Initial perspective of hybrid energy storage in zero carbon energy systems of a remote community of northern Canada
Bibliographic record
Abstract
We review towards zero and zero carbon remote community energy systems using wind and solar electricity generation combined with battery and thermal energy storage systems. Systems are modelled in the context of the Xeni Gwet’in First Nation Community located in the Nemiah Valley in British Columbia, Canada. The community has a population of 200 residents, most of whom are connected to the local microgrid. The goal of this research was to (1) investigate how low-cost thermal energy storage impacts the levelized cost of energy of these systems, and (2) assess the sensitivity of unmet energy capacity of the system at varying storage costs. Electrical and thermal load profiles for the community were developed and determined to average a combined annual load of 2 GWh with a peak of 900 kW. A model of the proposed system was built in HOMER Pro® and sensitivity analysis of PV, wind, and hybridized energy storage costs was conducted. Results indicate that as wind and PV costs are projected to decrease, minimizing the levelized cost of energy of the energy system is less dependent on thermal energy storage cost.; this correlation decreases as unmet capacity increases. Moreover, the analysis suggests that these systems yield greater than 70% curtailment of solar and wind energy, regardless of energy storage costs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".