Volume 63

A multi-objective evolutionary approach for integrating renewable energy in remote mines: A case study in Ellesmere Island (Nunavut, Canada) Daksh Khanna, Helen Li, Wania Khalid, Minghan Xu

Abstract

This paper develops a novel multi-objective optimization framework, capable of predicting the optimization of renewable energy integration from available sources in northern and remote regions, while considering energy supply, cost, greenhouse gas emissions (GHG), and social impacts. Wind turbines, photovoltaic (PV) panels, small modular reactors (SMRs), and geothermal systems, along with lithium-ion batteries, are modeled separately. Multi-objective evolutionary algorithms (MOEAs) are employed to systematically evaluate each technology from energy and cost to environmental and social impacts. Applied to Ellesmere Island, the optimal renewable deployment halves cost ($500𝑀 in lifetime savings), reduces carbon emissions by over 90%, and eases social disagreement from 53% to 36% compared to diesel grids. The solution is viable, provided a payback period of less than 6 years to recoup the higher initial capital costs.

Keywords Advanced energy systems, multi-objective optimization, evolutionary algorithms, renewable energy, sustainable mining, Northern Canada