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Abstract
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Before
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 select 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.
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After
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.
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Last Published
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April 04, 2022 09:44 AM
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After
April 07, 2022 07:53 AM
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Intervention (Public)
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Before
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 effectively simulate a dynamic price.
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After
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.
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Primary Outcomes (End Points)
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Before
The primary outcomes are 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.
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After
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.
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Experimental Design (Public)
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Before
In each three-month iteration of the study, 1000 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 hours of 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.
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After
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.
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Randomization Unit
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Before
Residential smart-meter user
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Residential smart-meter user x 30-minute interval
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Intervention (Hidden)
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Before
To recruit participants for the study, we will work with a large Indian electricity distribution company to target distribution feeders with a high proportion of domestic smart meter users. A random sample of 5,000 domestic smart meter users will be invited every three months over the course of one year to participate in the study. The invitation will describe the purpose of the study, the study procedures, and the benefits to participants as individuals and to society. The first 1000 customers that express an interest will be invited to download our smartphone app and enroll in the study. The research team will send them a Wi-Fi enabled smart switch, which will be used to remotely operate one large appliance, such as a washing machine, an air conditioner or an electric geyser. The IoT algorithm will generate brief switch-off events one or more times a day, which participants can override by turning the smart switch on manually or via a smartphone app. At the time of registration, participants will have the opportunity to state all the times during each day of the week when they would prefer not to have a switch-off event. They can change their preferences on the app at any time during the three months for which they will be enrolled in the study. All participants will be offered guaranteed monetary rewards in proportion to the units of electricity saved during the switch-off event. If they override the event and turn on the smart switch, they will only be rewarded for the amount of time that the appliance was switched off before the override.
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After
To recruit participants for the study, we will work with a large Indian electricity distribution company to target distribution feeders with a high proportion of domestic smart meter users. A random sample of 5,000 domestic smart meter users will be invited every three months over the course of one year to participate in the study. The invitation will describe the purpose of the study, the study procedures, and the benefits to participants as individuals and to society. The first 1,000 customers that express an interest will be invited to enroll in the study on a web platform. Participants will be sent a Wi-Fi enabled smart switch, which will be used to remotely operate one large appliance, such as a washing machine, an air conditioner or an electric geyser. The IoT algorithm will generate brief switch-off events one or more times a day, which participants can override by turning the smart switch on manually or via a smartphone app. At the time of registration, participants will have the opportunity to state all the times during each day of the week when they would prefer not to have a switch-off event. They can change their preferences on the app at any time during the three months for which they will be enrolled in the study. All participants will be offered guaranteed monetary rewards in proportion to the units of electricity saved during the switch-off event. If they override the event and turn on the smart switch, they will only be rewarded for the amount of time that the appliance was switched off before the override.
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