Synergy-based hydrogen pricing in hydrogen-integrated electric power system: Sensitivity analysis
Bibliographic record
Abstract
Hydrogen price significantly impacts its potential as a viable alternative in the sustainable energy transition. This study introduces a synergy-based Hydrogen Pricing Mechanism (HPM) within an integrated framework. The HPM leverages synergy between a Renewable-Penetrated Electric Power System (RP-EPS) and a Hydrogen Energy System (HES). Utilizing the Alternating Direction Method of Multipliers (ADMM), it facilitates data exchange, quantifying integration levels and simplifying the complexities. The study assesses the HPM's operational sensitivity across various scenarios of hydrogen generation, transportation, and storage. It also evaluates the benefits of synergy-based versus stand-alone HPMs. Findings indicate that the synergy-based HPM effectively integrates infrastructure and operational improvements from both EPS and HES, leading to optimized hydrogen pricing. • Hydrogen pricing based on integration of hydrogen and electric power system. • Lower hydrogen prices by aligning operational targets in integrated configuration. • Enhances market flexibility by transitioning from cost-plus pricing. • Quantified integration level using data exchange method enabled by ADMM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".