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Estimating Potential Demand for Electric Vehicles (EVs)

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 . Outline. Objective Study design

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Estimating Potential Demand for Electric Vehicles (EVs)

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  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

  3. Objectives • 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.

  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

  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 EV class GV class

  7. Results: Class Membership Model

  8. Results: Vehicle Choice Model *=value of yea saying is subtracted from the constants

  9. Results: Vehicle Choice Model Continued

  10. Results: Vehicle Choice Model Continued

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

  12. Comparing WTP Estimates with Battery Cost Estimates

  13. Comparing WTP and Battery Cost Estimates

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

  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.

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