Residential EV Charging Flexibility Experiment

Last registered on August 10, 2026
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Pre-Trial

Trial Information

General Information

Title
Residential EV Charging Flexibility Experiment
RCT ID
AEARCTR-0019056
Initial registration date
August 04, 2026

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
August 10, 2026, 9:08 AM EDT

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

Locations

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Primary Investigator

Name
Erica Myers
Affiliation
University of Calgary
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Other Primary Investigator(s)

PI Name
David Brown
PI Affiliation
University of Alberta
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PI Name
Koichiro Ito
PI Affiliation
University of Chicago
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PI Name
Blake Shaffer
PI Affiliation
University of Calgary
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Additional Trial Information

Status
In development
Start date
2026-06-30
End date
2027-04-30
Keywords
Behavior, Environment & Energy, Welfare
Additional Keywords
electric vehicles, automated scheduling, critical peak pricing, technology adoption, treatment effect heterogeneity
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
As electric vehicle (EV) adoption increases, unmanaged home charging has the potential to increase electricity demand during periods when the electric grid is already under stress. Utilities are increasingly exploring demand flexibility programs that encourage EV owners to shift charging away from peak periods, but little evidence exists on how best to recruit households into these programs or whether different program designs attract different participants and produce different charging behavior.

This randomized controlled trial evaluates alternative approaches to encouraging participation in an EV charging rewards program. Eligible households are randomly offered one of two program types. In a behavioral program, participants receive notifications and financial rewards for voluntarily avoiding charging during peak demand events. In an automated program, participants connect a smart charging application that automatically schedules vehicle charging around peak events while ensuring vehicles are charged when needed. Financial incentives for the two program types are randomly varied across households.

The study will measure how incentive levels and program design affect enrollment decisions, participation, and charging behavior during peak demand events. The results will provide evidence on the effectiveness of behavioral and automated approaches to residential demand flexibility and inform the design of utility programs that support reliable and efficient operation of the electric grid as EV adoption continues to grow.
External Link(s)

Registration Citation

Citation
Brown, David et al. 2026. "Residential EV Charging Flexibility Experiment." AEA RCT Registry. August 10. https://doi.org/10.1257/rct.19056-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Eligible EV owners are invited to participate in one of two EV charging rewards programs. The behavioral program provides notifications before peak demand events and rewards participants for voluntarily avoiding charging during those events. The automated program uses a smart charging application to automatically schedule charging outside of peak demand events while ensuring vehicles are charged when needed. Participants earn financial rewards for reducing charging during utility-designated peak demand events.
Intervention Start Date
2026-08-10
Intervention End Date
2027-03-31

Primary Outcomes

Primary Outcomes (end points)
Program enrollment (whether the household enrolls in either EV charging rewards program).
Program choice (behavioral program, automated program, or no enrollment).
Program retention (continued participation over the study period).
EV charging behavior during utility-designated peak demand events.
EV charging shifted outside utility-designated peak demand event windows.
Participant override behavior during automated charging events.
Primary Outcomes (explanation)
Program enrollment is defined as successful enrollment into either charging rewards program.

Program choice records whether households enroll in the behavioral program, the automated program, or neither.

Program retention is measured as continued active participation in the enrolled program throughout the study period.

Charging during peak demand events is measured using high-frequency EV charging telemetry and includes indicators for charging during an event and total charging occurring within the event window.

Charging shifted outside event windows is measured using charging occurring before and after utility-designated peak demand events relative to charging during the event.

Override behavior is defined as participant actions that temporarily disable or bypass automated charging control during a peak demand event.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The study recruits from a sampling frame of 4,676 residential customers identified by the utility as probable electric vehicle owners. Recruitment occurs in multiple waves. Approximately 900 households are included in the first recruitment wave, with the remaining eligible households recruited in subsequent waves.

Within each recruitment wave, households are stratified by baseline annual electricity consumption and individually randomized to one of six incentive combinations that independently vary the financial rewards offered for the behavioral and automated charging programs. Incentive offers range from $100 to $300 for each program.

Following randomization, households choose whether to enroll and, if they enroll, whether to participate in the behavioral or automated charging program. Enrolled participants may experience multiple weather-designated peak demand events each month and receive financial rewards for avoiding EV charging during those events. Event timing and duration vary according to the experimental protocol.

Allocation probabilities for later recruitment waves may be adjusted based on observed enrollment rates from earlier waves to improve statistical precision. Any such adjustments will be based solely on enrollment behavior and operational considerations and will not use charging or other outcome data.

Experimental Design Details
Not available
Randomization Method
Randomization was conducted by computer using a reproducible random number generator. Households were stratified by baseline annual electricity consumption, and random assignment was performed independently within each stratum prior to recruitment.
Randomization Unit
Individual households are the primary unit of randomization for recruitment incentive offers. Among enrolled participants, individual households are also randomized to alternative peak demand event windows as part of the event implementation protocol. If households have multiple utility accounts, only one email/utility account number combination can enroll in the program.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
4,676
Sample size: planned number of observations
4,676 households (with repeated household-level observations from AMI, EV charging telemetry, and administrative data)
Sample size (or number of clusters) by treatment arms
Wave 1: 906 households, equally allocated across six treatment arms (approximately 151 households per arm). Wave 2 and subsequent recruitment: The remaining eligible households (approximately 3,770) will be allocated across the six treatment arms using pre-specified allocation probabilities that may be adjusted based on observed enrollment rates from earlier waves to improve statistical precision. Final sample sizes by treatment arm will therefore depend on the adaptive allocation used in subsequent recruitment waves
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Conjoint Faculties Research Ethics Board (CFREB), University of Calgary
IRB Approval Date
2026-07-24
IRB Approval Number
REB26-0507
Analysis Plan