Multi-objective optimization of nanogrids for remote telecom base stations in Canada

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 ${\mathrm{CO}}_2$ under $LPSP<0.0001\text{\%}$ 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 ${\mathrm{CO}}_2$ by 18.59% and LCC by 5.26% versus baseline, with the most sustainable setup using 856 L/year (2.93 t ${\mathrm{CO}}_2$). Sensitivity analysis showed snow-aware designs avoid up to 40.9% higher LCC and 139.7% more ${\mathrm{CO}}_2$ 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.