Performance of Off-grid Floating Photovoltaic-Battery System Powering an Anion Exchange Membrane Electrolyser for Green Hydrogen Production
Bibliographic record
Abstract
This study investigates the integration of floating photovoltaic (FPV) systems with an anion exchange membrane (AEM) electrolyser for green hydrogen production. The experimental setup involves a 5 kW FPV system that powers an AEM electrolyser under outdoor environmental conditions. The results show that the system achieves up to 86% energy efficiency and hydrogen purity exceeding 99%, demonstrating both operational stability and high performance being driven by renewable energy. FPV technology, when integrated with AEM electrolyser, offers several key advantages: improved cooling efficiency, enhanced land use efficiency, reduced resource consumption compared to traditional ground-mounted solar systems, and alternative excess energy storage for PV systems. The ability of FPV systems to mitigate land use conflicts makes them particularly suitable for regions with limited available land for traditional solar energy production. Furthermore, the integration addresses the intermittency issues associated with PV, as the FPV systems are equipped with a lithium-ion battery bank that supplies the electrolyser during low irradiation conditions. While the results highlight the potential of FPV-AEM systems, several challenges remain, including the high initial capital costs of FPV installations and their reliance on favorable weather conditions for optimal energy production. To address these limitations, further research is needed into cost optimization strategies, scalable designs, and effective energy storage solutions that can ensure system operation during periods of low solar irradiance. Additionally, future work should focus on improving the durability of FPV systems in harsh weather conditions, as well as exploring advanced materials for floatation, anchoring, and system longevity. Real-time monitoring systems could also enhance operational reliability and predictive maintenance. This study lays the foundation for FPV-AEM systems to play a significant role in the global transition to sustainable energy through green hydrogen production.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".