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

Abstract

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.

A pseudo-optimal inexact stochastic interval T2 fuzzy sets approach for energy and environmental systems planning under uncertainty: A case study for Xiamen City of China

“…In past decades, a large number of optimal research works made proposals to deal with such uncertainties and resulted in a compromise between environmental and economic targets [16] required in the planning of energy systems [17,18,19,20,21,22,23,24,25]. In these works, optimization programming played a significant role by providing desired decision alternatives under such complex energy systems [26,27,28,29,30,31,32,12,13,14,10,33,34,24].…”

Chance-constrained two-stage fractional optimization for planning regional energy systems in British Columbia, Canada

“…However, there are many challenges in effective resource management due to issues of energy demand, supply, and allocation among various users [3]. In addition, a variety of uncertainties are associated with these issues and the related parameters such as future electricity demands, resource availabilities, energy allocation targets, facility capacity-expansion options, and economic costs, as well as their interrelationships [4].…”

An interval full-infinite programming approach for energy systems planning under multiple uncertainties

“…Decision makers are thus in a quandary on how to balance increasing energy demands with the population growth and the economic development, mandated requirements for GHG emissions reduction, and less fossil fuel consumption. In the past decades, there have been a number of studies on energy systems management and planning to reach the goal of sustainable economic development and effective environmental management [41,12,43,21,33,19,26,1,5,2,6,13,22,28]. However, energy system is subject to a variety of social, economic, political, environmental and technical factors, which are highly uncertain and interrelated.…”

“…Lin and Huang [28] developed an interval-fuzzy stochastic optimization for regional energy systems planning and greenhouse-gas emission management under uncertainty. The proposed method then applied to a case study for the Province of Ontario, Canada.…”