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.iiiFor Nunavummiut To all my colleagues on the CHARS project, it was a privilege to work with you and I can't stress enough how much I learned from everyone.I hope our paths cross again.I would like to specifically thank my mentors over the years -Rein, Santino, Matthew, and Mark.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".