An Assessment of Electric Drive Vehicle Deployment Through Mid- Century

Transcription

1 An Assessment of Electric Drive Vehicle Deployment Through Mid- Century ETSAP Workshop Paris, France 17 June 213 Joe DeCarolis, Samaneh Babaee Dept of Civil, Construction, and Environmental Engineering NC State University 1 Background and Motivation Electric Drive Vehicles (EDVs): Vehicles that derive a portion of their motive power from a battery hybrids, plug-in hybrids, and battery electric vehicles 1. Increasing concerns regarding U.S. oil imports, anthropogenic climate change, and urban air quality 2. Current U.S. policies, tax credits, and incentives promote electric drive vehicles (EDVs) 3. High uncertainty about the future market penetration of EDVs and the consequent effect on emissions despite the potential benefits over competing vehicles Electric 2 1

2 Research Goals Use the TIMES model generator with a VEDA-compatible US dataset to address the following: 1. Assess the circumstances under which EDVs achieve high levels of market penetration 2. Quantify the impact of wide-scale EDV deployment on electric sector planning and system-wide air pollutant emissions 3. Evaluate how different recharging patterns affect fuel consumption and air pollutant emissions 3 National US TIMES Database (NUSTD) Model time periods: (5-year periods) 12 time slices: summer/winter/intermediate; night/am/afternoon/pm Electricity Detailed electric sector; electricity prices determined endogenously Electric Fixed demands in all end-use sectors Commercial Residential Fuel share constraints Fuel Supply Exogenous, periodspecific fuel prices Fuels NUSTD is publicly available at Industrial Transport Detailed transport sector 4 2

3 NUSTD: Fuel Supply Exogenously specified prices based on unlimited supply (i.e., no supply curves) Prices drawn from the EIA s Annual Energy Outlook (AEO) NUSTD: Electric 32 electric generation technologies 71 emission retrofit technologies to capture CO 2, NO X, and SO 2 emissions Wind and solar availability factors indexed by time slice The price of electricity is determined endogenously Data based on US EPA (Lennox et al. 212) with updates based on the AEO 5 NUSTD: Transportation Transportation sector: light duty vehicles, heavy duty vehicles, and off-highway technologies Light Duty Transportation : 7 vehicle size classes (Mini-compact, Compact, Full, Minivan, Pick-up, Small SUV, Large SUV) 6 fuels types (E1, E85X, Diesel, Electric, CNG, H 2 ) 13 vehicle types Vehicles cost and performance data is from US EPA; updated based on the AEO The total demand for vehicle miles is drawn from AEO EDVs: hybrid, plug-in hybrid (PHEV2 and PHEV6), and electric (EV16) EDV performance data is based on the GREET model Hurdle rates of 7.8% (hybrid, plug-in hybrid, and diesel vehicles) and 1% (electric, CNG, and hydrogen fuel cell vehicles) 6 3

4 Fuel Share (%) Total Energy Demand (PJ) NUSTD: End-Use Demand s Commercial, industrial, and residential sectors are comprised of: Single aggregate energy demand (AEO) Fuel share constraints (AEO) Emission factors associated with in-sector fossil fuel combustion (EPA, AEO) 1% 14 9% 1 8% 7% 1 6% Electricity 8 5% 4% 6 3% 4 2% LPG Natural Gas 1% Distillate % Commercial sector Baseline Assumptions In the electric sector: Constraints on SO 2 and NO x emissions based on AEO, Mercury and Air Toxics Standards (MATS), and the Cross-State Air Pollution Rule (CSAPR) The overall minimum share of renewable energy for all states (including state-level RPSs): 2% in 21 and 13% by 225. In the transportation sector: New CAFE standard and the corresponding GHG emissions rate limit for LDVs: (54.5 mpg and 163 grams CO 2 per mile in model year 225) Renewable fuel requirements based on the Energy Independence and Security Act of 27 The effect of existing fuel subsidies and tax credits for new vehicles is included in the baseline cost assumptions (AEO). 8 4

5 Electric Capacity (GW) LDV Distance Traveled (1 9 km) Electric Capacity (GW) LDV Distance Traveled (1 9 km) Scenario Description Scenario development is focused on five factors likely to affect the cost-effectiveness of EDVs relative to other vehicle technologies: Natural gas price Crude oil price EDV battery cost A federal cap on CO 2 emissions A federal renewable portfolio standard (RPS) Examined all possible combinations of factor assumptions, resulting in 72 scenarios Factor Assumption 1 Assumption 2 Assumption 3 Natural gas price Reference Low High Crude oil price Reference Low High Federal CO 2 cap No Yes Federal RPS No Yes Battery development Reference Optimistic TIMES+NUSTD (Bottom-Up Energy System Model) Scenarios Fuel Prices Battery Cost CO 2 Policy, RPS Tech Deployment Emissions 9 Technology Deployment in Two Extreme Scenarios Lowest EDV deployment (low oil, base NG & battery, no RPS or CO 2 ) Highest EDV deployment (high oil, low NG & battery, RPS & CO 2 ) Existing coal steam Existing hydro Existing natural gas Existing nuclear Existing wind New coal steam New geothermal New natural gas New solar New nuclear New wind Year GSL E85X EV1 Diesel 14 Existing coal Existing hydro 12 Existing natural gas Existing nuclear Existing wind New coal IGCC-CCS New geothermal New natural gas 1 New nuclear New wind Year 7 GSL E85X Diesel Diesel Hybrid 6 GSL-PHEV2 GSL-PHEV6 EV Year Year 1 5

6 25 Emissions (SO 2, NO X in ktonnes; CO 2 in Mtonnes) 25 Distance Traveled by EDVs (billion km) Effect of Scenario Drivers on EDV Deployment NG-Low NG-Ref NG-High Oil-Low Oil-Ref Oil-High RPS-Y RPS-N CO2-Y CO2-N Batt-Y Batt-N Scenario Parameters 11 Effect of EDV Deployment on Emissions 1 Larger bubbles indicate higher oil price Scenarios in the dashed boxes include the CO 2 policy CO 2 (Battery-Low) CO 2 (Battery-High) SO 2 (Battery-Low) SO 2 (Battery-High) NO X (Battery-Low) NO X (Battery-High) Distance Traveled by Electric Drive Vehicles (billion km) 12 6

7 Modeling Insights High oil prices, the CO 2 policy, and low EDV battery costs are the strongest drivers of EDV deployment. The model consistently chooses to deploy PHEV6s and EV16s over other EDVs (contingent on battery cost reductions). EDV deployment produces only a small effect on system-wide CO 2, SO 2, and NO X emissions. The strongest driver of lower emissions overall is the CO 2 policy. 13 Next Steps Investigate the use of behavioral hurdle rates; update and resubmit current analysis for publication Determine the effect of charging time on EDV deployment and emissions Conduct a broader sensitivity analysis to determine the effects of key input parameters on outputs of interest 14 7

8 Acknowledgment This material is based upon work supported by the National Science Foundation under Grant No Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. 15 Questions and Comments? 16 8