Incentives to shift EV charging in a sun-soaked grid

Last registered on September 15, 2023
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Pre-Trial

Trial Information

General Information

Title
Incentives to shift EV charging in a sun-soaked grid
RCT ID
AEARCTR-0011698
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.

Locations

Country
Australia
Region

Primary Investigator

Name
Andrea La Nauze
Affiliation
University of Queensland
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Other Primary Investigator(s)

PI Name
Lana Friesen
PI Affiliation
University of Queensland
Contact Investigator
PI Name
Kai Li Lim
PI Affiliation
University of Queensland
Contact Investigator
PI Name
Flavio Menezes
PI Affiliation
University of Queensland
Contact Investigator
PI Name
Lionel Page
PI Affiliation
University of Queensland
Contact Investigator
PI Name
Thara Philip
PI Affiliation
University of Queensland
Contact Investigator

Additional Trial Information

Status
On going
Start date
2021-11-15
End date
2024-06-30
Keywords
Environment & Energy
Additional Keywords
Electric vehicle charging, electricity prices, time of use pricing, renewable energy, rooftop solar, solar power
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
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)
Project website

Registration Citation

Citation
Friesen, Lana et al. 2023. "Incentives to shift EV charging in a sun-soaked grid." AEA RCT Registry. September 15. https://doi.org/10.1257/rct.11698-1.0
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Experimental Details

Interventions

Intervention(s)
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
2023-09-11
Intervention End Date
2023-12-08

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
Not available
Randomization Method
Randomization done in office by computer
Randomization Unit
Participant
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
390
Sample size: planned number of observations
435
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)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Engineering, Architecture and Information Technology Low Negligible Risk Subcommittee
IRB Approval Date
2023-09-06
IRB Approval Number
Project Number 2021/HE002179
Analysis Plan