Inexact Chance-Constrained Mixed-Integer Regional Energy Model

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. System costs are minimized in this model. Results obtained from this model can help identify optimal patterns of renewable energy expansions in the Yukon. This model can encompass a temporal range between 2011-2035 and report results within that period.

Chen, J. P., Huang, G., Baetz, B. W., Lin, Q. G., Dong, C., & Cai, Y. P. (2018). Integrated inexact energy systems planning under climate change: A case study of Yukon Territory, Canada. Applied energy, 229, 493-504. https://doi.org/10.1016/j.apenergy.2018.06.140

governments, political and economic decision makers

costs (primary energy supply, power generation, capacity expansions, controlling contamination), electricity demand, energy resources availability, environmental constraints, capacity expansion options

energy supply during planning periods, electricity productions from different power generation technologies during planning periods