Renewable energy system demonstration at CHARS: modelling analysis report
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».