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.

Harmonization of Life Cycle Climate Performance and Its Improvements for Heat Pump Applications

Life Cycle Climate Performance (LCCP) is an evaluation method by which heating, ventilation, air conditioning and refrigeration systems can be evaluated for their global warming impact over the course of their complete life cycle. LCCP is more inclusive than previous metrics such as Total Equivalent Warming Impact. It is calculated as the sum of direct and indirect emissions generated over the lifetime of the system “from cradle to grave”. Direct emissions include all effects from the release of refrigerants into the atmosphere during the lifetime of the system. This includes annual leakage and losses during the disposal of the unit. The indirect emissions include emissions from the energy consumption during manufacturing process, lifetime operation, and disposal of the system. This thesis proposes a standardized approach to the use of LCCP and traceable data sources for all aspects of the calculation. An equation is proposed that unifies the efforts of previous researchers. Data sources are recommended for average values for all LCCP inputs. A residential heat pump sample problem is presented illustrating the methodology. The heat pump is evaluated at five U.S. locations in different climate zones. An excel tool was developed for residential heat pumps using the proposed method. The primary factor in the LCCP calculation is the energy consumption of the system. The effects of advanced vapor compression cycles are then investigated for heat pump applications. Advanced cycle options attempt to reduce the energy consumption in various ways. There are three categories of advanced cycle options: subcooling cycles, expansion loss recovery cycles and multi-stage cycles. The cycles selected for research are the suction line heat exchanger cycle, the expander cycle, the ejector cycle, and the vapor injection cycle. The cycles are modeled using Engineering Equation Solver and the results are applied to the LCCP methodology. The expander cycle, ejector cycle and vapor injection cycle are effective in reducing LCCP of a residential heat pump by 5.6%, 8.2% and 10.5%, respectively in Phoenix, AZ. The advanced cycles are evaluated with the use of low GWP refrigerants and are capable of reducing the LCCP of a residential heat by 13.7%, 16.3% and 18.6% using a refrigerant with a GWP of 10. To meet the U.S. Department of Energy’s goal of reducing residential energy use by 40% by 2025 with a proportional reduction in all other categories of residential energy consumption, a reduction in the energy consumption of a residential heat pump of 34.8% with a refrigerant GWP of 10 for Phoenix, AZ is necessary. A combination of advanced cycle, control options and low GWP refrigerants are necessary to meet this goal.

RACISM IN DIGITAL ERA: DEVELOPMENT AND INITIAL VALIDATION OF THE PERCEIVED ONLINE RACISM SCALE (PORS V1.0)

Racism continues to thrive on the Internet. Yet, little is known about racism in online settings and the potential consequences. The purpose of this study was to develop the Perceived Online Racism Scale (PORS), the first measure to assess people’s perceived online racism experiences as they interact with others and consume information on the Internet. Items were developed through a multi-stage process based on literature review, focus-groups, and qualitative data collection. Based on a racially diverse large-scale sample (N = 1023), exploratory and confirmatory factor analyses provided support for a 30-item bifactor model with the following three factors: (a) 14-item PORS-IP (personal experiences of racism in online interactions), (b) 5-item PORS-V (observations of other racial/ethnic minorities being offended), and (c) 11-item PORS-I (consumption of online contents and information denigrating racial/ethnic minorities and highlighting racial injustice in society). Initial construct validity examinations suggest that PORS is significantly linked to psychological distress.