Browsing by Subject "Plug-in electric vehicles"
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Item Anticipating the impacts of climate policies on the U.S. light-duty-vehicle fleet, greenhouse gas emissions, and household welfare(2011-05) Paul, Binny Mathew; Kockelman, Kara; Greene, David L.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.Item An assessment of the system costs and operational benefits of vehicle-to-grid schemes(2013-12) Harris, Chioke Bem; Webber, Michael E., 1971-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.Item Energy and environmental contexts of cities, transportation systems, and emerging vehicle technologies : how plug-in electric vehicles and urban design influence energy consumption and emissions(2013-12) Nichols, Brice G.; Kockelman, KaraThis 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.Item Modeling and simulation of distribution system components in anticipation of a smarter electric power grid(2011-05) Toliyat, Amir; Kwasinski, Alexis; Grady, WiliamSuccessful 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.