Simulation of a decentralized floating offshore wind hydrogen production system with hydro-pneumatic energy storage and subsea isobaric hydrogen storage
Bibliographic record
Abstract
Abstract Hydrogen production using offshore wind power is a promising solution for producing clean fuels in remote areas. However, the intermittency of offshore wind power poses significant challenges to Proton exchange membrane (PEM) water electrolysis systems. Besides, there is generally an imbalance between hydrogen production and hydrogen demand. In this study, hydro-pneumatic electricity energy storage and subsea isobaric hydrogen storage are integrated into the decentralized offshore wind hydrogen production system. The hydro-pneumatic energy storage unit (HPES) is used for mitigating the intermittency and fluctuation of wind power, thereby prolonging the lifespan of PEM electrolyzers. Subsea isobaric hydrogen storage unit is used for replacing conventional isochoric hydrogen storage. In this study, simulation models of a decentralized offshore wind hydrogen production system with various configurations are established with the software Simcenter Amesim 2021.1. The results show that an 83% reduction in the on/off operation can be achieved with the help of hydro-pneumatic electricity energy storage. The isobaric and isothermal storage of compressed hydrogen can be achieved with subsea isobaric hydrogen storage, saving compression energy and facilitating thermal management. In terms of the investigated decentralized offshore wind hydrogen production system, the amount of produced hydrogen is increased by less than 1% by integrating hydro-pneumatic energy storage and subsea isobaric hydrogen storage.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".