Exploring pathways for Norwegian green hydrogen A techno-economic evaluation of value chains for Power-to-Hydrogen from the Norwegian North Sea
Bibliographic record
Abstract
Renewable hydrogen can be transported using different hydrogen carriers and modes of transport. Each carrier and mode is associated with different drawbacks and benefits relating to – among other factors – storage density and losses. The forthcoming expansion of offshore wind is likely to be coupled with hydrogen production in some way and there are many different ways that this can be done. This master thesis investigates what value chains for Power-to-Hydrogen and Power-to-X that are most promising for the Norwegian offshore wind licensing area Sørlige Nordsjø II, assuming an investment in 2030. The model used to perform the necessary calculations is a proprietary business case scenario builder that was extended to include value chain steps downstream of the electrolyser up to delivery at an industrial cluster. The model is used to calculate the levelised cost of hydrogen for 17 different value chains, involving transport to two different geographical locations – Porsgrunn in Norway and Rotterdam in the Netherlands. The levelised cost of ammonia and methanol is also calculated for value chains based on these carriers, to evaluate the potential of direct utilisation of these chemicals. The results of the value chain comparison show that onshore and offshore pipeline transport of compressed hydrogen are the value chains with lowest levelised cost of hydrogen for Porsgrunn and Rotterdam, respectively. This holds for all uncertainties and sensitivities tested within the thesis. The impact on pipeline costs of hilly terrain is, however, not considered in the study. The investigation of potential for direct utilisation of ammonia and methanol shows that more studies are required to answer this question. Although the study shows clear results regarding the most promising value chains from a levelised cost of hydrogen-perspective, recognition is given to the fact that more aspects might affect which value chain is more promising, such as energy efficiency, demand for the products from nearby industries and investment flexibility.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".