Integrated inexact energy systems planning under climate change: A case study of Yukon Territory, Canada

Abstract This study developed an inexact optimization modelling approach for supporting regional energy systems decision-making and greenhouse gas emission mitigation under uncertainty. The developed model integrates multiple inexact optimization programming approaches, incorporating interval linear programming, mixed-integer programming, and chance-constrained programming in an optimization framework. Uncertainties expressed as interval values and probabilistic distributions can be effectively handled. This is the first attempt that applies an optimization-based modelling approach to Yukon Territory, Canada. Three scenarios and one business-as-usual scenario are evaluated. System costs are minimized in this model. Results obtained from this model can help identify optimal patterns of renewable energy expansions in the Yukon. The interval solutions obtained could help decision makers to identify desirable renewable energy polices and emission reductions.

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