Demographic and Geographic Region Definition in Energy System Modelling. A case study of Canada’s path to net zero greenhouse gas emissions by 2050 and the role of hydrogen
Bibliographic record
Abstract
The urgency of reducing greenhouse gas emissions is greater now than ever, with the impacts of climate change becoming more apparent each year. Due to this, governments are setting ambitious targets such as reaching net zero GHG emissions by 2050, as announced by Canada in November of 2020. Within this context, the energy transition continues to gain momentum, as energy systems currently contribute to a large portion of these emissions. In order to support the energy transition, researchers, planners and policy makers alike are considering alternative solutions, such hydrogen, and are becoming increasingly reliant on energy system models in order to determine how the energy systems of the future should evolve. This thesis adapts an optimization based energy system model called EnergyScope in order to model potential pathways for the production and utilization of hydrogen within an energy system. Further, different methods of the definition of regions within energy system models are considered. The EnergyScope model is adapted from a model based on regions defined by political boundaries, to a model based on regions defined by geographic and demographic characteristics. A method for defining these regions and integrating them into the model is developed. These models are then used to assess how Canada could meet its goal of reaching net zero GHG emissions by 2050 within the energy sector, and what role hydrogen could play in this future energy system. The results highlight the importance of electrification in achieving a net zero energy system, indicating that the future system will be mainly based on renewable electricity generated by PV, wind and solar technologies. This will be used to fulfill the energy demand of the electricity sector, as well as the heating and transportation sectors, which will be mostly electrified. The results also indicate that hydrogen has a potential role to play in energy storage and heating in this net zero energy system, storing electricity when it is produced in excess and being used directly for heating (in place of electricity) when electricity generation is lower. The model indicates that hydrogen will likely be produced by emission free technologies such as natural gas pyrolysis and electrolysis, although uncertainty remains as these technologies are still maturing. Further, it is shown that the definition of regions used in energy system models based on demographic and geographic characteristics, rather than political boundaries, can provide additional insights particularly regarding the distribution of the potential of variable renewable technologies such as wind and solar, and how this corresponds to the distribution of demands and energy resource exchange networks.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".