Stormwater Storage Pond Configuration for Hydropower Solutions: Adaptation and Optimization
Bibliographic record
Abstract
The purpose of this paper is to analyze different storage pond configurations used to manage the water inflow from storm events and to establish a methodology to optimize the energy production using this water. The case study area is a catchment based on the Alcântara zone, in Lisbon, Portugal. This is an urban area, which faces severe occurrences of storm events. These events are caused by a non-efficient drainage system, and the proximity of the river and the sea, which cannot handle large storm event inflows. Due to climate change effects, the storm rainfall hyetographs are modified and runoff in this urban area has increased for the last decades causing major inundations.The Danish Hydraulic Institute (DHI) MIKE BASIN (MB) and Bentley PondPack software are used to define solutions for this case study by embodying some of the Alcântara’s catchment characteristics. Different relevant components used to create the models are presented. In order to model completely the studied area, several physical elements such as reservoirs or hydropower plants are included. The Nedbør-Afstrømnings-Model (NAM model) is also presented. It is a rainfall-runoff simulator, included in DHI MB, which can create runoff time series from rainfall time series. The methodologies used to optimize energy production in each of case studies developed are detailed. Results are presented showing that some ponds configurations permit to produce more energy than others and the production of energy can be maximized by optimizing the hydropower production target demand.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".