Interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty—a case study for the Province of Ontario, Canada

Greenhouse gas (GHG) emission reduction is usually associated with energy systems management. Management of regional energy systems is a complex task due to the strong interactions among energy supply, demand and conversion activities, as well as those among energy, environmental and economic factors. These complexities may be further compounded due to the presence of uncertainties in a variety of processes and the related costs, impact factors and objectives. Therefore, the objective of this study is to develop a dynamic interval-fuzzy two-stage stochastic regional energy systems planning model (DIFT-REM) and analysis GHG-emission reduction policies within a general energy management systems framework. The developed model is then applied to the Province of Ontario to demonstrate its applicability in supporting regional energy systems management and GHG-emission reduction analysis under uncertainty. The results indicated that DIFT-REM could address not only interactions among multiple energy-related activities, but also uncertainties in multiple forms and dynamics within a multi-period, multi-facility, multi-scale and multi-uncertainty context. The results also suggested that, when GHG-emission-credit trading is available for Ontario, the task of GHG-emission reduction could be accomplished with a lower system cost.