Opinions in V1G

Americans’ Opinions and Interests in Plug-In Electric Vehicle Smart Charging Programs

Summary

Smart charging is beneficial to power companies to shift charging to off-peak hours in order to align charging with renewable energy. Dean and Kockelman (2024) have conducted an internet-based survey of 1,000 American participants on their opinions toward smart charging programs. The team proceeded with multinomial and ordered logit models (since paper is under review, the OL models are now changed to Tobit models) to reveal the participants who mostly support the smart charging program, and the minimum required enrollment and annual incentives.

Matt’s Survey

Charging Programs

1. SMC (Supplier Managed Charging) night only
2. SMC daily optimization
3. SMC grid strain only
4. Maybe - It means the user needs more information before considering SMC
5. UMC - User Managed Charging
6. Unmanaged - Regular charging
7. Unsure/Other

Results

1. Result of charging programs, see Figure 3:
   1. SMC night only, 17.5%
   2. SMC daily optimization, 8.9%
   3. SMC grid strain only, 10.6%
   4. Maybe, 25.4%
   5. UMC, 8.7%
   6. Unmanaged charging, 25.7%
   7. Unsure/Other, 3.2%
2. Result of logit models:
   1. MNL models (Table 2) reveal that BEV owners and wholesale energy price users are more willing to adopt SMC programs. The other type of power bill is time-varying.
   2. Ordered logit models (Tables 4 & 5) reveal the values of one-time and annual incentive. Mean value of one-time incentive ranges from $95.41 to $125.13. Mean value of annual incentive ranges from $12.70 to $14.15.
3. Automatically enroll participants and then ask for WTP to leave (Table 6):
   1. Those who value more on the SMC program will have high WTP.
   2. Those who believe it’s their right to charge freely will have low WTP.

Matt’s Study vs Our Study

1. Overall: Matt’s study seems to have used single answer survey questions, with multinomial and ordered logit models, both of which rely on fixed parameters (aka point estimate). In our version, we have conjoint survey analyzed by multinomial and mixed logit models. We surely have a more complex method, but it’s critical to take good care of the randomness.
2. Matt’s Study:
   1. Better smart charging and unmanaged charging faceting, where SMC are divided into 3 different types.
   2. WTP definition makes more sense (WTP to leave).
   3. More detailed participant faceting than ours.
   4. MNL and ordered logit models with Welch’s T-test.
3. Our Study:
   1. Conjoint survey containing both incentives and operations.
   2. Both MNL and MXL models.

Gaps

1. In the MNL of preferred smart charging program (Table 2), there is a question named “Gini Index”. I want to know what the full question is. I am curious on it since it gives a high coefficient of 2.71.
2. For the incentives, there is a overall mean value table (see Table 3). However, the logit models only show the coefficients of different conditions, but they do not merge to a traceable decision: how much should we set the one-time and annual incentive to our participants?
3. For monthly WTP to leave the SMC programs (see Table 6), the WTP values have 2 sources of unsuitability: one, the WTP values are “claimed” by the participants; two, the altruistic and pro-freedom categories are personas instead of real condition.
4. Survey questions are in single answer format, which means people are free to choose their most desired options. For example, I can at the same time value the SMC program very much, but give a low WTP. This distortion can be hedged by conjoint questions.

Tables and Charts

Figure 1: Perceptions on smart charging and green transportation

Figure 1: Perceptions on smart charging and green transportation

Figure 2: Perceived importance of smart charging benefits

Figure 2: Perceived importance of smart charging benefits

Figure 3: Preferred smart charging option

Figure 3: Preferred smart charging option

Figure 4: Preferred smart charging option by vehicle ownership

Figure 4: Preferred smart charging option by vehicle ownership

Figure 5: Predicted shifts of BEV owners and wholesale energy price users (these 2 groups are most supportive to smart charging)

Figure 5: Predicted shifts of BEV owners and wholesale energy price users (these 2 groups are most supportive to smart charging)

Table 1: Summary statistics

Table 1: Summary statistics

Table 2: Preferred smart charging program (MNL)

Table 2: Preferred smart charging program (MNL)

Table 3: Preferred one-time and annual incentives for SMC (Mean and StDev)

Table 3: Preferred one-time and annual incentives for SMC (Mean and StDev)

Table 4: Preferred one-time incentives for SMC (Ordered Logit)

Table 4: Preferred one-time incentives for SMC (Ordered Logit)

Table 5: Preferred annual incentives for SMC (Ordered Logit)

Table 5: Preferred annual incentives for SMC (Ordered Logit)

Table 6: Monthly WTP to leave auto-enrolled SMC program

Table 6: Monthly WTP to leave auto-enrolled SMC program

Literature Review

Table 7: Literature Review

Comments from Pingfan: The study of Tarroja and Hittinger (2021) was also referred in this study.

References

Dean, Matthew D., and Kara M. Kockelman. 2024. “Americans’ Opinions and Interests in Plug-in Electric Vehicle Smart Charging Programs.” Transportation Research Part D: Transport and Environment 129 (April): 104129. https://doi.org/10.1016/j.trd.2024.104129.

Tarroja, Brian, and Eric Hittinger. 2021. “The Value of Consumer Acceptance of Controlled Electric Vehicle Charging in a Decarbonizing Grid: The Case of California.” Energy 229 (August): 120691. https://doi.org/10.1016/j.energy.2021.120691.