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

Simple and innovative Internet-of-Things (IoT)-based technologies for automated electricity demand management have the potential to make the energy sector cleaner by shifting electricity consumption to the hours when power generation is the least carbon-intensive, leading also to cost savings. This study will therefore examine how Internet-of Things (IoT) 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. Randomising both access to the technology and the timing and intensity of automated appliance switch-off events across a large sample of residential smart meter users in India, the researchers will study the factors that affect technology adoption and usage behaviour. Leveraging real-time data on how households respond to automated control of select appliances, they will shed light on the scope for flexibility in electricity demand. Finally, the researchers 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.

The researchers will work in partnership with one of India’s public-private electricity distribution franchises, Tata Power, to conduct a randomised experiment in which they will offer residential electricity consumers access to automation technologies with the purpose of examining the scope for flexibility in their electricity demand and the behavioural drivers of adoption and usage of the technology. The sample will be drawn from the nearly 300,000 residential customers whose power meters have been replaced with smart meters and for whom the researchers will acquire real-time electricity consumption through a data access agreement with Tata Power. The researchers will offer these smart meter users WiFi-enabled smart plugs that control the operation of a household appliance, thus enabling greater flexibility in their demand. The plugs turn off the appliance for short intervals and participants will be offered a financial incentive to leave the appliance off for the duration of the interval. If they override the plug setting and turn the appliance on during the switch-off interval, they forgo the financial benefit. By varying the switch-off events by season, weather, and baseline consumption, the researchers will generate insights into how the potential for demand flexibility depends on these factors. The same experiment will allow the researchers to produce time-varying estimates of the maximum willingness to pay to avoid a supply disruption, or the value of lost load (VoLL), which is an essential policy parameter in the design of electricity markets and long-term generation capacity planning.

The evidence this study produces on the factors affecting the demand for automated electricity demand management, the scope for flexibility in household electricity demand and the welfare cost of supply interruptions will directly inform how electric utilities prioritise network investments and how regulators and policymakers in developing countries implement time-of-use pricing and automation programmes, which would contribute to the efficient decarbonisation of the energy system and promote green and sustainable growth. This study will also contribute to the burgeoning literature on automated energy demand management, which has so far been focused almost exclusively on developed countries, by investigating the drivers of demand for these technologies and the potential for demand flexibility in a fast-growing developing country, where the co-benefits of a rapid transition to clean energy, especially in terms of improved air quality, are large.



Shefali Khanna

Imperial College London

Ralf Martin

Imperial College London

Mirabelle Muûls

Imperial College London