Multi-objective optimization of nanogrids for remote telecom base stations in Canada
Bibliographic record
Abstract
The telecommunications sector targets net-zero emissions by 2050, yet many remote Canadian base stations rely on diesel generators, incurring high costs and emissions. Most hybrid renewable energy system (HRES) studies overlook snow accumulation, limiting relevance in northern climates. This work proposes a snow-aware hybrid nanogrid for a telecom base station in Dorval Lodge, Quebec, using bifacial PV modules, lithium iron phosphate (LFP) batteries, and a diesel generator. A preliminary HOMER Pro study showed 99% renewable penetration is technically possible but at high cost and without snow, bifacial, or aging effects. We developed a high-fidelity model including hourly snow coverage, seasonal albedo, battery aging, and diesel fuel emission behavior. A joint multi-objective optimization minimizing life cycle cost (LCC) and annual CO 2 under L P S P < 0 . 0001 % was solved using a Controlled Elitist NSGA-II algorithm. Three stages were tested: baseline, fixed controls, and monthly adaptive controls. The adaptive strategy achieved the largest gains, cutting CO 2 by 18.59% and LCC by 5.26% versus baseline, with the most sustainable setup using 856 L/year (2.93 t CO 2 ). Sensitivity analysis showed snow-aware designs avoid up to 40.9% higher LCC and 139.7% more CO 2 seen in snow-unaware cases. Integrating climate-specific snow modeling with adaptive controls enhances economic and environmental performance, offering a robust, transferable solution for remote telecom power in harsh climates.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".