Analyzing The Impacts of the Biogas-to-Electricity Purchase Incentives on Electric Vehicle Deployment with the MA3T Vehicle Choice Model [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 333.78 *Recreational and wilderness areas

Thông tin xuất bản: Oak Ridge, Tenn. : Oak Ridge, Tenn. : Oak Ridge National Laboratory ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2017

Mô tả vật lý: Medium: ED : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 264539

This analysis represents the biogas-to-electricity pathway under the Renewable Fuel Standard (RFS) as a point of purchase incentive and tests the impact of this incentive on EV deployment using a vehicle consumer choice model. The credit value generated under this policy was calculated in a number of scenarios based on electricity use of each power train choice on a yearly basis over the 15 year vehicle lifetime, accounting for the average electric vehicle miles travelled and vehicle efficiency, competition for biogas-derived electricity among electric vehicles (EVs), the RIN equivalence value and the time value of money. The credit value calculation in each of these scenarios is offered upfront as a point of purchase incentive for EVs using the Market Acceptance of Advanced Automotive Technologies (MA3T) vehicle choice model, which tracks sales, fleet size and energy use over time. The majority of the scenarios use a proposed RIN equivalence value, which increases the credit value as a way to explore the analysis space. Additional model runs show the relative impact of the equivalence value on EV deployment. The MA3T model output shows that a consumer incentive accelerates the deployment of EVs for all scenarios relative to the baseline (no policy) case. In the scenario modeled to represent the current biogas-to-electricity generation capacity (15 TWh/year) with a 5.24kWh/RIN equivalence value, the policy leads to an additional 1.4 million plug-in hybrid electric vehicles (PHEVs) and 3.5 million battery electric vehicles (BEVs) in 2025 beyond the no-policy case of 1.3 million PHEVs and 2.1 million BEVs when the full value of the credit is passed on to the consumer. In 2030, this increases to 2.4 million PHEVs and 7.3 million BEVs beyond the baseline. This larger impact on BEVs relative to PHEVs is due in part to the larger credit that BEVs receive in the model based on the greater percentage of electric vehicle miles traveled by BEVs relative to PHEVs. In this scenario 2025 also represents the last year in which biogas-derived electricity is able to fully supply the transportation electricity demand in the model. After 2025, the credit value declines on a per vehicle basis. At the same time a larger fraction of the credit may shift towards biogas producers in order to incent additional biogas production. The expanded 41 TWh/year biogas availability scenarios represent an increase beyond today s generation capacity and allow greater eRIN generation. With a 5.24kWh/RIN equivalence value, when all of the credit is directed towards reducing vehicle purchase prices, the 41 TWh/year biogas scenario results in 4.1 million additional PHEVs and 12.2 million additional BEVs on the road in 2030 beyond the baseline of 2.5 million PHEVs and 6.1 million BEVs. Under this expanded biogas capacity, biogas-derived electricity generation is able to fully supply electricity for a fleet of over 21 million EVs (15.6 million BEVs and 5.8 million PHEVs) on a yearly basis. In addition to assessing the full value credit scenarios described above, multiple scenarios were analyzed to determine the impact if only a fraction of the credit value was passed on to the consumer. In all of these cases, the EV deployment was scaled back as the fraction of the credit that was passed on to the consumer was reduced. These scenarios can be used to estimate the impact if the credit value is reduced in other ways as well, as demonstrated by the scenarios where the current (22.6 kWh/RIN) equivalence value was used. The EV deployment that results from an equivalence value of 22.6 kWh/RIN equivalence value is roughly equivalent to the EV deployment observed in the 25% case using the 5.24 kWh/RIN equivalence value. A higher equivalence value means that a smaller number of credits, and therefore value, is created for each kWh, and therefore the impact on EV deployment is reduced. This analysis shows several of the drivers that will impact eRIN generation and credit value, and tests the impact of an eRIN point of purchase incentive on EV deployment. This additional incentive can accelerate the deployment of EVs when it is used to reduce vehicle purchase prices. However, the ultimate impact of this policy, as modeled here, will be determined by future RIN prices, the extent to which eRIN credit value can be passed on to the consumer as a point of purchase incentive and the equivalence value.
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