Quantifying the Resilience of an Urban Traffic-Electric Power Coupled System

Transportation system resilience has been the subject of several recent studies. To assess the resilience of a transportation network, however, it is essential to model its interactions with and reliance on other lifelines. In this work, a bi-level, mixed-integer, stochastic program is presented for quantifying the resilience of a coupled traffic-power network under a host of potential natural or anthropogenic hazard-impact scenarios. A two-layer network representation is employed that includes details of both systems. Interdependencies between the urban traffic and electric power distribution systems are captured through linking variables and logical constraints. The modeling approach was applied on a case study developed on a portion of the signalized traffic-power distribution system in southern Minneapolis. The results of the case study show the importance of explicitly considering interdependencies between critical infrastructures in transportation resilience estimation. The results also provide insights on lifeline performance from an alternative power perspective.

Community- and population-level effects of chytridiomycosis in a Neotropical amphibian community

As the number of fungal pathogen outbreaks become more frequent worldwide across taxa, so have the number of species extirpations and communities persisting with the pathogen. This phenomenon raises questions, such as: “what leads to host extinction during an outbreak?” and “how are hosts persisting once the pathogen establishes?.” But the data on host populations and communities across life stages before and after pathogen arrival rarely exist to answer these questions. Over the past three to four decades, the amphibian-killing fungus Batrachochytrim dendrobatidis (Bd) spread in a wave-like manner across Central America, leading to rapid species extirpations and population declines. I collected data on tadpole and adult amphibians in El Copé, Panama before, during, and after the Bd outbreak to answer these questions. I used Bayesian statistical approaches to account for imperfect host and pathogen detection of marked and unmarked individuals. In the tadpole community, within 11 months of Bds arrival, density and occupancy rapidly declined. Species losses were phylogenetically correlated, with glass frogs disappearing first, and tree frogs and poison-dart frogs remaining. I found that tadpole communities resembled one another more strongly after the outbreak than they did before Bd invasion. I found no tadpoles within 22 months of the outbreak and limited signs of recovery within 10 years. In contrast, at the same site, for a population of male glass frogs, Espadarana prosopleon, I found that 10 years post-outbreak, the population was consistently half its historic abundance, and that the lack of recruits to the population explained why the population had not rebounded, rather than high pathogen-induced mortality. And finally, examining the entire amphibian community, I found high pathogen prevalence, low infection intensities, and high survival rates of uninfected and infected hosts. Bd transmission risk, i.e., the probability a susceptible host becomes infected, did not relate to host density, pathogen prevalence, or infection intensity– Bd transmission risk was uniform across the study area. My results are especially relevant to conservation biologists aiming to predict the future impacts of Bd outbreaks, those trying to manage persisting populations, and those interested in reintroducing species back into wild amphibian communities.

Multi-level, Multi-stage and Stochastic Optimization Models for Energy Conservation in Buildings for Federal, State and Local Agencies

Energy Conservation Measure (ECM) project selection is made difficult given real-world constraints, limited resources to implement savings retrofits, various suppliers in the market and project financing alternatives. Many of these energy efficient retrofit projects should be viewed as a series of investments with annual returns for these traditionally risk-averse agencies. Given a list of ECMs available, federal, state and local agencies must determine how to implement projects at lowest costs. The most common methods of implementation planning are suboptimal relative to cost. Federal, state and local agencies can obtain greater returns on their energy conservation investment over traditional methods, regardless of the implementing organization. This dissertation outlines several approaches to improve the traditional energy conservations models. Any public buildings in regions with similar energy conservation goals in the United States or internationally can also benefit greatly from this research. Additionally, many private owners of buildings are under mandates to conserve energy e.g., Local Law 85 of the New York City Energy Conservation Code requires any building, public or private, to meet the most current energy code for any alteration or renovation. Thus, both public and private stakeholders can benefit from this research. The research in this dissertation advances and presents models that decision-makers can use to optimize the selection of ECM projects with respect to the total cost of implementation. A practical application of a two-level mathematical program with equilibrium constraints (MPEC) improves the current best practice for agencies concerned with making the most cost-effective selection leveraging energy services companies or utilities. The two-level model maximizes savings to the agency and profit to the energy services companies (Chapter 2). An additional model presented leverages a single congressional appropriation to implement ECM projects (Chapter 3). Returns from implemented ECM projects are used to fund additional ECM projects. In these cases, fluctuations in energy costs and uncertainty in the estimated savings severely influence ECM project selection and the amount of the appropriation requested. A risk aversion method proposed imposes a minimum on the number of “of projects completed in each stage. A comparative method using Conditional Value at Risk is analyzed. Time consistency was addressed in this chapter. This work demonstrates how a risk-based, stochastic, multi-stage model with binary decision variables at each stage provides a much more accurate estimate for planning than the agency’s traditional approach and deterministic models. Finally, in Chapter 4, a rolling-horizon model allows for subadditivity and superadditivity of the energy savings to simulate interactive effects between ECM projects. The approach makes use of inequalities (McCormick, 1976) to re-express constraints that involve the product of binary variables with an exact linearization (related to the convex hull of those constraints). This model additionally shows the benefits of learning between stages while remaining consistent with the single congressional appropriations framework.