Investigating methods for electricity systems planning for uncertainty and socio-environmental sustainability with application to the electricity grid in Yukon, Canada
Bibliographic record
Abstract
Planning electricity systems is a growing challenge amidst pressures to deliver affordable power whilst simultaneously addressing the many facets of social, economic, and environmental sustainability, with uncertainty amplifying this already formidable task. This thesis explores methods which leverage unique energy modelling techniques to inform policy and investment decisions which robustly support affordability, reliability, and socio-environmental sustainability objectives amidst uncertainty. Using the electricity system in Yukon, Canada as a case study, I investigate the trade-offs and co-benefits of advancing various sustainability goals, aiming to identify effective strategies, key system vulnerabilities, and robust generation technologies. Results from this work indicate flexible and moderate strategies are more effectively at efficiently achieving target outcomes of reducing greenhouse emissions and improving social and ecological sustainability with minimal trade-offs to costs. Additionally, I find rising peak load and energy demand are among the key risks which threaten sustainability in Yukon, with demand side management and pumped hydro technologies robustly supporting decarbonization and affordability objectives. This thesis produces insights for Yukon but also delivers generalized decision-support frameworks to further socio-environmental sustainability of the electricity sector.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".