Public EV Charging Price Plunge Trial: Supporting Grid Stability through Dynamic Discounting

Last registered on January 02, 2025

Pre-Trial

Trial Information

General Information

Title
Public EV Charging Price Plunge Trial: Supporting Grid Stability through Dynamic Discounting
RCT ID
AEARCTR-0015061
Initial registration date
December 19, 2024

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 02, 2025, 9:52 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
Centre for Net Zero

Other Primary Investigator(s)

PI Affiliation
Centre for Net Zero
PI Affiliation
Newcastle University, Centre for Net Zero
PI Affiliation
Columbia University, Centre for Net Zero

Additional Trial Information

Status
In development
Start date
2024-12-19
End date
2025-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study will run a randomised control trial on electric vehicle users in the UK. We will have access to a pool of approximately 450,000 electroverse customers. Of these customers, approximately our sample will be the approximately 120,0000 users resident in the UK (where the trial takes place) who (as of 19 December 2024) have their device language set to English, have push notifications enabled, and have not opted out of receiving price plunge notifications. These customers will be exposed to one of four treatments:
1. Control: will receive no notifications about price plunge events;
2. Moral suasion: will receive a notification encouraging them to take advantage of hours with green electricity to charge their EV;
3. Low discount group: will receive notifications informing them that they will receive a discount of 15% during price plunge event hours; and
4. High discount group: will receive notifications informing them that they will receive a discount of 40% during price plunge event hours.
The specific times specified in the push notifications (i.e., “9pm-11pm”) will be exactly the same for all groups 2, 3, and 4.
Customers will be randomised into groups, each of which receives a sequence of treatments in a partial “Latin Squares” structure as part of a crossover randomisation design. There will be wash-out periods of 1 week between crossovers.
External Link(s)

Registration Citation

Citation
Bernard, Louise et al. 2025. "Public EV Charging Price Plunge Trial: Supporting Grid Stability through Dynamic Discounting." AEA RCT Registry. January 02. https://doi.org/10.1257/rct.15061-1.0
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Experimental Details

Interventions

Intervention(s)
Customers will be exposed to one of four treatments:
1. Control: will receive no notifications about price plunge events;
2. Moral suasion: will receive a notification encouraging them to take advantage of hours with green electricity to charge their EV;
3. Low discount group: will receive notifications informing them that they will receive a discount of 15% during price plunge event hours; and
4. High discount group: will receive notifications informing them that they will receive a discount of 40% during price plunge event hours.
Intervention Start Date
2024-12-19
Intervention End Date
2025-03-31

Primary Outcomes

Primary Outcomes (end points)
Charging: We will analyse whether the customer has charged during Price Plunge Events. This is a binary variable (1 = charged; 0 otherwise).
Electricity Consumption in kWh: We will analyse the electricity consumption (kWh) of participating customers during and around Price Plunge Events. This is a continuous variable, truncated at 0.
Primary Outcomes (explanation)
See analysis plan.

Secondary Outcomes

Secondary Outcomes (end points)
Charging outside of events (both as a binary outcome and in terms of kWh)
Secondary Outcomes (explanation)
See analysis plan.

Experimental Design

Experimental Design
See analysis plan.
Experimental Design Details
Not available
Randomization Method
Randomisation will take the form of a “natural” randomisation. Specifically, Electroverse customers are assigned a unique user ID when they first join the platform; this ID is a positive integer. While these IDs are not explicitly random, they are sequential with respect to the join date of the customer. Customers will be assigned to one of four groups based on the modulo operation applied to their user ID:
user_id mod 4
This calculation assigns each customer to a group based on the remainder when their user ID is divided by 4 (i.e., 0, 1, 2, or 3). This randomisation method naturally creates balanced groups across customers’ join dates.
Randomization Unit
user id
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
The cluster is the user id, because there will be ~15 events (and thus up to 15 outcomes per user).
We have approximately 120,000 users.
Sample size: planned number of observations
15 events * 120,000 users = 1,800,000 user*events
Sample size (or number of clusters) by treatment arms
Discussed above: ~15 events per user (see analysis plan for details)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
See analysis plan for detailed power calculations. For the main results, the following parameters were chosen: Arm size: 40,000 user IDs Events number: 16 Periods of the day: Daytime (starting time of the event between 10 am to 5pm) Under these conditions, we would be able to measure the MDES of a 12% increase in the baseline average probability of charging, and a 15% increase in total kWh purchased (corresponding to a MDES of 0.000198 and 0.00585kWh respectively). We believe these MDES suggest we are well powered. We expect effect sizes of 44% for the low group and 80% for the high group (based on previous price plunge events using a simple pre-post analysis). Using a baseline charging of 0.16%, we then expect that 0.29% and 0.23% of the high group and low group will be charging, respectively. That is a difference of 0.000576 percentage points, above the MDES of 0.000198.
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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