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.

Assessing the influence of abiotic factors and leaf-level properties on the stability of growing-season canopy greenness in a deciduous forest

Maps depicting spatial pattern in the stability of summer greenness could advance understanding of how forest ecosystems will respond to global changes such as a longer growing season. Declining summer greenness, or “greendown”, is spectrally related to declining near-infrared reflectance and is observed in most remote sensing time series to begin shortly after peak greenness at the end of spring and extend until the beginning of leaf coloration in autumn,. Understanding spatial patterns in the strength of greendown has recently become possible with the advancement of Landsat phenology products, which show that greendown patterns vary at scales appropriate for linking these patterns to proposed environmental forcing factors. This study tested two non-mutually exclusive hypotheses for how leaf measurements and environmental factors correlate with greendown and decreasing NIR reflectance across sites. At the landscape scale, we used linear regression to test the effects of maximum greenness, elevation, slope, aspect, solar irradiance and canopy rugosity on greendown. Secondly, we used leaf chemical traits and reflectance observations to test the effect of nitrogen availability and intrinsic water use efficiency on leaf-level greendown, and landscape-level greendown measured from Landsat. The study was conducted using Quercus alba canopies across 21 sites of an eastern deciduous forest in North America between June and August 2014. Our linear model explained greendown variance with an R2=0.47 with maximum greenness as the greatest model effect. Subsequent models excluding one model effect revealed elevation and aspect were the two topographic factors that explained the greatest amount of greendown variance. Regression results also demonstrated important interactions between all three variables, with the greatest interaction showing that aspect had greater influence on greendown at sites with steeper slopes. Leaf-level reflectance was correlated with foliar δ13C (proxy for intrinsic water use efficiency), but foliar δ13C did not translate into correlations with landscape-level variation in greendown from Landsat. Therefore, we conclude that Landsat greendown is primarily indicative of landscape position, with a small effect of canopy structure, and no measureable effect of leaf reflectance. With this understanding of Landsat greendown we can better explain the effects of landscape factors on vegetation reflectance and perhaps on phenology, which would be very useful for studying phenology in the context of global climate change

BALD EAGLES (HALIAEETUS LEUCOCEPHALUS) AS INDICATORS OF GREAT LAKES ECOSYSTEM HEALTH

Environmental indicators have been proposed as a means to assess ecological integrity, monitoring both chemical and biological stressors. In this study, we used nestling bald eagles as indicators to quantify direct or indirect tertiary-level contaminant exposure. The spatial and temporal trends of polychlorinated biphenyl (PCB) congeners were evaluated in nestling plasma from 1999–2014. Two hexa-chlorinated congeners, PCB-138 and 153, were detected with the highest frequency and greatest concentrations throughout Michigan. Less-chlorinated congeners such as PCB-52 and 66 however, comprised a greater percentage of total PCB concentrations in nestlings proximate to urbanized areas, such as along the shorelines of Lake Erie. Toxic equivalents were greatest in the samples collected from nestlings located on Lake Erie, followed by the other Great Lakes spatial regions. Nestling plasma samples were also used to measure concentrations of the most heavily-used group of flame retardants, brominated diphenyl ethers (BDEs), and three groups of alternative flame retardants, non-BDE Brominated Flame Retardants (NBFRS), Dechloranes, and organophosphate esters (OPs). BDE-47, 99 and 100 contributed the greatest to total BDE concentrations. Concentrations of structurally similar NBFRs found in this study and recent atmospheric studies indicate that they are largely used as replacements to previously used BDE mixtures. A variety of Dechloranes, or derivatives of Mirex and Dechlorane Plus, were measured. Although, measured at lesser concentrations, environmental behavior of these compounds may be similar to mirex and warrant future research in aquatic species. Concentrations of OPs in nestling plasma were two to three orders of magnitude greater than all other groups of flame retardants. In addition to chemical indicators, bald eagles have also been proposed as indicators to identify ecological stressors using population measures that are tied to the fitness of individuals and populations. Using mortality as a population vitality rate, vehicle collisions were found to be the main source of mortality with a greater incidence for females during white-tailed deer (Odocoileus virginianus) hunting months and spring snow-melt. Lead poisoning was the second greatest source of mortality, with sources likely due to unretrieved hunter-killed, white-tailed deer carcasses, and possibly exacerbated by density-dependent effects due to the growing population in Michigan.