Design and Implementation of an Electricity System Optimization Model for Remote Communities in Canada

Abstract

This study presents an energy system optimization model based on linear programming techniques to predict least-cost electricity generating systems for five remote communities in Quebec, Canada. The model integrates hourly electricity demand data, hourly wind speed data, and hourly solar power generation data, and considers relevant costs, to identify the optimal combination of generating technologies capable of meeting the communities' electricity demand throughout the year. To account for environmental considerations, the model was subject to two separate constraints. First, a carbon tax on carbon emissions from the system was incrementally increased. Second, carbon emissions were gradually constrained, ultimately reducing to zero allowed emissions. The results suggest that even in the absence of either aforementioned constraint, the least-cost system already incorporates wind power in conjunction with existing diesel generation, and a system with zero carbon emissions is less expensive still than a system fully reliant on diesel. Further, the results suggest that a carbon emissions constraint is a more impactful policy option to incent carbon emissions reductions than a carbon tax for the five communities studied, as the carbon tax increased system price while providing insignificant carbon emissions reductions.