic algorithm with simulated binary crossover is proposed for searching for the optimal reservoir operating rules. First, the fuzzy reservoir operation model is transformed into two deterministic sub‐models. Self‐adaptive genetic algorithm is then used to generate optimal solutions by applying penalty functions to integrate constraints into the objective function to form the fitness function. To achieve a final compromise between the fuzzy objective and the constraints, a ‘max‐min’ decision principle is incorporated into the optimization process to obtain satisfactory operating schemes. The methodology is demonstrated through a cascade system of reservoirs in the Yalong River, southwest China. A monthly reservoir operation model, which considered downstream ecological flow requirements as fuzzy constraints, is developed to optimize eco‐friendly reservoir operation with the objective of maximizing total hydropower generation. Monthly operating rules are generated for two cascade reservoirs, and the optimal hydropower generation is also obtained. Results indicate that the proposed approach can effectively improve the operation of the cascade reservoir system and fulfill the flow requirements of the downstream ecosystem that are expressed as fuzzy sets. It is a useful tool for generating optimal strategy to achieve an eco‐friendly reservoir operation under uncertainty. Copyright © 2011 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".