Can Electricity Demand Management Drive the Transition to Clean and Affordable Energy in Poor Economies?

Last registered on April 07, 2022

Pre-Trial

Trial Information

General Information

Title
Can Electricity Demand Management Drive the Transition to Clean and Affordable Energy in Poor Economies?
RCT ID
AEARCTR-0009118
Initial registration date
April 01, 2022

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
April 04, 2022, 9:44 AM EDT

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

Last updated
April 07, 2022, 7:53 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

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Primary Investigator

Affiliation
Imperial College London

Other Primary Investigator(s)

PI Affiliation
Imperial College London
PI Affiliation
Imperial College London

Additional Trial Information

Status
In development
Start date
2022-05-01
End date
2025-06-30
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study will contribute to our understanding of how Internet-of Things (IoT)-based technologies that enable automated electricity demand management can drive the clean energy transition in low and middle-income countries through an analysis of household energy demand. Randomizing both access to the IoT technology and the timing of automated appliance switch-off events across a large sample of residential smart meter users in India, we will study the factors that affect technology adoption and usage behavior. Leveraging real-time data on how households respond to automated control of selected appliances, we will shed light on the scope for flexibility in their electricity demand. Finally, we will use the experimental variation in electricity supply generated by the IoT algorithm to develop precise time-varying measures of the welfare cost of supply interruptions.
External Link(s)

Registration Citation

Citation
Khanna, Shefali, Ralf Martin and Mirabelle Muuls. 2022. "Can Electricity Demand Management Drive the Transition to Clean and Affordable Energy in Poor Economies?." AEA RCT Registry. April 07. https://doi.org/10.1257/rct.9118-1.1
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
The Government of India has a target of installing 250 million smart meters by 2025. This rapid deployment of smart meters has the potential to accelerate the clean energy transition by enabling dynamic retail pricing of electricity, which could incentivise consumers to use power when it is generated from clean sources and thereby reduce the need for costly supply-side solutions such as energy storage to compensate for times when the supply of renewable energy is insufficient to meet the demand. Furthermore, to the extent that dynamic pricing induces peak-shaving, regulations that enable dynamic pricing could lower the cost of supply, which is especially important in the Indian context where distribution companies often contract a large amount of generation capacity to meet the anticipated demand of only a few hours of the year. However, the effectiveness of dynamic pricing depends on (a) consumers’ awareness of the retail price of electricity, and (b) their ability and willingness to respond to changes in the retail price of electricity. We will conduct a randomised control trial in partnership with an electricity distribution company in India, where participants will be offered simple IoT-enabled automation devices that generate automated switch-off events. Participants will be offered rewards for each kWh of energy saved during the switch-off events. By allowing the rewards to vary over the hours of the day, the trial can be used to simulate a dynamic price, although we also expect to uncover alternative ways of generating incentives for load balancing.
Intervention Start Date
2023-07-01
Intervention End Date
2024-06-30

Primary Outcomes

Primary Outcomes (end points)
The primary outcomes are (a) participation, and (b) meter- and switch-level electricity consumption, which will be monitored automatically through an integrated web platform and smartphone app that have been developed for this project.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
In each three-month iteration of the study, 1,000 participants will be randomly assigned to one of two cross-cutting treatment groups: (1) high and low reward per kWh of electricity saved during switch-off event and (2) fixed vs variable reward rate schedule over the hours of the day. Depending on the number of switch-off events administered per customer-day, the research team may also (a) periodically vary the fixed and variable rate schedules for a subset of participants to more precisely estimate the willingness to pay to avoid a switch-off at each hour of the day, and (b) vary the amount of notice time given to participants before each switch-off event to estimate how the scope of demand flexibility varies with the amount of advance notice given to consumers.
Experimental Design Details
Not available
Randomization Method
Randomization done in office by a computer
Randomization Unit
Residential smart-meter user x 30-minute interval
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
4,000 participants
Sample size (or number of clusters) by treatment arms
1,000 participants in one of four reward treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number