Integrated energy-environmental modeling and climate change adaptation planning under uncertainty

In this dissertation research, a set of methodologies was developed for integrated energy-environmental management and climate change adaptation planning under uncertainty, including (a) fuzzy-random interval programming (FRIP) and interval-parameter superiority-inferiority (IPSI-REM) programming methods for supporting regional energy management systems (EMSs) planning, (b) inexact community-scale energy model (ICS-EM) and interval-parameter superiority-inferiority-based interval two-stage (ISITSP-CREM) programming model for supporting the planning of renewable EMSs, (c) interval-valued fuzzy robust programming (I-VFRP) and mixed interval parameter fuzzy-stochastic robust programming (MIFSRP) approaches for petroleum solid waste management, (d) an optimization-model-based interactive decision-support system (UREM-IDSS) for supporting energy systems planning in the region of Waterloo, and (e) a large-scale integrated modeling system (IMS) for supporting the adaptation planning of EMSs to climate change.;The main contribution of this research is the development of a number of innovative methods for supporting robust planning of multi-scale energy-environmental management systems and generating subsequent adaptation strategies toward climate change. The developed FRIP improved upon the previous inexact optimization methods by allowing the lower and upper bounds of interval parameters in both objective functions and constraints to be expressed as fuzzy random variables. The developed IPSI-REM model enhanced the existing optimization methods through addressing dynamic relations between uncertain parameters. ICS-EM could provide insights into the tradeoffs between energy-shortage risks and economic costs in a renewable EMS. The developed ISITSP-CREM model enhanced the capabilities of optimization modeling in dealing with multiple formats of uncertainties and their interactions. The developed I-VFRP model introduced the concept of interval-valued fuzzy sets to reflect gradient levels of subjective judgments from many solid waste managers. The developed MIFSRP could enhance the robustness of the optimization process for waste management through comprehensively considering varied subjective opinions of many decision makers and stakeholders. The UREM-IDSS is superior to the conventional decision support systems (DSSs) in terms of uncertainty analysis and systems characterization, and could help analyze and visualize various complex energy-related issues. The developed IMS proposed an innovative framework for seamless integration of climate-change impact analysis and integration, and adaptation planning from a systematic point of view, and for explicit reflection of multiple levels and multiple dimensions of uncertainties.