Simulation and Optimization of High-Penetration Wind and Solar Energy for the Canadian High Arctic Research Station
Bibliographic record
Abstract
This thesis explores the potential for renewable wind and solar energy to meet the electrical demand of the Canadian High Arctic Research Station, the Government of Canada's new flagship Arctic research facility located in Cambridge Bay, Nunavut. Time-series simulation models based on measured weather data and simulated energy demand were constructed in TRNSYS. The models were then coupled with GenOpt to optimize system configuration with respect to net present cost using the particle swarm optimization technique. The results suggest that renewable energy can meet a portion of the demand more costeffectively than diesel generation alone; however, a major challenge is the ability of the local grid to absorb surplus renewable power. Increasing the renewable penetration rate at the station beyond about 65% is not economically feasible with the generating and storage technologies considered in this thesis. Policy considerations regarding implementation of renewable energy in Nunavut were also discussed.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".