Uncertainties And Risk Analysis For Energy And Environmental System Planning

Uncertainties in Energy and environmental system planning have been a critical concern in the past decades. Effective management for energy and environmental planning should be based on a variety of inexact programming models which could approach the real-world cases well. Consequently, the characterization and integration of the high uncertain factors and their interactions in the programming models become the most challenging problems. Moreover, the risk result from the uncertainties that exist in the studied systems should be analyzed and further quantified. To facilitate more robust planning models for energy and environmental systems, advanced methodologies that can address the uncertainties and quantify the corresponding risk are desired.In this dissertation research, the concept of random-boundary intervals whose lower and upper bounds are random variables has been developed to represent high uncertain components in the energy and environmental systems. Moreover, six advanced programming models have been developed for supporting energy and environmental systems planning according to different types of uncertainties in the specific studying system. These models and the associated application fields include: (a) an integer programming model with random-boundary intervals (IPRBI) with solution method named two-boundary approach (TBA) for municipal power system (MPS) planning, (b) a fuzzy integer programming with random-boundary intervals (FIRBI) for MPS planning, (c) an interval programming with random variables (1FRV) with solution method named left-hand chance-constrained method (LCCM) for regional air quality management, (d) a two-stage programming with fuzzy random variables (TPFRV) for water resources management, (e) a random-boundary interval chance-constrained programming (RICCP) method for municipal solid waste (MSW) management, (f) a dual inexact fuzzy chance-constrained programming (DIFCCP) for MSW management. In addition, different types and levels of system-failure risks due to the uncertain inputs of the developed model are analyzed and further quantified. For each system, efforts are made in uncertainty characterization and integration, model conceptualization and formulation, solution method-development, risk analysis and quantification, and policy or scenario analysis. In fact, papers of the IPRBI and DIFCCP have been published on SCI journals, the remains have been submitted and now are under review.Compared with the existing inexact optimization approaches, the developed models are more robust due to the advantages in reflecting high uncertain components presented as random-boundary interval, and fuzzy random variables. Consequently, the obtained solutions are useful for decision makers to gain insight regarding the tradeoffs among environmental, economic and social criteria. A series of feasible schemes can be generated based on the interval solutions obtained under different scenarios and risk levels. Decision makers can identify the desired scheme according to the practical situation, the risk types and levels they can accept, and their experiences. In other words, the obtained results are adjustable and flexible in real-world cases.