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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 l.jpg

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 l.jpg

Outline

  • Objective

  • Study design

  • Estimation results

  • WTP estimates

  • Conclusion


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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.


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Study Design

  • Web based choice experiment

  • National Survey, N=3029

  • Sample resembles national census data

  • Latent class random utility model

3


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Sample EV Choice Set


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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


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Results: Class Membership Model


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Results: Vehicle Choice Model

*=value of yea saying is subtracted from the constants


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Results: Vehicle Choice Model Continued


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Results: Vehicle Choice Model Continued


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Top 10% WTP Estimates

Assumptions:

Fuel cost=$1.00/gal equivalent

Acceleration=5% slower

Pollution=75% lower


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Comparing WTP Estimates with Battery Cost Estimates


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Comparing WTP and Battery Cost Estimates


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Comparing WTP and Battery Cost Estimates in the Presence of Subsidy


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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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