This thesis contains contributions in three areas: the theory of generalized calculus, numerical algorithms for operations research, and applications of optimization to problems in modern electric power systems. A geometric approach is used to advance the theory and tools used for studying generalized notions of derivatives for nonsmooth functions. These advances specifically pertain to methods for calculating subdifferentials and to expanding our understanding of a certain notion of derivative of set-valued maps, called the coderivative, in infinite dimensions. A strong understanding of the subdifferential is essential for numerical optimization algorithms, which are developed and applied to nonsmooth problems in operations research, including non-convex problems. Finally, an optimization framework is applied to solve a problem in electric power systems involving a smart solar inverter and battery storage system providing energy and ancillary services to the grid.

Community solar is a renewable energy practice that's been adopted by multiple U.S. states and is being considered by many more, including the state of Oregon. A recent senate bill in Oregon, called the "Clean Electricity and Coal Transition Plan", includes a provision that directs the Oregon Public Utility Commission to establish a community solar program for investor-owned utilities by late 2017. Thus, energy consumers in Portland will be offered participation in community solar projects in the near future. Community solar is a mechanism that allows ratepayers to experience both the costs and benefits of solar energy while also helping to offset the proportion of fossil-fuel generated electricity in utility grids, thus aiding climate change mitigation. For community solar to achieve market success in the residential sector of Portland, ratepayers of investor-owned utilities must socially accept this energy practice. The aim of this study was to forecast the potential social acceptance of community solar among Portland residents by measuring willingness to participate in these projects. Additionally, consumer characteristics, attitudes, awareness, and knowledge were captured to assess the influence of these factors on intent to enroll in community solar. The theory of planned behavior, as well as the social acceptance, diffusion of innovation, and dual-interest theories were frameworks used to inform the analysis of community solar adoption. These research objectives were addressed through a mixed-mode survey of Portland residents, using a stratified random sample of Portland neighborhoods to acquire a gradient of demographics. 330 questionnaires were completed, yielding a 34.2% response rate. Descriptive statistics, binomial logistic regression models, and mean willingness to pay were the analyses conducted to measure the influence of project factors and demographic characteristics on likelihood of community solar participation. Roughly 60% of respondents exhibited interest in community solar enrollment. The logistic regression model revealed the percent change in utility bill (essentially the rate of return on the community solar investment) as a dramatically influential variable predicting willingness to participate. Community solar project scenarios also had a strong influence on willingness to participate: larger, cheaper, and distant projects were preferred over small and expensive local projects. Results indicate that community solar project features that accentuate affordability are most important to energy consumers. Additionally, demographic characteristics that were strongly correlated with willingness to enroll were politically liberal ideologies, higher incomes, current enrollment in green utility programs, and membership in an environmental organization. Thus, the market acceptance of community solar in Portland will potentially be broadened by emphasizing affordability over other features, such as community and locality. Additionally, I explored attitudinal influences on interest in community solar by conducting exploratory factor analysis on attitudes towards energy, climate change, and solar barriers and subsequently conducting binomial logistic regression models. Results found that perceiving renewable energy as environmentally beneficial was positively correlated with intent to enroll in community solar, which supported the notion that environmental attitudes will lead to environmental behaviors. The logistic regression model also revealed a negative correlation between community solar interest and negative attitudes towards renewable energy. Perceptions of solar barriers were mild, indicating that lack of an enabling mechanism may be the reason solar continues to be underutilized in this region.

Oregon utilities are replacing their portfolios of traditional fossil fuel generation with renewable generating sources. Stepping away from carbon-producing energy will leave a deficit of on-demand power, resulting in decreased reliability. To overcome these technical challenges, utilities must maximize the use of their present dispatchable resources. One such resource is the Portland General Electric (PGE) Dispatchable Standby Generation Program (DSG), which is an aggregated 105 MWs of distributed generation (DG). These resources are brought on-line when there is a critical need for power. Resources are added to the program if a transfer trip scheme is in place or a modeling study reveals that the feeder load is at least three times the generator capacity. If the load-to-capacity ratio were lower, more assets could be added to the DSG program. To investigate the impacts of lowering the DG load-to-capacity ratio on existing distribution feeders, we use Open-Source Distribution System Simulator (OpenDSS). We modeled the Oxford Rural feeder by converting a utility CYME database to instantiation files using several MATLAB programs. A MATLAB control program varies the load-to-capacity ratio of the OpenDSS feeder model and monitors the generator behavior immediately following a fault. We analyzed the results to determine the ideal load-to-capacity ratio that prevents unintentional islanding. The results show that the instantaneous (50) relay element settings dictate both the minimum load-to-capacity ratio and the maximum DG capacity. The present three-to-one ratio is very conservative and can be reduced. Additional dispatchable resources include a five MW battery-inverter system currently used as grid-back up. The battery is grid-tied to a 12.4 kV feeder making it an ideal candidate for conservation voltage reduction (CVR). Using the same feeder model, we investigated the effects of lowering the system voltage to the allowable minimum using injections of reactive power. A lower system voltage reduces the load at peak times. Conversely, increasing the voltage prevents generation conflicts. To determine the benefit of CVR by VAr-injection on the Oxford Rural feeder, we created a MATLAB optimization program to output the optimal feeder voltage for reduced system power. We use a Simulink feedback model to determine the appropriate reactive power needed to achieve the voltage change. We analyze the system model to reveal that the feeder is ideal for CVR but the system capacity must be increased to achieve the maximum power reduction.

One of the greatest challenges that will face humanity in the 21st century is the issue of climate change brought about by emissions of greenhouse gases. Energy use is one of the primary sources of greenhouse gas emissions. However, it is also one of the most important contributors to improved human welfare over the past two centuries and will continue to be so for years to come. This quandary has led a number of researchers to suggest that geoengineering may be required in order to allow for continued use of fossil fuels while at the same time mitigating the effects of the associated greenhouse gas emissions on the global climate. The goal of this research was to develop a model that would allow decision-makers and policy analysts to assess the optimal mix of energy and geoengineering resources needed to meet global or regional energy demand at the lowest cost while accounting for appropriate emissions, greenhouse gas concentration, or temperature rise constraints. The resulting software model is called the Combined Energy and Geoengineering Optimization Model (CEAGOM). CEAGOM was then used to analyze the recently announced U.S.-China emissions agreement and to assess what the optimal global energy resource mix might be over the course of the 21st century, including the associated potential need for geoengineering. These analyses yielded optimal mixes of energy and geoengineering resources that could be used to inform regional and global energy and climate management strategies.

Methods of generating renewable energy such as through solar photovoltaic (PV) cells and wind turbines offer great promise in terms of a reduced carbon footprint and overall impact on the environment. However, these methods also share the attribute of being highly stochastic, meaning they are variable in such a way that is difficult to forecast with sufficient accuracy. While solar power currently constitutes a small amount of generating potential in most regions, the cost of photovoltaics continues to decline and a trend has emerged to build larger PV plants than was once feasible. This has brought the matter of increased variability to the forefront of research in the industry. Energy storage has been proposed as a means of mitigating this increased variability -- and thus reducing the need to utilize traditional spinning reserves -- as well as offering auxiliary grid services such as peak-shifting and frequency control. This thesis addresses the feasibility of using electrochemical storage methods (i.e. batteries) to decrease the ramp rates of PV power plants. By building a simulation of a grid-connected PV array and a typical Battery Energy Storage System (BESS) in the NetLogo simulation environment, I have created a parameterized tool that can be tailored to describe almost any potential PV setup. This thesis describes the design and function of this model, and makes a case for the accuracy of its measurements by comparing its simulated output to that of well-documented real world sites. Finally, a set of recommendations for the design and operational parameters of such a system are then put forth based on the results of several experiments performed using this model.

Smart grid has been described as the Energy Internet: Where Energy Technology meets Information Technology. The incorporation of such technology into vast existing utility infrastructures offers many advantages, including possibilities for new smart appliances, energy management systems, better integration of renewable energy, value added services, and new business models, both for supply- and demand-side management. Smart grid also replaces aging utility technologies that are becoming increasingly unreliable, as the average ages for many critical components in utility systems now exceed their original design lives. However, while smart grid offers the promise of revolutionizing utility delivery systems, many questions remain about how such systems can be rolled out at the state, regional, and national levels. Many unique regulatory and market structure challenges exist, which makes it critical to pick the right technology for the right situation and to employ it in the right manner. Technology Roadmapping may be a valuable approach for helping to understand factors that could affect smart grid technology and product development, as well as key business, policy and regulatory drivers. As emerging smart grid technologies are developed and the fledgling industry matures, a critical issue will be understanding how the combination of industry drivers impact one another, what barriers exist to achieving the benefits of smart grid technologies, and how to prioritize R&D and acquisition efforts. Since the planning of power grids often relies on regional factors, it will also be important investigate linkages between smart grid deployment and regional planning goals. This can be used to develop strategies for overcoming barriers and achieving the benefits of this promising new technology. This research builds upon existing roadmapping processes by considering an integrated set of factors, including policy issues, which are specifically tuned to the needs of smart grids and have not generally been considered in other types of roadmapping efforts. It will also incorporate expert judgment quantification to prioritize factors, show the pathways for overcoming barriers and achieving benefits, and discussing the most promising strategies for achieving these goals.

Microorganisms are capable of producing advanced biofuels that can be used as ‘drop-in’ alternatives to conventional liquid fuels. However, vital physiological processes and membrane properties are often disrupted by the presence of biofuel and limit the production yields. In order to make microbial biofuels a competitive fuel source, finding mechanisms for improving resistance to the toxic effects of biofuel production is vital. This investigation aims to identify resistance mechanisms from microorganisms that have evolved to withstand hydrocarbon-rich environments, such as those that thrive near natural oil seeps and in oil-polluted waters. First, using genomic DNA from Marinobacter aquaeolei, we constructed a transgenic library that we expressed in Escherichia coli. We exposed cells to inhibitory levels of pinene, a monoterpene that can serve as a jet fuel precursor with chemical properties similar to existing tactical fuels. Using a sequential strategy of a fosmid library followed by a plasmid library, we were able to isolate a region of DNA from the M. aquaeolei genome that conferred pinene tolerance when expressed in E. coli. We determined that a single gene, yceI, was responsible for the tolerance improvements. Overexpression of this gene placed no additional burden on the host. We also tested tolerance to other monoterpenes and showed that yceI selectively improves tolerance. Additionally, we used genomic DNA from Pseudomonas putida KT2440, which has innate solvent-tolerance properties, to create transgenic libraries in an E. coli host. We exposed cells containing the library to pinene, selecting for genes that improved tolerance. Importantly, we found that expressing the sigma factor RpoD from P. putida greatly expanded the diversity of tolerance genes recovered. With low expression of rpoDP. putida, we isolated a single pinene tolerance gene; with increased expression of the sigma factor our selection experiments returned multiple distinct tolerance mechanisms, including some that have been previously documented and also new mechanisms. Interestingly, high levels of rpoDP. putida induction resulted in decreased diversity. We found that the tolerance levels provided by some genes are highly sensitive to the level of induction of rpoDP. putida, while others provide tolerance across a wide range of rpoDP. putida levels. This method for unlocking diversity in tolerance screening using heterologous sigma factor expression was applicable to both plasmid and fosmid-based transgenic libraries. These results suggest that by controlling the expression of appropriate heterologous sigma factors, we can greatly increase the searchable genomic space within transgenic libraries. This dissertation describes a method of effectively screening genomic DNA from multiple organisms for genes to mitigate biofuel stress and shows how tolerance genes can improve bacterial growth in the presence of toxic biofuel compounds. These identified genes can be targeted in future studies as candidates for use in biofuel production strains to increase biofuel yields.

A sociotechnical energy transition requires both a shift to new technologies and attention to social issues like political movements, policy and human behavior. This dissertation investigates social elements of the renewable energy transition occurring at different scales. The core research questions are: How are universities creating and responding to the shifting language of fossil fuel investments? How and for whom do behavioral interventions work? And finally, do in-home displays (IHDs) change behaviors and attitudes of millennial energy users? The three studies covered here occurred within higher education and reflect the importance of colleges and universities as dynamic players in energy transitions. These spaces encourage learning and organizational change on the inside while also pushing outward, challenging social norms. Using a coding approach and text analysis software, this research identifies common frames of language used by colleges and universities who have released formal statements rejecting or adopting divestment policies. This study provides a quantitative assessment of themes and an early overview of this dynamic movement. The second and third study describe the outcomes of a behavioral energy experiment with off-campus students at the University of Vermont testing real-time feedback and financial incentives on individuals' behavior. The second study analyzes the results of a survey conducted with participants in the experiment, investigating changes in attitudes and self-reported behaviors and correlations with actual energy usage. Applying Wilcoxon-signed rank tests and a repeated measures marginal model, showed a minimal effect from the behavioral interventions in survey responses. The results also raise questions about surveys as a reliable predictor for behavior-based outcomes. In the third study, interview data from participants sheds light on questions of how and for whom behavioral interventions work. A within-households split-incentive is discovered, describing one factor contributing to the limited effect of in-home displays on household energy usage. Other factors affecting household energy use are also discussed. This dissertation concludes with recommendations for utilities and policy makers.

Power transmission network is the most important link in the country’s energy system as they carry large amounts of power at high voltages from generators to substations. Modern power system is a complex network and requires high-speed, precise, and reliable protective system. Faults in power system are unavoidable and overhead transmission line faults are generally higher compare to other major components. They not only affect the reliability of the system but also cause widespread impact on the end users. Additionally, the complexity of protecting transmission line configurations increases with as the configurations get more complex. Therefore, prediction of faults (type and location) with high accuracy increases the operational stability and reliability of the power system and helps to avoid huge power failure. Furthermore, proper operation of the protective relays requires the correct determination of the fault type as quickly as possible (e.g., reclosing relays). With advent of smart grid, digital technology is implemented allowing deployment of sensors along the transmission lines which can collect live fault data as they contain useful information which can be used for analyzing disturbances that occur in transmission lines. In this thesis, application of machine learning algorithms for fault classification and location identification on the transmission line has been explored. They have ability to “learn” from the data without explicitly programmed and can independently adapt when exposed to new data. The work presented makes following contributions: 1) Two different architectures are proposed which adapts to any N-terminal in the transmission line. 2) The models proposed do not require large dataset or high sampling frequency. Additionally, they can be trained quickly and generalize well to the problem. 3) The first architecture is based off decision trees for its simplicity, easy visualization which have not been used earlier. Fault location method uses traveling wave-based approach for location of faults. The method is tested with performance better than expected accuracy and fault location error is less than ±1%. 4) The second architecture uses single support vector machine to classify ten types of shunt faults and Regression model for fault location which eliminates manual work. The architecture was tested on real data and has proven to be better than first architecture. The regression model has fault location error less than ±1% for both three and two terminals. 5) Both the architectures are tested on real fault data which gives a substantial evidence of its application.

One of the biggest threats to the power systems as critical infrastructures is large-scale blackouts resulting from cascading failures (CF) in the grid. The ongoing shift in energy portfolio due to ever-increasing penetration of renewable energy sources (RES) may drive the electric grid closer to its operational limits and introduce a large amount of uncertainty coming from their stochastic nature. One worrisome change is the increase in CFs. The CF simulation models in the literature do not allow consideration of RES penetration in studying the grid vulnerability. In this dissertation, we have developed tools and models to evaluate the impact of RE penetration on grid vulnerability to CF. We modeled uncertainty injected from different sources by analyzing actual high-resolution data from North American utilities. Next, we proposed two CF simulation models based on simplified DC power flow and full AC power flow to investigate system behavior under different operating conditions. Simulations show a dramatic improvement in the line flow uncertainty estimation based on the proposed model compared to the simplified DC OPF model. Furthermore, realistic assumptions on the integration of RE resources have been made to enhance our simulation technique. The proposed model is benchmarked against the historical blackout data and widely used models in the literature showing similar statistical patterns of blackout size.