Save water and energy: A techno‐economic analysis of a floating solar photovoltaic system to power a water integration project in the Brazilian semiarid
Bibliographic record
Abstract
Summary This paper proposes the use of a floating solar photovoltaic (FSPV) power plant as an alternative renewable energy resource for the San Francisco River Integration Project (SFIP), which aims to deliver water to 12 million people in the Brazilian semiarid. The SFIP requires considerable amounts of energy in a region that has had increasing electricity costs and consumption of water in the past decade. By simulating an FSPV power plant at the System Advisor Model (SAM) using techniques and parameters of real FSPV projects, the results demonstrated the techno‐economic feasibility of this technology linked to the SFIP. The economic outcomes are positive net present value (NPV), $2.8 million, and a payback varying from 10.5 to 11.7 years. The levelized cost of electricity (LCOE) of $32.17/MWh is smaller than the current energy rates paid by the SFIP administrator, and the water costs to final consumers could be reduced by 40%. In addition, the FSPV's capacity factor was 21.1%, and the system could minimize water evaporation from one of the SFIP's reservoirs by 16.7%. The system can also create revenues for the San Francisco and Parnaiba Valleys Development Company (CODEVASF) by trading the excess of electricity with the grid. This paper also analyses the FSPV's environmental impacts and its relevance under the water–energy nexus in the Brazilian Northeast. The FSPV could minimize the SFIP's operational costs, avoid environmental impacts, and improve the efficiency of water and energy management. Such components are crucial when analyzing the water–energy nexus in such a region, marked by strong competition for water access and long periods of drought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".