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The Distributional Impacts of Real-Time Pricing (WP-22-23)

Michael Cahana, Natalia Fabra, Mar Reguant, and Jingyuan Wang

The authors study the distributional impacts of real-time pricing (RTP) in the Spanish electricity market, where RTP was rolled out as the default tariff for a large share of residential customers. They complement aggregate patterns of distributional effects with a novel method to infer individual households' income using zip code income distributions. The researchers identify three channels for the distributional impacts of RTP: consumption profiles, appliance ownership, and location. The first channel makes the switch from monthly to hourly prices progressive since high-income households consume disproportionately more at peak times when real-time prices are higher. However, the other two channels make the switch from annual to monthly prices regressive: low-income households, who tend to have more electric heating, benefit from the price insurance provided by time-invariant prices during winter, when prices tend to be higher and more volatile. Given that price differences are greater across months than within months, the regressive effect dominates in the researchers’ application. Using counterfactual experiments, they find that RTP makes low-income households particularly vulnerable to adverse price shocks during winter. In the future, the wider adoption of enabling technologies (including storage and demand response devices) by the high-income groups might worsen the distributional impacts of RTP. The authors’ findings should allow to design an equitable real-time pricing system while retaining its efficiency properties.

Michael Cahana, Data Scientist, Quora

Natalia Fabra, Professor of Economics, Universidad Carlos III de Madrid

Mar Reguant, Associate Professor of Economics and IPR Associate, Northwestern University

Jingyuan Wang, Department of Economics, Northwestern University

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