Analyzing the impacts of a biogas-to-electricity purchase incentive on electric vehicle deployment with the MA3T vehicle choice model
by Kara Podkaminer, Fei Xie, Zhenhong Lin
In 2014, the EPA approved a biogas-to-electricity pathway under the Renewable Fuel Standard (RFS). However, no specific applications for this pathway have been approved to date (EPA, 2017). This analysis helps understand the impact of the pathway by representing the biogas-to-electricity pathway as a point of purchase incentive and tests the impact of this incentive on EV deployment using a vehicle consumer choice model. To show the potential impact on vehicles sold, the full or partial credit value is modeled as a point of purchase incentive for EVs using the Market Acceptance of Advanced Automotive Technologies (MA3T) vehicle choice model, which tracks annual sales, overall vehicle fleet size and energy use on a yearly basis. The resulting analysis shows several of the drivers that will impact electricity Renewable Identification Number (eRIN) generation and credit value. While these eRINs can accelerate the deployment of EVs when used to reduce vehicle purchase prices, the ultimate impact will be determined by future RIN prices, the extent to which eRIN credit value is passed on to the consumer to reduce purchase price and the equivalence value.
A series of scenarios were constructed to assess the potential impact of credit parameters, including the biogas-derived electricity availability and the electricity equivalence value, which determines the frequency of eRIN generation. In addition, market dynamics can affect how the credit value is split among eRIN supply chain participants. In an efficient market, greater value would likely go towards EV deployment when biogas-derived electricity exceeds electricity demand and to producers when demand for electricity outstrips biogas-derived electricity supply. This behavior was represented in the model though a series of scenarios that altered the percent of credit value that was passed on to the consumer in the form of a purchase incentive.
Today, biogas-derived electricity generation exceeds transportation electricity demand. In the scenario modeled to represent the existing biogas electricity generation (15 TWh/year) with a 5.24 kWh/RIN equivalence value, when the full value of the credit is passed on to the consumer, 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. 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.
This policy could also incent additional biogas-derived electricity production if some of the credit value is shared with biogas electricity producers. A recent study estimated the biogasderived electricity potential at 41 TWh/year. Using that biogas-derived electricity availability to represent an expanded capacity the impacts of greater eRIN generation is modeled. When 75% of the credit is directed towards reducing vehicle purchase prices (reserving 25% of the credit value to bring additional biogas-electricity production online) under the 5.24 kWh/RIN equivalence value, this high biogas scenario results in 2.7 million additional PHEVs and 8.8 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 demand for a fleet of over 20 million EVs (5.2 million PHEVs and 14.9 million BEVs) on a yearly basis. In this optimistic scenario, eRIN generation would xiv constitute at most 8% of the 16 billion gallon cellulosic RFS target in 2022 and 43% in 2030, leaving room for other cellulosic fuels.
In addition to assessing the scenarios described above, multiple scenarios were analyzed examining the impact of the policy if only a fraction of the credit value was passed on to the consumer. In all of these cases, EV deployment is scaled back as that fraction is reduced. Similarly, since a higher equivalence value means that a smaller number of credits are generated for a given amount of electricity, the credit value calculated using the current (22.6 kWh/RIN) equivalence value results in lower EV deployment relative to the proposed (5.24 kWh/RIN) equivalence value.
Overall the impact of the incentive on EV deployment scales with the magnitude of the point of purchase incentive. The greater the value that is created and passed on to the consumer, the greater the acceleration of EV deployment is observed.