The increased penetration of renewable energy sources poses new challenges for grid stability. The stochastic and uncontrollable generation of solar and wind power cannot be adjusted to match the load profile, and the transition away from traditional synchronous generators is reducing the grid capacity to arrest and recover from frequency disturbances. Additionally, the distributed nature of many renewable energy sources makes centralized control of generation more complicated. The traditional power system paradigm balances the supply and demand of electricity on the grid by regulating generation. As this becomes more difficult, one alternative is to adjust the load instead. This is not entirely novel, and utilities have incentivized large industrial customers to reduce consumption during peak hours for years. However, the residential sector, which constitutes 37% of electricity consumption in the U.S., currently has very little capacity for load control. Smart electric water heaters provide utilities with an appliance that can be remotely controlled and serves as a form of energy storage. They have very fast response times and make up a large amount of residential energy consumption, making them useful for load peak shifting as well as other ancillary grid services. As smart appliances become increasingly widespread, more and more devices can be brought into the utility's control network and aggregated into a flexible resource on a megawatt scale. This work demonstrates the usefulness of aggregated electric water heaters for peak shifting and frequency response. Because a large number of assets are required, emulators are developed based on observations of real devices. Emulated water heaters are then connected to an energy resource aggregator using an internet-of-things network. The aggregator successfully uses these assets to shift consumption away from peak hours. An algorithm was developed for detecting upward frequency disturbances in real-time. The aggregator uses this algorithm to show that an aggregation of water heaters is well-suited to respond to these frequency disturbances by quickly adding a large amount of load to the grid.

Residential electricity consumption in the United States has many adverse impacts, such as greenhouse gas emissions, dependence on fossil fuels, and costs. Efficient and renewable energy technologies have the potential to help mitigate some of these impacts, but appear to be under-utilized in the United States. One major barrier to expanding the deployment of these kinds of technologies and maximizing the benefits they can provide is a lack of consumer engagement. The overall purpose of this thesis is to better understand the extent to which efficient and renewable energy technologies are being engaged with and what factors may influence such engagement (or lack thereof) through case studies on smart meters and a community anaerobic digester system (CADS) in Vermont. In this thesis, engagement involves awareness, support, and utilization. Additionally, a subset of awareness (a precursor to awareness for many) was examined in each of these studies, which is interest in receiving additional information on the technology. While each case study focuses on different aspects of engagement that are unique to each smart meters and CADS, there is some overlap on the topics explored, especially when it comes to awareness of the technology, potential concerns about the technology, and interest in receiving additional information on it. The focus of the first study is on how efficiently smart meters have been utilized by residential electricity customers in Vermont and what factors may influence this. This study was conducted via a statewide telephone survey in Vermont and involved a sample that was statistically representative of the state. These data were analyzed via quantitative analysis. The focus of the second study is on local support of a CADS in Vermont and what factors may influence this. This study was conducted via a mailout survey to houses located in or near the area where the community anaerobic digester was located, and the data were analyzed via quantitative and qualitative analysis. In both studies, limitations to engagement with the technologies were found. In the smart meter study, less than 50% of the surveyed customers reported having a smart meter and, for those who did report having a smart meter, less than 20% of them thought that the smart meter had reduced their electricity use. In the CADS study, 52.1% of respondents reported being familiar with the CADS project, and 69.8% reported support for the project. However, other forms of support for the project, such as WTP for the Cow Power program or willingness to drop of food scraps to the CADS, were more limited. Additionally, a variety of demographic and other factors were found to have a statistically significant impact on or relationship to consumer engagement with these technologies. Overall, the results show that there is some engagement with these technologies, but more can be done to bolster engagement with them. One potential strategy to increase engagement with these technologies may be to tailor outreach according to factors that correspond to different levels of engagement. It is hoped that the results from these studies can be used to help improve consumer engagement with these and other efficient and renewable energy technologies, thus hopefully expanding their utilization and benefits they can provide in the process.

Our energy system is rapidly transforming, partially due to advances in internet and communications technologies that leverage an unprecedented amount of data. Industry proponents of the so-called “smart grid” suggest these technologies facilitate deeper engagement with end-users of energy (utility customers) that can in turn drive behavior-based changes and accelerate a renewable energy transition. While there has been progress in understanding how these technologies change consumer behavior using, for example, real-time feedback, it’s unclear how specific segments (e.g., renters) respond to these interventions; it’s also unclear why feedback is, or is not, producing changes in energy consumption. The literature suggests that behavioral strategies (e.g. information feedback, competitions, incentives) coupled with technology may present a way for utilities and efficiency programs to create savings—expanding opportunities for those often underserved by traditional approaches, such as renters—yet this coupling is not well understood, neither broadly (for all end users) nor specifically (for renters). This dissertation builds upon that literature and explores a human side of the smart grid, using a field experiment in renter households to test the interacting effects of real-time energy feedback and a novel form of financial incentive, referred to here as a competitive performance-based incentive. The experiment had two phases: phase one tested the feedback against a control group; phase two tested feedback, the incentive, and a combined treatment, against a control group. Results of these interventions were measured with pre- and post-treatment surveys as well as observed electricity consumption data from each household’s smart meter. The results of this experiment are described in three papers. Paper one examines the interventions’ individual and combined effectiveness at motivating renters to reduce or shift timing of electricity consumption. Feedback alone produced a significant savings effect in phase one. In phase two, the effect of the feedback wore off; the incentive alone had no significant effect; and the group that received feedback and the incentive experienced a doubling of savings relative to the effect of feedback alone, as observed in phase one. Paper two uses pre- and post-intervention survey data to examine how individual perceptions of energy change as a result of the interventions. Perception of large energy-using appliances changed the most in households that received feedback, suggesting that better information may lead to more effective behavior changes. Paper three leverages the results of the first two components to evaluate the policy implications and impacts on demand side management for utilities, efficiency programs, and the potential for behavior-based energy efficiency programs. Advocates of the smart grid must recognize the technology alone cannot produce savings without better engagement of end-users. Utility rate designers must carefully consider how time-based rates alone may over-burden those without the enabling technology to understand the impact of their energy choices.

The transportation sector is a largest emitter of greenhouse gases in the U.S., accounting for 28.6% of all 2016 emissions, the majority of which come from the passenger vehicle fleet [1,2]. One major technology that is being investigated by researchers, planners, and policy makers to help lower the emissions from the transportation sector is the plug-in electric vehicle (PEV). The focus of this work is to investigate and model the impacts of increased levels of PEVs on the regional electric power grid and on the net change in CO2 emissions due to the decrease tailpipe emissions and the increase in electricity generation under current emissions caps. The study scope includes all of New England and New York state, modeled as one system of electricity supply and demand, which includes the estimated 2030 baseline demand and the cur- rent generation capacity plus increased renewable capacity to meet state Renewable Portfolio Standard targets for 2030. The models presented here include fully electric vehicles and plug-in hybrids, public charging infrastructure scenarios, hourly charging demand, solar and wind generation and capacity factors, and real-world travel derived from the 2016-2017 National Household Travel Survey. We make certain assumptions, informed by the literature, with the goal of creating a modeling methodology to improve the estimation of hourly PEV charging demand for input into regional electric sector dispatch models. The methodology included novel stochastic processes, considered seasonal and weekday versus weekend differences in travel, and did not force the PEV battery state-of-charge to be full at any specific time of day. The results support the need for public charging infrastructure, specifically at workplaces, with the “work” infrastructure scenario shifting more of the unmanaged charging demand to daylight hours when solar generation could be utilized. Workplace charging accounted for 40% of all non-home charging demand in the scenario where charging infrastructure was “universally” available. Under the increased renewable fuel portfolio, the reduction in average CO2 emissions ranged from 90 to 92% for the vehicles converted from ICEV to PEV. The total emissions reduced for 15% PEV penetration and universally available charging infrastructure was 5.85 million metric tons, 5.27% of system-wide emissions. The results support the premise of plug-in electric vehicles being an important strategy for the reduction of CO2 emissions in our study region. Future investigation into the extent of reductions possible with both the optimization of charging schedules through pricing or other mechanisms and the modeling of grid level energy storage is warranted. Additional model development should include a sensitivity analysis of the PEV charging demand model parameters, and better data on the charging behavior of PEV owners as they continue to penetrate the market at higher rates.

The surge of mobile devices, such as smartphones, and tables, demands additional capacity. On the other hand, Internet-of-Things (IoT) and smart grid, which connects numerous sensors, devices, and machines require ubiquitous connectivity and data security. Additionally, some use cases, such as automated manufacturing process, automated transportation, and smart grid, require latency as low as 1 ms, and reliability as high as 99.99\%. To enhance throughput and support massive connectivity, sharing of the unlicensed spectrum (3.5 GHz, 5GHz, and mmWave) is a potential solution. On the other hand, to address the latency, drastic changes in the network architecture is required. The fifth generation (5G) cellular networks will embrace the spectrum sharing and network architecture modifications to address the throughput enhancement, massive connectivity, and low latency. To utilize the unlicensed spectrum, we propose a fixed duty cycle based coexistence of LTE and WiFi, in which the duty cycle of LTE transmission can be adjusted based on the amount of data. In the second approach, a multi-arm bandit learning based coexistence of LTE and WiFi has been developed. The duty cycle of transmission and downlink power are adapted through the exploration and exploitation. This approach improves the aggregated capacity by 33\%, along with cell edge and energy efficiency enhancement. We also investigate the performance of LTE and ZigBee coexistence using smart grid as a scenario. In case of low latency, we summarize the existing works into three domains in the context of 5G networks: core, radio and caching networks. Along with this, fundamental constraints for achieving low latency are identified followed by a general overview of exemplary 5G networks. Besides that, a loop-free, low latency and local-decision based routing protocol is derived in the context of smart grid. This approach ensures low latency and reliable data communication for stationary devices. To address data security in wireless communication, we introduce a geo-location based data encryption, along with node authentication by k-nearest neighbor algorithm. In the second approach, node authentication by the support vector machine, along with public-private key management, is proposed. Both approaches ensure data security without increasing the packet overhead compared to the existing approaches.

The growing computational demand of scientific applications has greatly motivated the development of large-scale high-performance computing (HPC) systems in the past decade. To accommodate the increasing demand of applications, HPC systems have been going through dramatic architectural changes (e.g., introduction of many-core and multi-core systems, rapid growth of complex interconnection network for efficient communication between thousands of nodes), as well as significant increase in size (e.g., modern supercomputers consist of hundreds of thousands of nodes). With such changes in architecture and size, the energy consumption by these systems has increased significantly. With the advent of exascale supercomputers in the next few years, power consumption of the HPC systems will surely increase; some systems may even consume hundreds of megawatts of electricity. Demand response programs are designed to help the energy service providers to stabilize the power system by reducing the energy consumption of participating systems during the time periods of high demand power usage or temporary shortage in power supply. This dissertation focuses on developing energy-efficient demand-response models and algorithms to enable HPC system's demand response participation. In the first part, we present interconnection network models for performance prediction of large-scale HPC applications. They are based on interconnected topologies widely used in HPC systems: dragonfly, torus, and fat-tree. Our interconnect models are fully integrated with an implementation of message-passing interface (MPI) that can mimic most of its functions with packet-level accuracy. Extensive experiments show that our integrated models provide good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance. In the second part, we present an energy-efficient demand-response model to reduce HPC systems' energy consumption during demand response periods. We propose HPC job scheduling and resource provisioning schemes to enable HPC system's emergency demand response participation. In the final part, we propose an economic demand-response model to allow both HPC operator and HPC users to jointly reduce HPC system's energy cost. Our proposed model allows the participation of HPC systems in economic demand-response programs through a contract-based rewarding scheme that can incentivize HPC users to participate in demand response.

Wireless power transfer (WPT) technologies have become important for our everyday life. The most commonly used near-field WPT method is inductive coupling, which suffers from low efficiency and small range. The Strongly Coupled Magnetic Resonance (SCMR) method was developed recently, and it can be used to wirelessly transfer power with higher efficiency over a longer distance than the inductive coupling method. This dissertation develops new SCMR systems that have better performance compared to standard SCMR systems. Specifically, two new 3-D SCMR systems are designed to improve the angular misalignment sensitivity of WPT systems. Their power transfer efficiency for different angular misalignment positions are studied and analyzed. Prototypes are built for both systems and their performance is validated through measurement. Furthermore, new planar broadband conformal SCMR (CSCMR) systems are developed that maintain high efficiency while providing significantly larger bandwidth than standard CSCMR systems. Such broadband CSCMR systems are used here for the first time to simultaneously accomplish highly efficient wireless power transfer and high data rate communication through the same wireless link. These systems that combine wireless power and communication are expected to enable next-generation applications with battery-less and “power-hungry” sensors. Example applications include implantable and wearable sensors as well as embedded sensors for structural health monitoring.

With the growth of renewable energy usage and energy storage adoption in recent decades, people have started to reevaluate the possible roles of dc systems in current and future electrical systems. The dc voltage distribution has been applied in various applications, such as data centers and aircraft industry, for high efficiency and power density. However, for some applications such as subsea gas and oil fields, and ocean observatory systems, the dc current distribution is preferred over dc voltage distribution for its low cost and robustness against cable faults. Design and control of dc power distribution systems for different applications is an emerging research area with complex technical challenges. This dissertation solves the technical challenges in analysis, design, modeling, control and protection of series resonant converters (SRCs) for dc current distribution applications. An optimum design that has high efficiency, high reliability, and minimum required control efforts for the SRC with constant input current has been achieved and demonstrated by applying the analysis and design procedures developed in this dissertation. The modeling and analysis presented in this dissertation represents an operating condition that has not been studied in the literature and could be easily extended to other resonant converter topologies. Explicit analytical expressions have been provided for all key transfer functions, including input impedance and control-to-output, offering valuable resources to design feed-back regulation and to evaluate system stability. Based on the control strategies and control design presented in this dissertation, stable and reliable operation of dc current distribution systems with long distance cable has been achieved and demonstrated. The proposed analysis, design procedure, stability evaluation, control strategy and protection techniques in this dissertation can be applied to a wide range of similar scenarios as well, which greatly increases their value.

The purpose of the present study is to determine the thermal feasibility of an air-cooled power inverter. The inverter circuitry layout is designed in tandem with the thermal management of the devices. The cylindrical configuration of the air-cooled inverter concept accommodates a collinear axial air blower and a cylindrical capacitor with inverter cards oriented radially between them. Cooling air flows from the axial fan around the inverter cards and through the center hole of the cylindrical capacitor. The present study is a continuation of the thermal feasibility study conducted in fiscal year 2009 for the Oak Ridge National Laboratory to design a power inverter with a radial inflow cylindrical configuration. Results in the present study are obtained by modeling the inverter concept in computer simulations using the finite element method. Air flow rate, ambient air temperature, voltage, and device switching frequency are studied parametrically. Inlet air temperature was 50°C for all the results reported. Transient and steady-state simulations are based on inverter current that represents the US06 supplemental federal test procedure from the US EPA. The source of heat to the system comes from the power dissipated in the form of heat from the switches and diodes and is modeled as a function of the voltage, switching frequency, current, and device temperature. Since the device temperature is a result as well as an input variable, the steady-state and transient solution are iterative on this parameter. The results demonstrate the thermal feasibility of using air to cool an axial-flow power inverter. This axial inflow configuration decreases the pressure drop through the system by 63% over the radial inflow configuration, and the ideal blower power input for an inlet air flow rate of 540 cfm is reduced from 936 W to 312 W for the whole inverter. When the model is subject to one or multiple current cycles, the maximum device temperature does not exceed 164°F (327°F) for an inlet flow rate of 270 cfm, ambient temperature of 120°C, voltage of 650 V, and switching frequency of 20 kHz. Although the maximum temperature in one cycle is most sensitive to ambient temperature, the ambient temperature affect decays after approximately half the duration of one cycle. Of the parametric variables considered in the transient simulations, the system is most sensitive to inlet air flow rate.

This thesis study presents the design, simulation, micro fabrication, and testing steps of microelectromechanical systems (MEMS) based electromagnetic micro power generators. These generators are capable of generating power using already available environmental vibrations, by implementing the electromagnetic induction technique. There are mainly two objectives of the study: (i) to increase the bandwidth of the traditional micro generators and (ii) to improve their efficiency at low frequency environmental vibrations of 1-100 Hz where most vibrations exist. Four main types of generators have been proposed within the scope of this thesis study. The first type of generator is mainly composed of 20 parylene cantilevers on which coils are fabricated, where the cantilevers are capable of resonating with external vibrations with respect to a stationary magnet. This generator has dimensions of 9.5&times / 8&times / 6 mm3, and it has been shown that 0.67 mV of voltage and 56 pW of power output can be obtained from a single cantilever of this design at a vibration frequency of 3.45 kHz. The second type generator aims to increase the bandwidth of the traditional designs by implementing cantilevers with varying length. This generator is sized 14&times / 12.5&times / 8 mm3, and the mechanical design and energy generation concept is similar to the first design. The test results show that by using 40 cantilevers with a length increment of 3 &amp / #956 / m, the overall bandwidth of the generator can be increased to 1000 Hz. It has also been shown that 9 mV of constant voltage and 1.7 nW of constant power output can be obtained from the overall device in a vibration frequency range of 3.5 to 4.5 kHz. The third type is a standard large mass coil type generator that has been widely used in the literature. In this case, the generator is composed of a stationary base with a coil and a magnet-diaphragm assembly capable of resonating with vibrations. The fabricated device has dimensions of 8.5&times / 7&times / 2.5 mm3, and it has been considered in this study for benchmarking purposes only. The test results show that 0.3 mV of voltage and 40 pW of power output can be obtained from the fabricated design at a vibration frequency of 113 Hz. The final design aims to mechanically up-convert low frequency environmental vibrations of 1-100 Hz to a much higher frequency range of 2-3 kHz. This type of generator has been implemented for the first time in the literature. The generator is composed of two parts / a diaphragm-magnet assembly on the top, and 20 cantilevers that have coils connected in series at the base. The diaphragm oscillates by low frequency environmental vibrations, and catches and releases the cantilevers from the tip points where magnetic nickel (Ni) areas are deposited. The released cantilevers then start decaying out oscillations that is at their damped natural frequency of 2-3 kHz. It has been shown with tests that frequency up-conversion is realized in micro scale. The fabricated device has dimensions of 8.5&times / 7&times / 2.5 mm3, and a maximum voltage and power output of 0.57 mV and 0.25 nW can be obtained, respectively, from a single cantilever of the fabricated prototype at a vibration frequency of 113 Hz.