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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".