Linking the Power and Transport Sectors—Part 2: Modelling a Sector Coupling Scenario for Germany
Bibliographic record
Abstract
“Linking the power and transport sectors—Part 1” describes the general principle of “sector coupling” (SC), develops a working definition intended of the concept to be of utility to the international scientific community, contains a literature review that provides an overview of relevant scientific papers on this topic and conducts a rudimentary analysis of the linking of the power and transport sectors on a worldwide, EU and German level. The aim of this follow-on paper is to outline an approach to the modelling of SC. Therefore, a study of Germany as a case study was conducted. This study assumes a high share of renewable energy sources (RES) contributing to the grid and significant proportion of fuel cell vehicles (FCVs) in the year 2050, along with a dedicated hydrogen pipeline grid to meet hydrogen demand. To construct a model of this nature, the model environment “METIS” (models for energy transformation and integration systems) we developed will be described in more detail in this paper. Within this framework, a detailed model of the power and transport sector in Germany will be presented in this paper and the rationale behind its assumptions described. Furthermore, an intensive result analysis for the power surplus, utilization of electrolysis, hydrogen pipeline and economic considerations has been conducted to show the potential outcomes of modelling SC. It is hoped that this will serve as a basis for researchers to apply this framework in future to models and analysis with an international focus.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".