Day-ahead planning optimization framework for cascaded hydro-wind-photovoltaic hybrid systems considering time delay effects
Bibliographic record
Abstract
The time delay in cascade hydropower stations refers to the time required for water to travel from the upstream reservoir to the downstream reservoir. Although some studies consider time delays in hydro-wind-photovoltaic hybrid system models, comprehensive research on their impacts and mitigation strategies remains limited. These delays hinder the joint optimization of hydropower, wind, and photovoltaic resources, reducing system benefits and increasing operational risks. This phenomenon is referred to as the time delay effect. This study proposes a day-ahead planning optimization framework to mitigate the time delay effect and improve system benefits and reliability under multiple uncertainties. This framework incorporates joint and updated optimizations to address challenges posed by the time delay effect. A case study on the Yalong River Basin hydro-wind-photovoltaic system shows that the proposed framework enhances generation benefits by 2.79% over traditional methods and reduces the power shortage rate by 43.19%. Additional multi-objective scheduling experiments assess the framework’s risk control capabilities and the variations in scheduling across different schemes. Based on these analyses, an improvement strategy for this system is developed and validated to significantly reduce power shortage rates while minimizing revenue losses.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".