This thesis explores the potential for renewable wind and solar energy to meet the electrical demand of the Canadian High Arctic Research Station, the Government of Canada's new flagship Arctic research facility located in Cambridge Bay, Nunavut. Time-series simulation models based on measured weather data and simulated energy demand were constructed in TRNSYS. The models were then coupled with GenOpt to optimize system configuration with respect to net present cost using the particle swarm optimization technique. The results suggest that renewable energy can meet a portion of the demand more cost-effectively than diesel generation alone; however, a major challenge is the ability of the local grid to absorb surplus renewable power. Increasing the renewable penetration rate at the station beyond about 65% is not economically feasible with the generating and storage technologies considered in this thesis. Policy considerations regarding implementation of renewable energy in Nunavut were also discussed.