EV sales in US reaching ~10% of sales

Source: Argonne National Lab, www.anl.gov/ev-facts/model-sales

Introduction

  • Unmanaged BEV charging is becoming a problem to the grid.
  • Managed charging is cheaper and smoothes out the grid load.
  • Smart charging: Supplier-Managed Charging (SMC) and Vehicle-to-Grid (V2G).

SMC - Supplier Managed Charging

  • SMC smooths out overnight EV charging demand.
  • Electricity demand is controlled below capacity threshold.
  • It saves money and reduces pollution.

SMC - Supplier Managed Charging

  • SMC smooths out overnight EV charging demand.
  • Electricity demand is controlled below capacity threshold.
  • It saves money and reduces pollution.

V2G - Vehicle-to-Grid

Smart charging depends on enrollment.

Literature Review

  1. A study by Wong et al. (2023) examined incentives affect the EV owners’ acceptance, but EV ownership is only 19%.
  2. A study by Philip and Whitehead (2024) found range anxiety matters, but EV ownership is only 1.28%.
  3. Another study by Huang et al. (2021) indicates the importance of fast charging, but the sample size is only 157.

None of them have demographics data to study heterogeneity.

We need high EV ownership & large sample size, and consider heterogeneity.

Research Questions

  1. Sensitivity: How do changes in smart charging program features influence BEV owners’ willingness to opt in?

  2. Enrollment Rate: Under what combinations of features will BEV owners be more willing to opt in to smart charging programs?


Conjoint survey to collect BEV owners’ willingness.

Multinomial logit model for utility simulations.

Survey Design with formr



Conjoint Questions

  1. Monetary Incentives
  2. Charging Limitations
  3. Flexibility

Demographic Questions

  1. BEV Ownership
  2. Personal Info
  3. Household Info

Conjoint Question Explained

A Sample Conjoint Question

  1. Provide respondents with different sets of attributes.
  2. Observe choices across random sets.
  3. Estimate utility of each attribute.

SMC Programs

Attributes

No. Attributes Range
1 Enrollment Cash $50 to $300
2 Monthly Cash $2 to $20
3 Monthly Override 0 to 5
4 Min Battery 20% to 40%
5 Guaranteed Battery 60% to 80%

Sample Program

Attributes Values
Enrollment Cash $300
Monthly Cash $20
Monthly Override 5

(Range determined by stated vehicle they own)

V2G Programs

Attributes

No. Attributes Range
1 Enrollment Cash $50 to $300
2 Occurrence Cash $2 to $20
3 Monthly Occurrence 1 to 4
4 Lower Bound 20% to 40%
5 Guaranteed Battery 60% to 80%

Sample Program

Attributes Values
Enrollment Cash $300
Occurrence Cash $20
Monthly Occurrence 1

(Range determined by stated vehicle they own)

Sample SMC Question

Sample V2G Question

Survey Fielding - 1356 in Total

Meta Ads: Voluntary participants

  • 803 responses
  • March to July in 2024

Dynata Recruitment: Paid survey

  • 553 responses
  • September to November in 2024

Survey Question - Car Ownership

Survey Results - Top 10 BEV

Survey Results - Demographics

Survey Results - Willingness to Participate

Multinomial Logit Models

\[ \begin{align*} u_j = v_j + \epsilon_j = \beta' x + \epsilon_j \qquad P_j = \frac{e^{v_j}}{\sum_{k=1}^{J} e^{v_k}} \end{align*} \]

Utility esimated using maximum likelihood estimation (MLE).

SMC Estimates

V2G Estimates

Without compensation, users will not participate.

Enrollment Sensitivity

Baseline Simulation

Choice between “None” and this program:

Attributes Values
Enrollment Cash $0 - $1000
Monthly Cash $2
Monthly Override 1

Sensitivity Plot

Enrollment Sensitivity

  1. Steeper slope indicates higher sensitivity.
  2. Diminishing returns exist.

Equivalencies of 5% Enrollment Increase


SMC

Attribute Equivalence Value Unit
Enrollment Cash 64.7 $
Monthly Cash 3.2 $
Override Days 2.0 Days
Minimum Threshold 54.8 %
Guaranteed Threshold 5.5 %

V2G

Attribute Equivalence Value Unit
Enrollment Cash 45.0 $
Occurrence Cash 2.3 $
Monthly Occurrence 1.5 Times
Lower Bound 8.5 %
Guaranteed Threshold 7.2 %


  1. Smaller value indicates higher efficiency.
  2. Monetary incentives are valued more in V2G than SMC.
  3. Guaranteed threshold is more important in SMC than V2G, indicating range anxiety.
  4. Attribute equivalencies can be used to inform incentive design.

SMC Scenario Analysis

  1. Flexibility is highly valued.
  2. Recurring incentives are more important than one-time.
  3. Payment alone is not enough.

V2G Scenario Analysis

  1. Still, recurring incentives are more important than one-time.
  2. But flexibility is not as important compared with SMC.
  3. Owners are willing to leverage BEV as a source of income.

Smart Charging Enrollment Simulator

Contributions


  1. First large N study of BEV owners’ preferences for smart charging programs.
  2. Quantified the sensitivity of BEV owners’ preferences for smart charging features.
  3. Introduced the concept of attribute equivalencies to inform incentive design.

Appendix - SMC Logit Model

\[ \begin{align*} u_j = \beta_1 x_j^{\text{enroll_cash}} + \beta_2 x_j^{\text{monthly_cash}} + \beta_3 \delta_j^{\text{override_allowed}} + \beta_4 x_j^{\text{num_overrides}} \notag \\ + \beta_5 x_j^{\text{min_threshold}} + \beta_6 x_j^{\text{guaranteed_threshold}} + \beta_7 \delta_j^{\text{no_choice}} + \epsilon_j \end{align*} \]

Attribute Coef. Est. SE Level Unit
Enrollment Cash β₁ 0.0031 0.0002 50, 100, 200, 300 USD
Monthly Cash β₂ 0.0623 0.0027 2, 5, 10, 15, 20 USD
Override Days β₃ 0.1010 0.0118 0, 1, 3, 5 Days
Override Flag β₄ 0.3622 0.0538 Yes, No -
Minimum Threshold β₅ 0.0037 0.0021 20, 30, 40 %
Guaranteed Threshold β₆ 0.0362 0.0021 60, 70, 80 %
No Choice β₇ 3.0026 0.1779 - -

Appendix - V2G Logit Model

\[ \begin{align*} u_j = \beta_1 x_j^{\text{enroll_cash}} + \beta_2 x_j^{\text{occur_cash}} + \beta_3 x_j^{\text{num_occurrences}} + \beta_4 x_j^{\text{lower_threshold}} \notag \\ + \beta_5 x_j^{\text{guaranteed_threshold}} + \beta_6 \delta_j^{\text{no_choice}} + \epsilon_j \end{align*} \]

Attribute Coef. Est. SE Level Unit
Enrollment Cash β₁ 0.0045 0.0026 50, 100, 200, 300 USD
Occurrence Cash β₂ 0.0863 0.0040 2, 5, 10, 15, 20 USD
Monthly Occurrence β₃ 0.1305 0.0217 1, 2, 3, 4 Times
Lower Threshold β₄ 0.0237 0.0030 20, 30, 40 %
Guaranteed Threshold β₅ 0.0278 0.0030 60, 70, 80 %
No Choice β₆ 2.8759 0.2647 - -

Reference List

Huang, Bing, Aart Gerard Meijssen, Jan Anne Annema, and Zofia Lukszo. 2021. “Are Electric Vehicle Drivers Willing to Participate in Vehicle-to-Grid Contracts? A Context-Dependent Stated Choice Experiment.” Energy Policy 156 (September): 112410. https://doi.org/10.1016/j.enpol.2021.112410.
Philip, Thara, and Jake Whitehead. 2024. “Consumer Preferences Towards Electric Vehicle Smart Charging Program Attributes: A Stated Preference Study.” Rochester, NY. https://doi.org/10.2139/ssrn.4812923.
Wong, Stephen D., Susan A. Shaheen, Elliot Martin, and Robert Uyeki. 2023. “Do Incentives Make a Difference? Understanding Smart Charging Program Adoption for Electric Vehicles.” Transportation Research Part C: Emerging Technologies 151 (June): 104123. https://doi.org/10.1016/j.trc.2023.104123.