Increasing renewable energy utilization in the Arctic: Benefits of electric thermal storage in hybrid PV-wind-diesel microgrids
Bibliographic record
Abstract
Remote Canadian communities, predominantly indigenous, often rely on diesel fuel for electricity, which is costly and environmentally detrimental. Integrating renewable energy (RE) sources, as shown in a previous study from the same authors (Paulin-Bessette et al., 2023), can mitigate greenhouse gas (GHG) emissions, improve air quality, and reduce costs, although limitations due to RE curtailment were observed when using photovoltaic (PV) panels and wind turbines (WT) without storage. This paper proposes a novel microgrid configuration incorporating electric thermal storage (ETS) units to store excess RE production as heat, offering a cost-effective alternative to electrochemical batteries that reduces curtailment, supports grid stability, and lowers heating costs in Arctic communities with high thermal demand. The diesel microgrid model incorporating PV panels, wind turbines, and ETS units was developed in MATLAB Simulink, using experimental data from Hydro-Quebec to accurately simulate thermal storage behavior. Results show that ETS integration can increase the fuel savings by close to 50 % when compared to the results obtained in the initial study without storage. This approach supports decarbonization goals and promotes healthier living conditions in remote Arctic communities. • Most Canadian remote communities rely on fossil fuels to produce electricity. • Introducing renewable energy can reduce fuel consumption and GHG emissions. • Without storage, the penetration of variable renewable energy is limited. • In these microgrids, fossil fuel heating emits more GHG than electricity generation. • ETS can mitigate RE variability and reduce fossil fuel use for heating.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".