Green hydrogen viability in the transition to a fully-renewable energy grid
Bibliographic record
Abstract
Abstract With the transition to a fully renewable energy grid arises the need for a green source of stability and baseload support, which classical renewable generation such as wind and solar cannot offer due to their uncertain and highly-variable generation. In this paper, we study whether green hydrogen can close this gap as a source of supplemental generation and storage. We design a two-stage mixed-integer stochastic optimization model that accounts for uncertainties in renewable generation. Our model considers the investment in renewable plants and hydrogen storage, as well as the operational decisions for running the hydrogen storage systems. For the data considered, we observe that a fully renewable network driven by green hydrogen has a greater potential to succeed when wind generation is high. In fact, the main investment priorities revealed by the model are in wind generation and in liquid hydrogen storage. This long-term storage is more valuable for taking full advantage of hydrogen than shorter-term intraday hydrogen gas storage. In addition, we note that the main driver for the potential and profitability of green hydrogen lies in the electricity demand and prices, as opposed to those for gas. Our model and the investment solutions proposed are robust with respect to changes in the investment costs. All in all, our results show that there is potential for green hydrogen as a source of baseload support in the transition to a fully renewable-powered energy grid.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".