Date of Award

8-2018

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Industrial Engineering

Committee Member

Dr. Scott J. Mason, Committee Chair

Committee Member

Dr. Russell Bent

Committee Member

Dr. Sandra D. Ekşioğlu

Committee Member

Dr. Harsha Gangammanavar

Abstract

In recent years, global climate change has become a major factor in long-term electrical infrastructure planning in coastal areas. Over time, accelerated sea level rise and fiercer, more frequent storm surges caused by the changing climate have imposed increasing risks to the security and reliability of coastal electrical infrastructure systems. It is important to ensure that infrastructure system planning adapts to such risks to produce systems with strong resilience. This dissertation proposes a decision framework for long-term, resilient electrical infrastructure adaptation planning for a future with the uncertain sea level rise and storm surges in a changing climate. As uncertainty is unavoidable in real-world decision making, stochastic optimization plays an essential role in making robust decisions with respect to global climate change. The core of the proposed decision framework is a stochastic optimization model with the primary goal being to ensure operational feasibility once uncertain futures are revealed. The proposed stochastic model produces long-term climate adaptations that are subject to both the exogenous uncertainty of climate change as well as the endogenous physical restrictions of electrical infrastructure. Complex, state-of-the-art simulation models under climate change are utilized to represent exogenous uncertainty in the decision-making process. In practice, deterministic methods such as scenario-based analyses and/or geometric-information-system-based heuristics are widely used for real-world adaptation planning. Numerical experiments and sensitivity analyses are conducted to compare the proposed framework with various deterministic methods. Our experimental results demonstrate that resilient, long-term adaptations can be obtained using the proposed stochastic optimization model. In further developing the decision framework, we address a class of stochastic optimization models where operational feasibility is ensured for only a percentage of all possible uncertainty realizations through joint chance-constraints. It is important to identify the significant scalability limitations often associated with commercial optimization tools for solving this class of challenging stochastic optimization problems. We propose a novel configuration generation algorithm which leverages metaheuristics to find high-quality solutions quickly and generic relaxations to provide solution quality guarantees. A key advantage of the proposed method over previous work is that the joint chance-constrained stochastic optimization problem can contain multivariate distributions, discrete variables, and nonconvex constraints. The effectiveness of the proposed algorithm is demonstrated on two applications, including the climate adaptation problem, where it significantly outperforms commercial optimization tools. Furthermore, the need to address the feasibility of a realistic electrical infrastructure system under impacts is recognized for the proposed decision framework. This requires dedicated attention to addressing nonlinear, nonconvex optimization problem feasibility, which can be a challenging problem that requires an expansive exploitation of the solution space. We propose a global algorithm for the feasibility problem's counterpart: proving problem infeasibility. The proposed algorithm adaptively discretizes variable domains to tighten the relaxed problem for proving infeasibility. The convergence of the algorithm is demonstrated as the algorithm either finds a feasible solution or terminates with the problem being proven infeasible. The efficiency of this algorithm is demonstrated through experiments comparing two state-of-the-art global solvers, as well as a recently proposed global algorithm, to our proposed method.

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