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Successful development of the electric power grid of the future, hereinafter referred to as a smart grid, implicitly demands the capability to model the behavior, performance, and cost of distribution-level smart grid components. The modeling and simulation of such individual components, together with their overall interaction, will provide a foundation for the design and configuration of a smart grid. It is the primary intent of this thesis, to provide a basic insight into the energy transfer of various distribution-level components by modeling and simulating their dynamic behavior. The principal operations of a smart grid must be considered, including variable renewable generation, energy storage, power electronic interfaces, variable load, and plug-in electric vehicles. The methodology involves deriving the mathematical equations of components, and, using the MATLAB/Simulink environment, creating modules for each component. Ultimately, these individual modules may be connected together via a voltage interface to perform various analyses, such as the treatment of harmonics, or to acquire an understanding of design parameters such as capacity, runtime, and optimal asset utilization. / text

Driven by technical, economic and environmental benefits for different stakeholders in the power industry, the electric distribution system is currently undergoing a major paradigm shift towards having an increasing portion of its growing demand supplied via distributed generation (DG) units. As the number of DG units increase; microgrids can be defined within the electric distribution system as electric regions with enough generation to meet all or most of its local demand. A microgrid should be able to operate in two modes, grid-connected or islanded. The IEEE standard 1547.4 enumerates a list of potential benefits for the islanded microgrid operation. Such benefits include: 1) improving customers’ reliability, 2) relieving electric power system overload problems, 3) resolving power quality issues, and 4) allowing for maintenance of the different power system components without interrupting customers. These benefits motivate the operation of microgrid systems in the islanded mode. However the microgrid isolation from the main grid creates special technical challenges that have to be comprehensively investigated in order to facilitate a successful implementation of the islanded microgrid concept. Motivated by these facts, the target of this thesis is to introduce new analysis and operational control algorithms to tackle some of the challenges associated with the practical implementation of the islanded microgrid concept. In order to accomplish this target, this study is divided into four perspectives: 1) developing an accurate steady-state analysis algorithm for islanded microgrid systems, 2) maximizing the possible utilization of islanded microgrid limited generation resources, 3) allowing for the decentralized operation of islanded microgrid systems and 4) enabling the islanded microgrid operation in distribution systems with high penetration of plug-in electric vehicles (PEVs). First for the steady-state analysis of islanded microgrid systems, a novel and generalized algorithm is proposed to provide accurate power flow analysis of islanded microgrid systems. Conventional power flow tools found in the literature are generally not suitable for the islanded microgrid operating mode. The reason is that none of these tools reflect the islanded microgrid special philosophy of operation in the absence of the utility bus. The proposed algorithm adopts the real characteristics of the islanded microgrid operation; i.e., 1) Some of the DG units are controlled using droop control methods and their generated active and reactive power are dependent on the power flow variables and cannot be pre-specified; 2) The steady-state system frequency is not constant and is considered as one of the power flow variables. The proposed algorithm is generic, where the features of distribution systems i.e. three-phase feeder models, unbalanced loads and load models have been taken in consideration. The effectiveness of the proposed algorithm, in providing accurate steady-state analysis of islanded microgrid systems, is demonstrated through several case studies. Secondly, this thesis proposes the consideration of a system maximum loadability criterion in the optimal power flow (OPF) problem of islanded microgrid systems. Such consideration allows for an increased utilization of the islanded microgrid limited generation resources when in isolation from the utility grid. Three OPF problem formulations for islanded microgrids are proposed; 1) The OPF problem for maximum loadability assessment, 2) The OPF for maximizing the system loadability, and 3) The bi-objective OPF problem for loadability maximization and generation cost minimization. An algorithm to achieve a best compromise solution between system maximum loadability and minimum generation costs is also proposed. A detailed islanded microgrid model is adopted to reflect the islanded microgrid special features and real operational characteristics in the proposed OPF problem formulations. The importance and consequences of considering the system maximum loadability in the operational planning of islanded microgrid systems are demonstrated through comparative numerical studies. Next, a new probabilistic algorithm for enabling the decentralized operation of islanded microgrids, including renewable resources, in the absence of a microgrid central controller (MGCC) is proposed. The proposed algorithm adopts a constraint hierarchy approach to enhance the operation of islanded microgrids by satisfying the system’s operational constraints and expanding its loading margin. The new algorithm takes into consideration the variety of possible islanded microgrid configurations that can be initiated in a distribution network (multi-microgrids), the uncertainty and variability associated with the output power of renewable DG units as well as the variability of the load, and the special operational philosophy associated with islanded microgrid systems. Simulation studies show that the proposed algorithm can facilitate the successful implementation of the islanded microgrid concept by reducing customer interruptions and enhancing the islanded microgrid loadability margins. Finally, this research proposes a new multi-stage control scheme to enable the islanded microgrid operation in the presence of high PEVs penetration. The proposed control scheme optimally coordinates the DG units operation, the shedding of islanded microgrid power demand (during inadequate generation periods) and the PEVs charging/discharging decisions. To this end, a three-stage control scheme is formulated in order to: 1) minimize the load shedding, 2) satisfy the PEVs customers’ requirements and 3) minimize the microgrid cost of operation. The proposed control scheme takes into consideration; the variability associated with the output power of renewable DG units, the random behaviour of PEV charging and the special features of islanded microgrid systems. The simulation studies show that the proposed control scheme can enhance the operation of islanded microgrid systems in the presence of high PEVs penetration and facilitate a successful implementation of the islanded microgrid concept, under the smart grid paradigm.

Variable generator output levels from renewable energies is an important technical obstacle to the transition from fossil fuels to renewable resources. Super grids and smart grids are among the most effective solutions to mitigate generation variability. In a super grid, electric utilities within an interconnected system can share generation and reserve units so that they can produce electricity at a lower overall cost. Smart grids, in particular demand response programs, enable flexible loads such as plug-in electric vehicles and HVAC systems to consume electricity preferntially in a grid-friendly way that assists the grid operator to maintain the power balance. These solutions, in conjunction with energy storage systems, can facilitate renewable integration. This study aims to provide an understanding of the achievable benefits from integrating demand response into wholesale and retail electricity markets, in particular in the presence of significant amounts of variable generation. Among the options for control methods for demand response, market-based approaches provide a relatively efficient use of load flexibility, without restricting consumers' autonomy or invading their privacy. In this regard, a model of demand response integration into bulk electric grids is presented to study the interaction between variable renewables and demand response in the double auction environment, on an hourly basis. The cost benefit analysis shows that there exists an upper limit of renewable integration, and that additional solutions such as super grids and/or energy storage systems are required to go beyond this threshold. The idea of operating an interconnection in an unified (centralized) manner is also explored. The traditional approach to the unit commitment problem is to determine the dispatch schedule of generation units to minimize the operation cost. However, in the presence of price-sensitive loads (market-based demand response), the maximization of economic surplus is a preferred objective to the minimization of cost. Accordingly, a surplus-maximizing hour-ahead scheduling problem is formulated, and is then tested on a system that represents a 20-area reduced model of the North America Western Interconnection for the planning year 2024. The simulation results show that the proposed scheduling method reduces the total operational costs substantially, taking advantage of renewable generation diversity. The value of demand response is more pronounced when ancillary services (e.g. real-time power balancing and voltage/frequency regulation) are also included along with basic temporal load shifting. Relating to this, a smart charging strategy for plug-in electric vehicles is developed that enables them to participate in a 5-minute retail electricity market. The cost reduction associated with implementation of this charging strategy is compared to uncontrolled charging. In addition, an optimal operation method for thermostatically controlled loads is developed that reduces energy costs and prevents grid congestion, while maintaining the room temperature in the comfort range set by the consumer. The proposed model also includes loads in the energy imbalance market. The simulation results show that market-based demand response can contribute to a significant cost saving at the sub-hourly level (e.g. HVAC optimal operation), but not at the super-hourly level. Therefore, we conclude that demand response programs and super grids are complementary approaches to overcoming renewable generation variation across a range of temporal and spatial scales. / Graduate / 0791 / sahandbehboodi@gmail.com

This thesis is divided into two parts. The first evaluates the role of the built environment in life-cycle energy consumption, by comparing different neighborhood and city styles. Through a holistic modeling and accounting framework, this work identifies the largest energy-consuming sectors, among residential and commercial buildings, personal vehicles and transit trips, and supporting infrastructure (roads, sidewalks, parking lots, water pipes, street lighting). Life-cycle energy calculations include operational energy use (e.g., gasoline for vehicles, electricity and natural gas for buildings) and embodied energy used to produce materials and construct buildings and infrastructure. Case study neighborhoods in Austin, Texas, and larger-scale regional models suggest that building energy demands comprise around 50% of life-cycle energy demands, while transportation demands (from driving and infrastructure alike) contribute around 40%, across all cases. However, results also suggest that population density and average residential unit size play a major role in defining per-capita energy consumption. Operational demands made up about 90% of life-cycle energy demands, suggesting that v most urban energy savings can be obtained from reduced personal vehicle trips and more efficient vehicles and buildings. Case study comparisons suggest that neighborhoods and regions with greater density and higher share of multi-family housing units tend to reduce operational (and thus life-cycle) energy demands with less travel demand and decreased home and work energy use, per capita. The second part of this modeled plug-in electric vehicle (PEV) emissions impacts in Texas, by considering four possible vehicle adoption scenarios (where PEVs make up 1, 5, 10, and 25% of total passenger vehicles). The analysis anticipates PEV electricity demand and emissions rates, based on current Texas power grid data. Results indicate that PEV emissions depend significantly on which specific power plants are used to power the vehicles, but that PEVs' average per-mile emissions rates for NO[subscript x], PM, and CO₂ are all likely to be lower than today's average passenger car, when today's average mix is used. Power produced from 100% coal plants could produce 14 times as much NO[subscript x], 3,200 times as much SO₂, nearly 10 times as much CO₂ and CO₂eq, 2.5 times as much PM₁₀, and VOCs, and nearly 80 times the NO₂ compared to a grid with 100% natural gas plants. / text

Widespread adoption of plug-in electric vehicles (PEVs) can help to achieve deep reductions in global greenhouse gas emissions; however, the degree to which this potential will be realized depends on consumers’ decisions to purchase these vehicles over conventional ones. To provide comprehensive insight into the psychological and contextual influences on consumer vehicle preferences, three studies were performed using a mixed-methods approach. Study 1 employed a survey and stated choice experiment to explore: 1) the explanatory power of the three psychological variables from Ajzen’s (1991; 2005) theory of planned behaviour in predicting PEV purchase intentions among new vehicle buyers from British Columbia, and 2) the influence of hypothetical variations in financial and non-financial incentives on estimated PEV preference, with the goal of informing the design of provincial policy measures. Vehicle preferences were most strongly influenced by purchase price and point-of-sale incentives – with a roughly 4% forecasted increase in PEV new vehicle market share under a $5,000 purchase rebate – as well as by attitudes about PEVs (especially concerning personally-relevant PEV benefits), perceived behavioural control, and social norms. In Study 2, a latent class choice model was used to integrate survey and choice experiment data to characterize consumer classes based on vehicle preferences, demographic characteristics, and psychological variables. Findings revealed profiles of five distinct preference-based segments and demonstrated that the inclusion of psychological covariates can improve the fit of such latent class models. Study 3 extended these findings through a controlled message framing experiment that evaluated the impact of psychological distance on PEV purchase intentions. Results demonstrated that messages emphasizing both personally-relevant and societally-relevant PEV benefits increased related purchase intentions compared to the control group. Taken together, these findings may be useful in the development of PEV policies as well as targeted marketing and communications strategies aimed at supporting a transition to PEVs within Canada. / Graduate / 0451 / 0621 / 0709 / christine.kormos@gmail.com

Submitted by Marlucy Farias Medeiros (marlucy.farias@unipampa.edu.br) on 2017-10-31T16:36:39Z No. of bitstreams: 1 John Jefferson Antunes Saldanha - 2017.pdf: 2295770 bytes, checksum: 7d9b5b6835d3e02633dca3155dd44fe7 (MD5) / Approved for entry into archive by Marlucy Farias Medeiros (marlucy.farias@unipampa.edu.br) on 2017-10-31T18:24:47Z (GMT) No. of bitstreams: 1 John Jefferson Antunes Saldanha - 2017.pdf: 2295770 bytes, checksum: 7d9b5b6835d3e02633dca3155dd44fe7 (MD5) / Made available in DSpace on 2017-10-31T18:24:47Z (GMT). No. of bitstreams: 1 John Jefferson Antunes Saldanha - 2017.pdf: 2295770 bytes, checksum: 7d9b5b6835d3e02633dca3155dd44fe7 (MD5) Previous issue date: 2017-09-28 / Uma difusão considerável p elo uso dos veículos elétricos plug-in (VEPs) tem sido promovida, de modo a reduzir as emissões poluentes dos veículos movidos a combustão, bem como preservar as fontes de energia fóssil. Entretanto, cabe ressaltar que os VEPs necessitam se conectar a rede elétrica para recarregar suas baterias. Nesse contexto, caso uma quantidade significativa de veículos elétricos plug-in solicitem recarga ao mesmo tempo, a operação do sistema elétrico de potência (SEP) será comprometida. Em contrapartida, os VEPs também podem auxiliar a rede elétrica através do controle da taxa de recarga e injeção de energia ativa. Assim, é importante realizar o controle da recarga dos VEPs. Dessa forma, este trabalho propõe um sistema inteligente fundamentado em duas interfaces para controlar a taxa de recarga dos VEPs. A primeira interface visa controlar a taxa de recarga de uma frota de veículos com base em um controlador lógico fuzzy projetado e posteriormente ajustado. Nesta interface, buscam-se atender os requisitos do consumidor. Na segunda, gerenciam-se diversas frotas de VEPs visando minimizar perdas de energia e desvios de tensão na rede elétrica. Os resultados da primeira interface mostram que ambos os controladores projetado e ajustado respondem ao cálculo da taxa de recarga levando em consideração as informações inseridas pelo consumidor. Em adição, a resposta do controlador ajustado é mais próxima da resposta desejada, comparando com o controlador projetado. Os resultados da segunda interface mostram que o método de otimização reduziu as perdas de energia elétrica e os desvios de tensão no sistema teste estudado. Concomitantemente, a energia entregue para os VEPs aumentou de maneira significativa. Desta forma, com o sistema desenvolvido, espera-se reduzir o impacto no sistema elétrico de potência e otimizar sua operação, beneficiando a concessionária local, a rede elétrica e o consumidor. / In the present work we investigated experimentally and theoretically the photophysical characterization of organic compounds of the type benzothiazoles, targeting applications in optoelectronic devices, mainly in organic light emitting diodes and photoelectric devices. The study was developed to identify the optical and structural properties of the compounds and the effect of the addition of an amine radical on the ring PhO (benzene-bound benzene) of the benzothiazole compound. Other variations were analyzed, such as changes in the positions of the amine radical added to said compound and absence of the hydroxyl radical. Absorption and photoluminescence experiments were carried out with the purpose of verifying the excitation and fluorescence energies of the compounds, as well as Stokes displacement. The photophysical characterization was also investigated theoretically by means of an ab initio or first principles computational model based on the Density Functional Theory (DFT), implemented in the Gaussian® program, which uses quantum mechanics to calculate the molecular structures and their vibrational properties. We investigated the molecular geometric structure, obtaining the interatomic distances, structure of electronic orbitals, diagrams of energy bands, molecular vibrations and frequency of vibrational modes. By means of Raman spectroscopy, the frequencies of the active Raman vibrational modes were obtained, allowing the comparison with the theoretical results of the simulations. The compounds 4HBS, 4HBSN and 5HBS have their first theoretical characterization from the study of this dissertation.

With the emerging nationwide availability of plug-in electric vehicles (PEVs) at prices attainable for many consumers, electric utilities, system operators, and researchers have been investigating the impact of this new source of electricity demand. The presence of PEVs on the electric grid might offer benefits equivalent to dedicated utility-scale energy storage systems by leveraging vehicles' grid-connected energy storage through vehicle-to-grid (V2G) enabled infrastructure. Existing research, however, has not effectively examined the interactions between PEVs and the electric grid in a V2G system. To address these shortcomings in the literature, longitudinal vehicle travel data are first used to identify patterns in vehicle use. This analysis showed that vehicle use patterns are distinctly different between weekends and weekdays, seasonal interactions between vehicle charging, electric load, and wind generation might be important, and that vehicle charging might increase already high peak summer electric load in Texas. Subsequent simulations of PEV charging were performed, which revealed that unscheduled charging would increase summer peak load in Texas by approximately 1\%, and that uncertainty that arises from unscheduled charging would require only limited increases in frequency regulation procurements. To assess the market potential for the implementation of a V2G system that provides frequency regulation ancillary services, and might be able to provide financial incentives to participating PEV owners, a two-stage stochastic programming formulation of a V2G system operator was created. In addition to assessing the market potential for a V2G system, the model was also designed to determine the effect of the market power of the V2G system operator on prices for frequency regulation, the effect of uncertainty in real-time vehicle availability and state-of-charge on the aggregator's ability to provide regulation services, and the effect of different vehicle characteristics on revenues. Results from this model showed that the V2G system operator could generate revenue from participation in the frequency regulation market in Texas, even when subject to the uncertainty in real-time vehicle use. The model also showed that the V2G system operator would have a significant impact on prices, and thus as the number of PEVs participating in a V2G program in a given region increased, per-vehicle revenues, and thus compensation provided to vehicle owners, would decline dramatically. From these estimated payments to PEV owners, the decision to participate in a V2G program was analyzed. The balance between the estimated payments to PEV owners for participating in a V2G program and the increased probability of being left with a depleted battery as a result of V2G operations indicate that an owner of a range-limited battery electric vehicle (BEV) would probably not be a viable candidate for joining a V2G program, while a plug-in hybrid electric vehicle (PHEV) owner might find a V2G program worthwhile. Even for a PHEV owner, however, compensation for participating in a V2G program will provide limited incentive to join. / text

The first part of this thesis relies on stated and revealed preference survey results across a sample of U.S. households to first ascertain vehicle acquisition, disposal, and use patterns, and then simulate these for a synthetic population over time. Results include predictions of future U.S. household-fleet composition, use, and greenhouse gas (GHG) emissions under nine different scenarios, including variations in fuel and plug-in-electric-vehicle (PHEV) prices, new-vehicle feebate policies, and land-use-density settings. The adoption and widespread use of plug-in vehicles will depend on thoughtful marketing, competitive pricing, government incentives, reliable driving-range reports, and adequate charging infrastructure. This work highlights the impacts of various directions consumers may head with such vehicles. For example, twenty-five-year simulations at gas prices at $7 per gallon resulted in the highest market share predictions (16.30%) for PHEVs, HEVs, and Smart Cars (combined) — and the greatest GHG-emissions reductions. Predictions under the two feebate policy scenarios suggest shifts toward fuel-efficient vehicles, but with vehicle miles traveled (VMT) rising slightly (by 0.96% and 1.42%), thanks to lower driving costs. The stricter of the two feebate policies – coupled with gasoline at $5 per gallon – resulted in the highest market share (16.37%) for PHEVs, HEVs, and Smart Cars, but not as much GHG emissions reduction as the $7 gas price scenario. Total VMT values under the two feebate scenarios and low-PHEV-pricing scenarios were higher than those under the trend scenario (by 0.56%, 0.96%, and 1.42%, respectively), but only the low-PHEV-pricing scenario delivered higher overall GHG emission estimates (just 0.23% more than trend) in year 2035. The high-density scenario (where job and household densities were quadrupled) resulted in the lowest total vehicle ownership levels, along with below-trend VMT and emissions rates. Finally, the scenario involving a $7,500 rebate on all PHEVs still predicted lower PHEV market share than the $7 gas price scenario (i.e., 2.85% rather than 3.78%). The second part of this thesis relies on data from the U.S. Consumer Expenditure Survey (CEX) to estimate the welfare impacts of carbon taxes and household-level capping of emissions (with carbon-credit trading allowed). A translog utility framework was calibrated and then used to anticipate household expenditures across nine consumer goods categories, including vehicle usage and vehicle expenses. An input-output model was used to estimate the impact of carbon pricing on goods prices, and a vehicle choice model determined vehicle type preferences, along with each household’s effective travel costs. Behaviors were predicted under two carbon tax scenarios ($50 per ton and $100 per ton of CO2-equivalents) and four cap-and-trade scenarios (10-ton and 15-ton cap per person per year with trading allowed at $50 per ton and $100 per ton carbon price). Results suggest that low-income households respond the most under a $100-per-ton tax but increase GHG emissions under cap-and-trade scenarios, thanks to increased income via sale of their carbon credits. High-income households respond the most across all the scenarios under a 10-ton cap (per household member, per year) and trading at $100 per ton scenario. Highest overall emission reduction (47.2%) was estimated to be under $100 per ton carbon tax. High welfare loss was predicted for all households (to the order of 20% of household income) under both the policies. Results suggest that a carbon tax will be regressive (in terms of taxes paid per dollar of expenditure), but a tax-revenue redistribution can be used to offset this regressivity. In the absence of substitution opportunities (within each of the nine expenditure categories), these results represent highly conservative (worst-case) results, but they illuminate the behavioral response trends while providing a rigorous framework for future work. / text

This thesis presents novel algorithms and a revised modeling framework to evaluate residential aggregate electrical demand response performance under scenarios with limited device-state feedback. These algorithms permit the provision of balancing reserves, or the smoothing of variable renewable energy generation, via an externally supplied target trajectory. The responsive load populations utilized were home heat pumps and deferred electric vehicle charging. As fewer devices in a responsive population report their state information, the error of the demand response program increases moderately but remains below 8%. The associated error of the demand response program is minimized with responsive load populations of approximately 4500 devices; the available capacity of the demand response system scales proportionally with population size. The results indicate that demand response programs with limited device-state feedback may provide a viable option to reduce overall system costs and address privacy concerns of individuals wishing to participate in a demand response program. / Graduate