EV Charging Network Expansion Network Quality Signaling

Last registered on January 22, 2025

Pre-Trial

Trial Information

General Information

Title
EV Charging Network Expansion Network Quality Signaling
RCT ID
AEARCTR-0015225
Initial registration date
January 20, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
January 22, 2025, 8:50 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Affiliation
University of California San Diego, Deep Decarbonization Initiative

Other Primary Investigator(s)

PI Affiliation
U Melbourne

Additional Trial Information

Status
In development
Start date
2025-01-01
End date
2025-03-02
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
In 2024 UCSD expanded their network of electric vehicle (EV) charging representing a net addition of 160 EV charging ports (a 49% increase). The goal of the expansion was to increase workplace charging. Our our data shows that up to 60% of the drivers in our club reduced or eliminated charging on campus when comparing charging in the spring to the fall of 2024. One of the primary mechanism driving this sustained shift away from workplace charging may have been a temporary decline in the reliability of new chargers. Notably these drivers show reduced charging even after the reliability issues were resolved. The UC San Diego Transportation Department independently concluded that quality disruptions during the expansion were a major concern, and our experiment utilizes a $10 credit issued to drivers by the department. Our current experiment is designed to test the effectiveness of both informational and financial signalling of to increase workplace charging among drivers. We will provide half of the affected drivers with information about improvements in the quality of the network for three weeks. Following this we will provide half of the control and half of the information drivers with the $10 incentive from the transportation department. We interpret this $10 credit as a potentially costly signal of quality by the transportation department.
External Link(s)

Registration Citation

Citation
Tebbe, Sebastian and Charlie Thompson. 2025. "EV Charging Network Expansion Network Quality Signaling." AEA RCT Registry. January 22. https://doi.org/10.1257/rct.15225-1.0
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Experimental Details

Interventions

Intervention(s)
The first intervention is providing information about the improved quality of campus charging, this includes information about lower glitch rates, higher availability due to an increased number of chargers, and faster charging via higher throughput.

The second intervention leverages a payment from the transportation department to affected drivers of a one time $10 credit. While this amount is relatively low, we believe it represents a costly signal by transportation to drivers about the quality of the network, convincing some to return to charging on campus.
Intervention Start Date
2025-01-21
Intervention End Date
2025-03-02

Primary Outcomes

Primary Outcomes (end points)
We have two primary outcomes. First is total charging, which is measured as the kWh per driver per week. The second is the number of sessions per driver per week. We also examine the ratio of total charging to the past calendar year's average, and the ratio of the number of sessions per driver per week to the average number of sessions per week per driver in the previous year.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
The same variables (kwh, and number of sessions as well as the ratio relative to past averages) but for each of the groups of charging behavior listed below.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experiment represents an attempt to shift the behavior of EV drivers towards charging on campus. The drivers in our study previous all charged on campus between 1/1/2024 and 12/31/2024, with many charging in 2023 as well. During 2024 the UCSD campus expanded their network of electric vehicle (EV) charging representing a net addition of 160 EV charging ports (a 49% increase). The goal of the expansion was to increase workplace charging by increasing the number and quality of chargers available to drivers. However some lots expirenced several weeks without access to chargers as old chargers were removed and new chargers installed. Further our data also shows that there was also an increase in the number of failed charging sessions initiated during the expansion due to hardware issues with the new chargers. A major concern is that the disruptions may have switched drivers habits to higher carbon intensity at home charging. Indeed, our initial data indicated that drivers who charged during the expansion actually reduced the frequency and energy draw of their on campus charging in the academic quarter following the expansion. We found this decline persisted among affected drivers even after the reliability issues were resolved.

Independently the UC San Diego Transportation Department concluded that disruptions caused by the network expansion were a major concern and wished to signal to drivers that the network quality has since improved. To do this they planned to issue a $10 credit as an incentive to have drivers try the network once more. We worked with the transportation department to randomize the timing of the receipt of this financial incentive across affected drivers. The goal of this expirment is to test if information and costly incentive signaling can cause drivers to return their habits to on-campus driving once more.

Our experiment covers a six week period and uses two interventions in a two by two design. A six week period was chosen as the network will be stable (will not experience additional expansions) and the Transportation Department has agreed to limit other improvements during this time so as to not impact drivers. During the first three weeks, half of the affected drivers will receive information about the improved quality of charging on campus. This information will be delivered via email three times, roughly once per week. The Transportation Department planned to provide all affected drivers with a one time, $10 credit explicitly designed to as a mea culpa for the decreased quality experienced by drivers during the expansion. The Transportation Department has agreed to stagger the distribution of these funds with half of the drivers receiving the funds at the start of week 4, and half receiving the funds at the end of week 6. Further the half who receive the funds early will be evenly split between our information intervention and control groups. This will result in 4 groups of even size. A control group who receives no information and no money during the intervention, an information only group who receives information in the first three weeks and no financial incentive during the intervention, an incentive only group who receives no information in the first three weeks but financial incentive during the second three weeks, and an information and incentive group who receives information in the first three weeks, and the financial incentive at the start of the second three weeks.

We will also calculate the changes in the amount of power consumed and the number of sessions by time of day. Specifically we will calculate the results for the primary outcomes for drivers charging in the period of 5am-7am, 7 am-10 am, 10 am-4pm, 4 pm-9 pm, and 9 pm-5 am. Previous experiments within this club, we found incentives could drive perceptions of scarcity, and therefore change the time of day rather than the amount of charging. We therefore wish to analyze if time of day changes are present here as well.
Experimental Design Details
Not available
Randomization Method
Drivers were randomized electronically via computer with treated and control groups are to be pre stratified based on demographic information provided as part of the initial club intake survey. For example, UC Status (faculty, graduate student, undergraduate etc, Housing type, EV charger in residence, Income, Age, Race, Expected number of days working on campus, Car type (plugin EV vs Hybrid). We also stratify based on the post expansion behavior. Some drivers maintained their pre-expansion charging levels, some decreased their charging, and some stopped charging on campus all together. We plan to randomize between treatment and control within these groups. Drivers are sorted into these groups based on the ratio of their maximum charge in the spring of 2024 to their maximum charge in the fall of 2024. These groups are as follows:

Attrited: Driver did not charge in the summer or fall of 2024, we believe most of these drivers are individuals who have left campus permanently, such as graduated students, or staff who are now employed elsewhere. (ratio=0)

Eliminated Campus Charging: Driver did not charge in the fall of 2024 (but did charge in the summer). We believe these are discouraged drivers who's poor experience with the network expansion resulted in their opting to charge off campus. (ratio=0)

Reduced Campus Charging: Drivers who show lower, but non zero, charging in the fall of 2024 relative to the spring of 2024. These are drivers who have shifted some, but not all, of their charging off-campus. (0<ratio<0.85)

No Change or Increase: Drivers who have approximately the same or higher charging in the fall of 2024 relative to the spring (85<ratio)
Randomization Unit
Triton Club Drivers who were members of the club and charged during the period of disruption.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
756 Drivers
Sample size: planned number of observations
756 Drivers
Sample size (or number of clusters) by treatment arms
378 drivers per each intervention, or 189 drivers per treatment combination (control, information only, incentive only, both information and incentive)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For total charging, the SD of all affected drivers for all prior charging is 18.33 kWh per driver per week. Our minimum detectable effect (MDE) size is 3.7 kWh for the information treatment when half of the participants are treated. With a mean charge per driver per week of 14.8 kWh this represents a 22% increase/decrease in charging. When the financial incentives are distributed, the proportion of treated drivers increases to 75% and the corresponding MDE increases to 4.2 kWh. This represents a 26% increase/decrease in charging. We calculate MDE for the ratio of the driver's previous charging to the charging they do in the final week of our current data. This results in a SD of 1.23 with a MDE of the ratio of 0.24 for the first phase of the intervention and 0.28 for the second phase of the experiment. With a mean ratio of 0.4 this represents a 62% and 71% increase/decrease respectively. For the number of sessions, the SD of all affected drivers for all prior charging is 0.8 sessions. Our MDE for the number of sessions is 0.2 for the information only part of the treatment, and 0.23 for the information and incentive part of the treatment. This represents a 20% and 23% increase/decrease from the baseline respectively. We calculate MDE for the ratio of the driver's previous sessions to the sessions they do in the final week of our current data. This results in a SD of 1 with a MDE of the ratio of 0.16 for the first phase of the intervention and 0.19 for the second phase of the experiment. With a mean ratio of 0.4, this represents a 40% and 46% increase/decrease respectively.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
UC San Diego Office of IRB Administration
IRB Approval Date
2024-03-01
IRB Approval Number
805222