Browsing by Subject "Infrastructure networks"
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Item Methods for risk and resilience evaluation in interdependent infrastructure networks(2020-08-17) Balakrishnan, Srijith; Zhang, Zhanmin, 1962-; Machemehl, Randy; Boyles, Stephen; Gao, LuUrban infrastructure plays a key role in the structure and dynamics of every city. Besides ensuring the sustainability of communities and businesses, high-quality infrastructure services are crucial for generating jobs and attracting capital investments. Modern infrastructure systems are highly interconnected to enhance efficiency and safety of operations; however, the interconnections increase the risks of cascading failures during extreme events, such as natural disasters, acts of terrorism, and pandemics. Not only are the normal operations interrupted during such events, but prolonged operational disruptions in infrastructure services also have debilitating effects on emergency response and economic recovery in affected regions. With the emergence of new threats and intensifying climate change, the resilience of infrastructure systems has become a necessity rather than a choice for our cities. As with any resource allocation problem, potential resilience investments require identifying priorities and evaluating project alternatives. Appropriate resilience indicators can be used to rank and prioritize infrastructure components and systems as well as to evaluate the efficacy of resilience interventions. The dissertation proposes five indicator-based methodological frameworks to assist decision-makers in analyzing the intrinsic risks and resilience in large-scale interdependent infrastructure networks. For generic interdependent networks, an agent-based simulation approach is adopted. In this approach, the interdependent network is modeled as a weighted bi-directed network where nodes represent infrastructure components and links denote the interconnections. For evaluating the risks of cascading failures and the network's resilience, a hybrid risk measure based on the well-known Inoperability Input-Output Model (IIM) using expert judgments is developed. In the process, to handle the issue of epistemic uncertainty associated with subjective infrastructure dependency data, a method based on possibility theory is also proposed. Later, the hybrid risk measure is extended to develop two resilience indexes for quantifying the criticality and susceptibility of infrastructure components and ranking algorithms are presented. In addition, the hybrid risk measure is combined with socio-economic characteristics obtained from census data to develop a priority index to quantify the risks of cascading failures in various urban communities. With regard to infrastructure-specific networks, the dissertation developed infrastructure ranking and prioritization methods for two distinct transportation systems, specifically road networks, and marine port systems, based on empirical disaster data. For characterizing the resilience of road networks, the dissertation proposed three indicators based on the concepts of resilience triangle and extreme travel time observations. The dissertation combined time series decomposition techniques with anomaly detection algorithms to segregate disaster effects from normal traffic patterns. For characterizing the risks of natural hazards to port systems, the dissertation employed disaster impact data along with international trade data and identified the ports with the highest risks.Item Optimization approaches for energy infrastructure network design(2021-05-08) Geetha Jayadev, Gopika; Leibowicz, Benjamin D.; Bard, Jonathan F.; Kutanoglu, Erhan; Rai, VarunThis dissertation focuses on applying operations research techniques to problems arising in the energy sector. The work presented attempts to push the existing frontiers in energy optimization by adding to an existing open-source framework in electricity infrastructure optimization and applying bilevel programming to relatively unexplored applications in natural gas market modeling. This dissertation consists of five chapters. Chapter 1 provides an introduction encompassing a brief description and motivation for the dissertation’s work. The future development of the U.S. electricity sector will be shaped by technological, economic, and policy drivers whose trajectories are highly uncertain. Chapter 2 describes an optimization model for the integrated generation and transmission system in the continental U.S., which is used to explore electricity infrastructure pathways from the present through 2050. By comparing and contrasting results from numerous scenarios and sensitivity settings, we ultimately affirm five key policy-relevant insights. (1) U.S. electricity can be substantially decarbonized at a modest cost, but complete decarbonization is very costly. (2) Significant expansion of solar PV and wind to combine for at least 40% of the generation mix by 2050 is fairly certain. However, solar PV and battery storage are more affected by economic and policy assumptions than wind. (3) Investments in long-distance transmission are minimal, while investments in battery storage are much more significant under a wide range of assumptions. (4) Optimal solutions include large investments in natural gas capacity, but gas capacity utilization rates decline steadily and significantly. (5) Cost structures shift away from operating expenditures and toward capital expenditures, especially under climate policy. We conclude our article by discussing the policy implications of these findings. Chapter 3 presents a bilevel programming model to aid decision-making in natural gas markets where multiple autonomous agents interact strategically, possibly with conflicting interests. In the proposed model, a liquefied natural gas (LNG) operator is the leader, and a natural gas (NG) producer is the follower. The LNG operator attempts to optimally locate LNG export terminals, purchase gas from the NG producer, and export it as LNG. The NG producer aims to optimize production, pipeline investments, and sales to domestic consumers and the LNG operator. To solve the bilevel problem, we first reformulate it as a single-level problem by exploiting the lower-level problem’s convexity. Then, we use disjunctive reformulations of complementarity constraints and piecewise linear approximations of objective function terms to convert the problem into a convex quadratic mixed-integer program (QMIP). Computational experiments confirm that the QMIP is tractable and can be solved efficiently. We apply our bilevel framework to a case study of the Gulf-Southwest region of the United States and evaluate several decision-making scenarios. The scenario results emphasize the importance of using the bilevel methodology to anticipate the effects of new LNG export facilities on NG prices across the domestic gas network. Adding these large gas-consuming facilities at specific locations puts upward pressure on domestic gas prices, although this is somewhat mitigated by the NG producer’s optimal production response to the increased demand. Chapter 4 presents a risk-based integer bilevel programming model that considers two players in a leader-follower setting, where each tries to maximize their individual profits. The leader or manufacturer makes decisions pertaining to facility location and the amount of raw material to buy from the supplier. The supplier or follower decides how much raw material to produce. Both players face demand uncertainty in their respective markets while making investment, production, and sales decisions. We develop models for optimal decision-making under risk-neutral and risk-averse objectives and explore the effects of different risk attitudes on the problem and its optimal solution. These problems are hard to solve as the number of scenarios increase, and solving them generally involves developing customized algorithms. To find feasible solutions to the resulting stochastic bilevel problem, an algorithm is developed that iteratively solves a restricted version of the problem to obtain feasible solutions to the original problem. Extensive computational experiments are performed to evaluate algorithmic tractability and solution quality. The proposed algorithm is able to find high-quality feasible solutions to the bilevel problem in a very reasonable amount of time, and the attained solutions are found to be close to the optimal solution. The methodology is demonstrated with an example that addresses strategic issues in the natural gas market. The model considers two players, the liquefied natural gas (LNG) operator or leader and the natural gas (NG) producer or follower acting in a leader-follower setting. The LNG operator makes decisions pertaining to the locations of LNG facilities and the amount of gas to buy from the producer. The follower decides how much gas to produce. Both players face demand uncertainty in their respective markets and make investments, production, and sales decisions. The case study explores the Gulf-Southwest region of the United States and demonstrates the impact of the risk-averse decision-making approach on investment and operational decisions. Chapter 5 concludes this dissertation by summarizing the most important findings from each study and outlining valuable directions for future research.