The research presented in this thesis examines the use of electric vehicles and renewable energy to reduce emissions of CO₂, SO₂ and NO[subscript x], and within the state of Texas. The analysis examines the impact of increased renewable energy output and electric vehicle charging on the emissions of fossil fuel electric generators used to serve the bulk power system within Texas. The analysis then compares those impacts to alternative scenarios in which fossil fuel generation replaces some renewable energy generation, and Internal Combustion Engine (ICE) vehicles of varying efficiency are used instead of electric vehicles. This research uses temporally-resolved regression analysis combined with a unit commitment and dispatch model that incorporates several different scenarios for EV charging and fuel mixes to evaluate emissions outcomes based on a variety of conditions. Hourly historical generation and emission data for each fossil fuel generator, combined with hourly output data for non-fossil fuel units aggregated by fuel type (i.e. nuclear, wind, hydro-electric) within the Electric Reliability Council of Texas (ERCOT) footprint is regressed to assess the impact of wind generation output on fossil-fuel generation emissions. The regression analysis is used to assess potential increases in emissions resulting from the ramping of fossil-fuel Electric Generation Units (EGUs) to compensate for variability in wind generation output due to changing weather conditions. The unit commitment dispatch model is used to evaluate the impact of changes in customer demand due to increased usage and charging of electric vehicles on the ERCOT system and any resulting increase in emissions from generation used to meet this new demand. The model uses detailed cost, performance and emissions data for EGUs in the ERCOT footprint to simulate the impact of a variety of charging scenarios and fuel mixes on EGU dispatch patterns and any resulting change in system-wide emissions. The results of this model are combined with the results of the regression analysis to present a more complete analysis of the combined impacts of increase EV and renewable energy usage on the emissions of CO₂, SO₂ and NO[subscript x] within the ERCOT footprint. Based on these analyses the increases in renewable energy generation demonstrate clear benefits in terms of emission reductions when the impacts of increased emissions due to more frequent ramping of fossil-fuel units are taken into account. This analysis also finds that EV charging generally has emissions benefits across a range of charging patterns and bulk power system fuel mixes, although in certain circumstances EV charging might result in higher emissions than the use of ICE vehicles. This research finds when future ICE vehicles with reduced emissions are taken into account, approximately half of the modeled scenarios show net emissions benefits from EV charging, while half show net emissions costs when emissions impacts across pollutants are taken into account. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/23624 |
| Date | 24 March 2014 |
| Creators | Meehan, Colin Markey |
| Source Sets | University of Texas |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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