Evaluating land use impacts of self-driving vehicles and leveraging intelligently charged electrified transit to support a renewable energy grid in the Austin, Texas region

dc.contributor.advisorKockelman, Kara
dc.creatorWellik, Tyler Katherine
dc.date.accessioned2021-05-19T18:45:20Z
dc.date.available2021-05-19T18:45:20Z
dc.date.created2019-12
dc.date.issued2019-12-10
dc.date.submittedDecember 2019
dc.date.updated2021-05-19T18:45:21Z
dc.description.abstractThis thesis is divided into two parts. The first part focuses heavily on the land use model SILO and its implementation in the Austin, Texas six-county region over a 27-year period of full adoption of self-driving vehicles. It discusses the model framework and capabilities and critically evaluates SILO’s specifications. SILO was then integrated with the agent-based transportation model MATSim for the Austin region. Land use and travel results were generated for a business-as-usual case (BAU) of 0% self-driving or “autonomous” vehicles (AVs) over the model timeframe versus a scenario where households’ value of travel time savings (VTTS) is reduced by 50%, to reflect the travel-burden reductions of no longer having to drive. A third scenario is also compared and examined against BAU to understand the impacts of rising vehicle occupancy (VO), and/or higher roadway capacities, due to dynamic ride-sharing (DRS) options in shared AV (SAV) fleets. Results suggest an 8.1% increase in average commute times when VTTS falls by 50% and VO remains unaffected (the 100% AV scenario), and a 33.3% increase in the number of households with “extreme commutes” (over 1 hour, each way) in the final model year (versus BAU of 0% AVs). When VO is raised to 2.0 and VTTS falls instead by 25% (the “Hi-DRS” SAV scenario), average commute times increase by 3.5% and the number of households with “extreme commutes” increase by 16.4% in the final model year (versus BAU of 0% AVs). The ITLUM also predicts 5.3% fewer households and 19.1% more available, developable land in the City of Austin in the 100% AV scenario in the final model year relative to the BAU scenario’s final year, with 5.6% more households and 10.2% less developable land outside the City. In addition, the model results predict 5.6% fewer households and 62.9% more available developable land in the City of Austin in the Hi-DRS SAV scenario in the final model year relative to the BAU scenario’s final year, with 6.2% more households and 9.9% less developable land outside the City. This thesis’ second part looks at how electric buses can support a power grid that relies heavily on renewable energy sources, like wind and solar. The transportation sector is a major greenhouse gas (GHG) emitter. Concurrent electrification of vehicles and investment in renewable energy is required to deeply decarbonize this and other sectors of our economies. The introduction of intermittent renewable energy sources, like solar and wind, at a large scale presents major challenges to grid operators and utility companies. This study examines the benefits and costs that a Vehicle-to-Grid (V2G) Battery Electric Bus (BEB) fleet offers Austin, Texas by buffering against sharp shifts in renewable energy production to help smooth power demands from traditional energy sources (like coal, natural gas, and nuclear power plants). A V2G BEB “smart charging” (SC) scenario’s cost and emissions were compared to those in a BEB “charge-as-needed” scenario and to those in a diesel bus scenario, for 423+ buses and over 88,000 bus-miles per day. By simply electrifying Austin’s buses, without any SC strategies, the total external cost of all of Austin’s electricity grid emissions and bus emissions falls by approximately 3.42%, amounting to over 21¢-savings per bus-mile, relative to the diesel-bus scenario. By using SC strategies, those same emission costs fell by 5.64% or over 35¢-savings per bus-mile. These emissions savings become very significant when summed over the course of a year. In the non-SC BEB scenario, emissions savings amount to approximately $6.86M/year, and in the SC BEB scenario, emissions savings reach approximately $11.3M/year. Such reductions are thanks to high renewable energy use in Austin’s power mix and because diesel fuel is much more emitting (per kWh) than power plants. From the transit operator’s perspective, a BEB fleet costs more than a diesel bus fleet, but such costs can be more than offset by renewable energy savings and emissions-costs benefits. Thanks to SC strategies, the utility manager is estimated to save 22% of their daily power-purchase cost in this case study.
dc.description.departmentCivil, Architectural, and Environmental Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/85760
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/12711
dc.language.isoen
dc.subjectAutonomous vehicles
dc.subjectShared autonomous vehicles
dc.subjectLand use modeling
dc.subjectIntegrated transportation and land use modeling
dc.subjectBattery electric bus
dc.subjectSmart charging
dc.subjectVehicle-to-Grid
dc.subjectElectric vehicle
dc.subjectRenewable energy
dc.titleEvaluating land use impacts of self-driving vehicles and leveraging intelligently charged electrified transit to support a renewable energy grid in the Austin, Texas region
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentCivil, Architectural, and Environmental Engineering
thesis.degree.disciplineCivil Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering

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