Regionalisation in high share renewable energy system modelling
Bibliographic record
Abstract
Governments are setting ambitious targets to tackle the issue global warming by switching to renewable energy sources and reducing CO2-emissions. For example, as announced in November 2020, Canada aims to achieve net zero GHG emissions by 2050. Large countries such as Canada cannot easily apply a global energy strategy, each region having different energy demands and potentials. Optimization-based energy models can be used to simulate and compare different energy transition pathways - one of them is based on the use and production of hydrogen. For this purpose, different methods of defining regions within energy system models are considered by considering (i) political boundaries and (ii) clustering geographical and demographic characteristics. We propose a new modeling strategy by comparing two region definition strategies, applied to the case of Canada, assessing the competing role of electricity and Hydrogen as energy vectors. Our case study shows the electrification of the energy system being essential to achieve net-zero emissions across all sectors to satisfy the mobility, heating and electrical demands, while the role hydrogen in the power, industrial and transport sectors for valorizing excess electricity and decarbonizing them is identified.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".