An inexact optimization model for energy-environment systems management in the mixed fuzzy, dual-interval and stochastic environment

Greenhouse gas (GHG)-emission mitigation has been a complex issue challenging decision makers in energy systems management. This study presents a fuzzy dual-interval multi-stage stochastic programming (FDMSP) approach for the planning of integrated energy-environment systems under multiple uncertainties. The approach is derived by incorporating the concepts of fuzzy programming, interval-parameter programming and dual-interval programming within a multi-stage stochastic optimization framework. With the FDMSP approach, issues of GHG-emission mitigation can be effectively reflected throughout the process of energy systems planning. The proposed method has advantages in integrating inherent system uncertainties, expressed not only as discrete intervals and dual intervals but also as possibility and probability distributions, into its solution procedure. Moreover, the method can also address the dynamics of system conditions within a multi-stage planning context. Through the application of the FDMSP to a hypothetical case of regional energy-environment system management, it indicated that reasonable solutions could be generated for both binary and continuous variables in deterministic, interval and dual-interval formats; and that interactions among multiple energy related activities could be effectively reflected. Generated decision alternatives from a FDMSP model could help decision makers identify desired strategies related to renewable/non-renewable energy production and allocation, GHG emission mitigation, as well as facility capacity expansion in a mixed multi-uncertain environment. Tradeoffs among system costs, energy utilizations and GHG emission control could be effectively addressed.