Coordinated EV Adoption: Double-Digit Reductions in Emissions and Fuel Use for $40/Vehicle-Year

Abstract

Adoption of electric vehicles (EVs) would affect the costs and sources of electricity and the United States efficiency requirements for conventional vehicles (CVs). We model EV adoption scenarios in each of six regions of the Eastern Interconnection, containing 70% of the United States population. We develop electricity system optimization models at the multidecade, day-ahead, and hour-ahead time scales, incorporating spatial wind energy modeling, endogenous modeling of CV efficiencies, projections for EV efficiencies, and projected CV and EV costs. We find two means to reduce total consumer expenditure (TCE): (i) controlling charge timing and (ii) unlinking the fuel economy regulations for CVs from EVs. Although EVs provide minimal direct GHG reductions, controlled charging provides load flexibility, lowering the cost of renewable electricity. Without EVs, a 33% renewable electricity standard (RES) would cost $193/vehicle-year more than the reference case (10% RES). Combining a 33% RES, EVs with controlled charging and unlinking would reduce combined electric- and vehicle-sector CO2 emissions by 27% and reduce gasoline consumption by 59% for $40/vehicle-year more than the reference case. Coordinating EV adoption with adoption of controlled charging, unlinked fuel economy regulations, and renewable electricity standards would provide low-cost reductions in emissions and fuel usage.

Consequential life cycle air emissions externalities for plug-in electric vehicles in the PJM interconnection

“…The gap between correlation and causality is particularly evident for the dispatch of hydroelectric plants, which may change generation timing in response to new load but typically will not change total energy generated in response to new load. Alternatively, bottom-up normative models of the power system, such as those used by Sioshansi et al [9], Peterson et al [10], Choi et al [11] and Weis et al [12], use optimization models to estimate how a power system should operate to minimize costs subject to a variety of constraints. These models can assess changes of grid operation in response to new PEV load.…”

Effects on Greenhouse Gas Emissions of Introducing Electric Vehicles into an Electricity System with Large Storage Capacity

“…Published studies (e.g., Choi et al 2013;Wang et al 2011;Druitt and Früh 2012) mainly refer to systems in which scheduling the recharging of EVs can help to smooth temporal variations in demand and thereby enable more use of intermittent renewable sources. Sternberg and Bardow (2015) concluded that, if storage is added to an existing grid, the highest GHG reductions are achieved by using surplus power in heat pumps and EVs.…”

“…Published studies (e.g., Choi et al. ; Wang et al. ; Druitt and Früh ) mainly refer to systems in which scheduling the recharging of EVs can help to smooth temporal variations in demand and thereby enable more use of intermittent renewable sources.…”

Industrial ecology: Quantitative methods for exploring a lower carbon future

“…As an example, equation 11 shows the objective function-minimizing the cost of electricity production over time, in this case from Choi et al [31]. Here Ci, Fi, and Vi are the capital costs, fixed operating costs, and variable operating costs of each type of electricity generation i for each year t; r is the discount rate.…”

“…The decision variables are the total capacity (in MW) of each generation technology in each time period, xi,t, the new capacity to be built in each time period, yi,t , the capacity to be retired, qi,t, and the amount of electricity to be generated for electric vehicles, zev i,s,w,h,t , and for all other uses, z i,s,w,h,t . This problem is designed to examine the interactions of wind energy generation and electric vehicles, so both of these activities are modeled separately: electricity generation is represented by zev and z for the electricity used for electric vehicles (EVs) and for all other demand, respectively, and LCwind is the levelized cost of wind energy and zwind is the electricity generated from wind resources at season s and hour h. Further details are given in Choi et al [31].…”