Multi-scale solar-to-hydrogen system design: An open-source modeling framework
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Hydrogen produced from renewable energy holds significant potential in providing sustainable solutions to achieve Net-Positive goals. However, one technical challenge hindering its widespread adoption is the absence of open-source precise modeling tools for sizing and simulating integrated system components under real-world conditions. In this work, we developed an adaptable, user-friendly and open-source Python® model that simulates grid-connected battery-assisted photovoltaic-electrolyzer systems for green hydrogen production and conversion into high-value chemicals and fuels. The code is publicly available on GitHub, enabling users to predict solar hydrogen system performance across various sizes and locations. The model was applied to three locations with distinct climatic patterns – Sines (Portugal), Edmonton (Canada), and Crystal Brook (Australia) – using commercial photovoltaic and electrolyzer systems, and empirical data from different meteorological databases. Sines emerged as the most productive site, with an annual photovoltaic energy yield 39 % higher than Edmonton and 9 % higher than Crystal Brook. When considering an electrolyzer load with 0.5 W EC /W p PV capacity solely powered by the photovoltaic park, the solar-to-hydrogen system in Sines can reach an annual green hydrogen production of 27 g/W p PV and export 283 Wh/W p PV of surplus electricity to the grid. Continuous 24/7 electrolyzer operation increased the annual hydrogen output to 33 g/W p PV , with a reduced Levelized Cost of Hydrogen of €6.42/kg H2 . Overall, this work aims to advance green hydrogen production scale-up, fostering a more sustainable global economy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it