Forecast-driven blockchain framework for multi-node solar–hydrogen hybrid energy systems
Bibliographic record
Abstract
The growing reliance on solar power underscores the need for long-duration storage to mitigate weather-driven variability. Solar–Green Hydrogen Hybrid Systems (SGHHS) offer a solution, yet single-node setups remain exposed to clustered low-irradiance events. This study proposes a forecast-driven, blockchain-enabled framework for hydrogen sharing across a five-node SGHHS network, where standardized PV–electrolyzer–storage–fuel cell units exchange hydrogen via blockchain-mediated transfers. A Resilience Index evaluates loss-of-load probability, unserved energy, and surplus utilization. Simulations with Typical Meteorological Year data show coordination removes 10.22 MWh/year of unserved energy, achieving a Resilience Index of 1.00 under extreme clustering. Even with trucking transfers (~7 h), deficit avoidance exceeds 96% in winter and reaches 100% in summer. Key contributions include integrating multi-node hydrogen sharing with forecast-driven blockchain automation, moving beyond conventional single-node studies. Economic validation using Value of Lost Load confirms cost neutrality at $3,900/MWh and net annual benefits above $60,000, establishing a scalable and secure blueprint for resilient hydrogen corridors.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".