Scott Peterson — 2009-10 Fellow
This research will explore possible revenue streams from using onboard energy storage for grid support. Investigate the effect of widespread PHEV adoption on the grid by estimating changes in dispatch curves in response to PHEV use. Calculate the average energy storage capacity throughout the year, and estimate the net social welfare of lowering peak grid demand and the effect of vehicle charging and discharging on CO2 emissions. Integrating the expertise of CEIC, MSE and EPP provides the multidisciplinary support group required to study this problem
Approach and Methodology
Data are currently being collected on battery degradation in the lab. These data will provide the basis for estimating degradation costs associated with using PHEVs to provide grid services. In turn, we will use that cost combined with locational marginal pricing data from PJM, ISO-NE, and NYISO to calculate the net revenue that a vehicle owner could receive from energy sales. We also will examine how additional incentives for energy arbitrage use could be created to incorporate some of the net-social welfare benefits described by Walawalkar et ali.
While widespread PHEV adoption may enable the U.S. to meet aggressive RPS goals proposed as one means to mitigate climate change, realization of this potential requires attention to systemic complexity. It will, for example, increase the load at certain times of the day. If PHEVs are charged during low demand periods they will necessitate smaller changes in grid infrastructure. The methodology described by Lemoline et al will be used to make predictions about the grid effects in PJM, ISONE and NYISOii.
Battery vehicles or PHEVs can decrease electric power costs because charging in the night from efficient baseload plants would displace low efficiency and expensive gas and oil plants during higher demand hours. The degradation cost associated with this energy arbitrage will need to be accounted for in the difference in prices. We will use a methodology similar to that described by Newcomer et al to estimate this effectiii. We will then use this information to determine likely changes in CO2 emissions.
Finally the average energy storage capacity throughout the year will be calculated. The National Household Transportation Survey (NHTS) contains the distance and frequency of trips along with notations that describe necessary details of the trips to determine how far a vehicle is driven in a day. We will utilize this information will be utilized to determine the total energy used for driving needs. When this is combined with hypothetical PHEV fleet characteristics the total energy storage capacity can be calculated as a function of PHEV adoption and battery size distribution.
i Rahul Walawalkar et al., "An economic welfare analysis of demand response in the PJM electricity market," Energy Policy 36, no. 10 (2008): 3692-3702.
ii D M Lemolne, D M Kammen, and Alex E Farrell, "An innovation and policy agenda for commercially competitive plug-in hybrid electric vehicles," Environmental Research Letters, no. 3 (2008), http://www.iop.org/EJ/abstract/1748-9326/3/1/014003/.
iii Adam Newcomer et al., "Short Run Effects of a Price on Carbon Dioxide Emissions from U.S. Electric Generators," Environmental Science & Technology 42, no. 9 (2008): 3139-3144.