Renewable energy system demonstration at CHARS: modelling analysis report
Bibliographic record
Abstract
The majority of the energy demands in the Canadian Arctic are met with fossil fuels such as heating oil and diesel electric generation. As a result of climate change impacts, there is strong desire to explore and adopt renewable electricity generation technologies. There is also a desire to increase resilience of energy infrastructure, not just in the Arctic, but globally. The Arctic presents unique challenges in deploying renewable generation technologies due to the unique environmental conditions, logistics, and policies. This project is focused on the deployment of a small wind turbine and battery energy storage system for an off- grid energy system primarily to meet the lighting and vehicle block heating loads of a large storage shelter located at the Canadian High Arctic Research Station in Cambridge Bay, Nunavut. Polar Knowledge Canada operates and manages the facility, and is collaborating with National Research Council Canada on the project. The energy system will be instrumented and monitored to evaluate the performance of the turbine under Arctic operating conditions. This is the first report issued for the project, which describes the simulation and analysis of the system undertaken by NRC to support equipment selection and system design. Wind is a renewable energy resource which is typically available throughout the year and likely to be a more consistent source to meet energy demands in the Arctic, whereas solar energy has significant seasonal variation at high latitudes. Several wind demonstration projects have been undertaken in Nunavut since the 1990s, but these have been short-lived due to high maintenance costs and equipment failure under harsh Arctic conditions. Nonetheless, the technology has continued to advance over the past few decades and it is of interest to determine if the latest generation of wind turbine technologies can reliably utilize available wind resources. The micro-grid system was modelled using the transient energy system simulation tool TRNSYS. The performance of a 6 kW commercially-available wind turbine was simulated using manufacturer-reported performance data and climate data for Cambridge Bay provided by Environment and Climate Change Canada. A transient model of a 22.8 kWh battery system was also implemented into the model, as well as an empirical model of a diesel generation system and numerical models of the equipment shelters. The simulation analysis estimated that the renewable energy system will reduce annual greenhouse gas emissions by 21%. An additional simulation showed that doubling the specified battery capacity increased annual emissions reductions by 31%. The potential benefit of including on-site solar generation was also analyzed by adding a 2.32 kW solar PV array to the base system model, and it was shown to achieve annual emissions reductions by 46%. This PV study highlighted that despite the significant seasonal variation and demand- generation mismatch associated with solar in the Arctic, there is still a potential for significant benefit from solar PV dispatched in Arctic climates. Finally, the sensitivity of the micro-grid system performance to demand estimates was analyzed by doubling and quadrupling annual energy demand for vehicle block heating. These loading scenarios were found to reduce annual emissions savings to 13% and 7%, respectively. These results highlighted the significant sensitivity of system performance to site demand characteristics.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".