Incentives to shift EV charging in a sun-soaked grid

Last registered on September 15, 2023


Trial Information

General Information

Incentives to shift EV charging in a sun-soaked grid
Initial registration date
September 06, 2023

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
September 15, 2023, 8:45 AM EDT

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



Primary Investigator

University of Queensland

Other Primary Investigator(s)

PI Affiliation
University of Queensland
PI Affiliation
University of Queensland
PI Affiliation
University of Queensland
PI Affiliation
University of Queensland
PI Affiliation
University of Queensland

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Transition of energy systems to renewable sources and electrification of transportation are key components of most net-zero emission pathways. Electric vehicles could act as ``batteries on wheels’’ to help stabilize the grid and store energy from times of peak renewable production to times of peak consumption, or they could put increasing pressure on the grid if they add to peak demand. We randomly assign vehicle owners to receive incentives to shift charging of electric vehicles towards times of abundant solar generation and away from times when solar generation falls and other sources of demand peak. We implement the experiment using novel telematics data to measure volume and time of charge for over 400 vehicles in Australia. We compare the results of the trial to expert predictions.
External Link(s)

Registration Citation

Friesen, Lana et al. 2023. "Incentives to shift EV charging in a sun-soaked grid." AEA RCT Registry. September 15.
Sponsors & Partners

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Experimental Details


The interventions are monetary incentives ($ per kWh) to (a) reduce charging (kWh) in peak hours (4pm to 8pm) and (b) increase charging in the middle of the day or ‘sun soak’ hours (10am – 3pm) on weekdays relative to their time of day baselines (determined during pre-treatment data).
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Hourly charge
Primary Outcomes (explanation)
Charge (kWh) added to vehicle by hour of day is measured from real time charging records.

Secondary Outcomes

Secondary Outcomes (end points)
Share of charge by hour of day (%).
State of battery charge by hour of day (min, max, mean).
Driving distance by hour of day (km).
Share of fast charge by hour of day (%).
Location of charge by hour of day (geocode).
Expert predictions.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We recruit Tesla owners to participate in a nationwide study on electric vehicle driving and charging in Australia. We use telematics data to track driving and charging of vehicles at the minute-by-minute level and surveys to collect owner characteristics. We collect up to two years of data per vehicle. Vehicle owners in the treatment group will receive rewards to change the timing of their charge for a period of 3 months. We also elicit expert predictions of the effects of treatment.
Experimental Design Details
Participants who have more than 2 Teslas, who have a Baseline Daily Charge > 40kWh, and participants who are not able to shift their charging in response to incentives (e.g. solar vehicle owner who never charges in peak period) are removed from the sample before randomization to treatment and control.

Using survey data, the sample is divided into two groups: (Solar) those with rooftop solar and (Non Solar) those without rooftop solar. Treatment is stratified by Solar, Baseline Daily Charge > 10kWh, and when the participant is first observed.
Randomization Method
Randomization done in office by computer
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
Sample size: planned number of observations
Sample size (or number of clusters) by treatment arms
195 treatment 195 control (70 non solar treatment 70 non solar control, 125 solar treatment 125 solar control).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Engineering, Architecture and Information Technology Low Negligible Risk Subcommittee
IRB Approval Date
IRB Approval Number
Project Number 2021/HE002179


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials