| LDR | | 00000nam u2200205 4500 |
| 001 | | 000000435264 |
| 005 | | 20200228090811 |
| 008 | | 200131s2019 ||||||||||||||||| ||eng d |
| 020 | |
▼a 9781085792875 |
| 035 | |
▼a (MiAaPQ)AAI13885628 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 629.2 |
| 100 | 1 |
▼a Sheppard, Colin J. R. |
| 245 | 10 |
▼a A Nexus Between Two Disruptions: A Multiscale Analysis of Transportation Electrification to Forecast the Impacts of Vehicle Grid Integration. |
| 260 | |
▼a [S.l.]:
▼b University of California, Berkeley.,
▼c 2019. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2019. |
| 300 | |
▼a 213 p. |
| 500 | |
▼a Source: Dissertations Abstracts International, Volume: 81-04, Section: B. |
| 500 | |
▼a Advisor: Walker, Joan. |
| 502 | 1 |
▼a Thesis (Ph.D.)--University of California, Berkeley, 2019. |
| 506 | |
▼a This item must not be sold to any third party vendors. |
| 520 | |
▼a In this dissertation, I present a body of work that advances our understanding of the technical and economic potential for vehicle grid integration based on a variety of methodological approaches that quantify the opportunity at multiple scales, across multiple geographies, and that cover scenarios with both personally owned plug-in electric vehicles (PEVs) and shared autonomous electric vehicles (SAEVs). The key research questions addressed in this dissertation include:* How can charging infrastructure be cost-effectively deployed to maximize utilization and value to PEV drivers?* How much flexibility exists in the charging demand from PEVs?* What is the economic opportunity to manage the charging of PEVs to occur at lower cost time periods?* How will fleets of electrified autonomous vehicles serving mobility on-demand differ in how they are managed to minimize the cost of charging or to serve as a source of electricity for buildings?These questions are motivated by the fact that transportation electrification and emerging forms of mobility are dramatically changing how the transportation system is planned, operated, and analyzed. PEVs present new challenges and constraints around the siting and operation of refueling infrastructure. Electric load from PEVs can exacerbate grid congestion at either transmission or distribution scales if left unmanaged. Sharing and autonomy are changing mobility which will have unique implications for the grid integration of PEVs.Meanwhile, there are strong social and environmental forces compelling planners, regulators, and private industry to electrify transportation as soon as possible. The transportation sector is the largest emitter of greenhouse gases in the United States. With the exception of the great recession, emissions in the transportation sector have been growing for the last three decades, in contrast to the electric power and industrial sectors which have been on a downward trend in emissions. Transportation, therefore, represents one of the primary challenges to achieving deep decarbonization of the U.S. economy.In the electric power sector, policy and economic forces are upending incumbent generation technologies (coal and natural gas) in favor of lower cost and lower carbon alternatives, particularly wind and solar power. As these intermittent renewable resources increase in capacity, the incidence of renewable energy (RE) curtailment increases due to time periods when supply is greater than demand and generators are turned down or shut off from the level that they would otherwise be producing. Curtailment raises the overall system cost of supplying electricity. In addition, some utilities must meet an energy production standard to satisfy state mandates for renewable production. Renewable curtailment forces utilities to either acquire more RE or introduce sources of grid flexibility to relieve the curtailment. One low cost strategy to mitigate these challenges is to manage the temporal profile of electricity demand to make use of the renewable resources when they are available.PEVs are generally analyzed through modeling using one of two approaches, statistical modeling and activity-based modeling. Statistical models typically summarize or infer travel patterns from travel survey data and use them to characterize the need for PEV charging and the temporal opportunities to charge. The key disadvantage of such approaches is that they cannot account for the individual mobility constraints of travelers and they typically require an assumption that charging infrastructure is unlimited. Another common approach is to develop Markov Chain models of mobility and PEV charging. In these models, transitions between states are treated as random events. Because they lack a representation of the causal mechanism for these transitions, these models are difficult to generalize and their utility is degraded if applied in prospective contexts assuming a transportation system with dramatically different characteristics than present.Activity-based models make use of travel diaries from surveys or GPS data logging which are then provided as input to mobility and charging simulations. Agent-based models are a subset of activity-based models, in so far as they treat travelers individually and require a representation of each individual's activity schedule in order to model the travel necessary to engage in those activities. What distinguishes agent-based models are two key features: 1) wrapping the individuals in a virtual environment (e.g. the transportation system) with detailed representation of transportation supply and 2) dynamically simulating the agents' interactions with the virtual environment and with each other. These interactions open the opportunity to model the choices of the agents based on empirical studies of human behavior as well as to make agent behavior contingent on the time-evolving state of their environment and other agents.In the electric power and grid modeling domain, load from PEVs are typically represented as static or derived from very rudimentary estimation techniques. Studies either ignore flexibility entirely or they make simplistic assumptions. (Abstract shortened by ProQuest). |
| 590 | |
▼a School code: 0028. |
| 650 | 4 |
▼a Transportation. |
| 650 | 4 |
▼a Automotive engineering. |
| 690 | |
▼a 0709 |
| 690 | |
▼a 0540 |
| 710 | 20 |
▼a University of California, Berkeley.
▼b Civil and Environmental Engineering. |
| 773 | 0 |
▼t Dissertations Abstracts International
▼g 81-04B. |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0028 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2019 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T15491455
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 202002
▼f 2020 |
| 990 | |
▼a ***1816162 |
| 991 | |
▼a E-BOOK |