1. Estimating Potential Demand for Electric Vehicles (EVs) Michael K. Hidrue and George R. Parsons Camp Resources XVII Wrightsville Beach, NC June 24-25, 2010 Sponsored by: US Department of Energy, Office of Electricity Delivery and Reliability

2. Outline Objective Study design Estimation results WTP estimates Conclusion 1

3. Estimate potential market demand for EVs Assess the value of adding V2G on demand for EVs V2G vehicles are special type of EVs that allow people to sell power from their batteries back to electric companies. 2 Objectives

4. Study Design Web based choice experiment National Survey, N=3029 Sample resembles national census data Latent class random utility model 3

5. Sample EV Choice Set 4

6. Results: Number of Latent Classes BIC identified two latent classes The EV class has positive EV constant, high value for fuel saving and tend to be green The GV class has negative EV constant, low value for fuel cost saving and tend not to be green 5 EV class GV class

7. Results: Class Membership Model Variable CoefficientT-statOdds ratio Constant -2.9 -12 0.06 Young (25-35yrs old) 0.78 6.2 2.2 Middle age (36 -55 yrs old) 0.26 2.4 1.3 Expected gas price in 5 years 0.07 2.6 1.07 Change in life style & shopping habit : Major 1.157.83.2 Change in life style & shopping habit: Minor 0.715.82.0 Having access for installing charger 1.1410.13.1 Expected next vehicle: Hybrid 1.0610.42.9 Multicar household -0.12-1.20.9 College (>=B.A) 0.060.71.1 Male 0.343.71.4 Four more variables… 6

8. Results: Vehicle Choice Model Attributes Parameters Prob. Weighted Implicit Prices GV classEV class Coef.T-stat.Coef.T-stat. EV constant-4.02-10.00.474.0-$9,337* Yea saying-0.08-0.8-0.27-5.0 Price-1.8E-04-6.0-1.04E-04-25.1 Pr *pr on GV4.1E-070.041.1E-066.9 Fuel cost-0.16-1.4-0.34-9.3-$2,764 7 *=value of yea saying is subtracted from the constants

9. Results: Vehicle Choice Model Continued Attributes Parameters Probability weighted Implicit Prices GV classEV class Coef.T-stat.Coef.T-stat. Driving range (Ref=75 mi) 150 mi0.813.80.447.3$5,177 200 mi0.894.30.8414.6$8,134 300 mi1.346.41.1417.4$11,391 Charging time for 50 mi (Ref=10 hrs) 5 hours0.572.70.091.6 $2,136 1 hour1.045.30.458.3 $5,858 10 minutes1.316.80.7413.6 $8,567 8

10. Results: Vehicle Choice Model Continued Attributes Parameters Probability weighted Implicit Prices GV classEV class Coef.T-stat.Coef.T-stat. Acceleration relative to respondent’s next car (Ref=20% slower) 5% slower0.582.80.061.04 $1,957 5% faster0.924.20.284.7 $4,372 20% faster1.165.30.508.2 $6,521 Pollution relative to respondent’s next car (Ref=25% lower) 50% lower0.110.520.050.84 $636 75% lower0.331.90.081.3 $1,428 95% lower0.532.80.284.8 $3,333 9

11. Top 10% WTP Estimates 10 Assumptions: Fuel cost=$1.00/gal equivalent Acceleration=5% slower Pollution=75% lower

12. Comparing WTP Estimates with Battery Cost Estimates 11

13. Comparing WTP and Battery Cost Estimates 12

14. Comparing WTP and Battery Cost Estimates in the Presence of Subsidy 13

15. Conclusion Driving range, charging time and performance are significant drivers of EV choice Green life style, hybrid buyer, outlet access, expected gas price and age are significant predictors of EV choice Multicar household, college education and regions are not significant predictors of EV choice People will pay premium for some EV designs For EVs to compete on the market, battery cost has to decline substantially. 14