Co-Location Potential of Floating PV with Hydropower Plants: Case Study in Ecuador
Bibliographic record
Abstract
This study explores the potential for co-locating floating photovoltaics (FPVs) with existing hydropower plants (HPPs) in Ecuador. Ecuador’s heavy reliance on hydropower for electricity generation, combined with recent blackouts caused by prolonged dry seasons, underscores the importance of diversifying energy sources. The integration of FPVs with HPPs offers a promising opportunity to enhance energy security by reducing dependency on a single energy source and improving economic, electrical, and environmental outcomes. In this paper, we assess all HPPs in Ecuador and quantify the potential performance of FPV systems when installed at their sites. Our results show that FPV systems can not only contribute additional electricity to the grid but also improve HPP performance by reducing water evaporation from reservoirs and maintaining generation capacity during dry seasons, when solar irradiation is typically higher. To model the energy production, yield, and performance of the FPV systems, we applied RINA’s methodology to estimate representative weather conditions for each site and simulate FPV performance, accounting for system design loss factors. Additionally, we calculated the water savings resulting from FPV installation. Our findings reveal that, out of approximately 70 HPPs in Ecuador, 11 present favorable conditions for large-scale FPV deployment. Among these, Cumbayá HPP (40 MW) exhibited the most suitable conditions, supporting a maximum FPV capacity of 17 MWp. Marcel Laniado de Wind HPP (213 MW) and Mazar HPP (170 MW) were also identified as optimal candidates, each with potential FPV capacities equal to their installed HPP capacities. While this study primarily aims to provide scientific evidence on the potential of FPV-HPP co-location, the results and methodology can also guide Ecuadorian government authorities and investors in adopting FPV technology to strengthen the country’s energy infrastructure.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".