Seminario de Investigación ECOBAS: Mar Reguant – Northwestern University (Ilinois) & Barcelona School of Economics
‘The Distributional Impacts of Real-Time Pricing’
Joint work with Michael Cahana, Natalia Fabra and Jingyuan Wang
Xoves 27 de Xaneiro ás 12:00H (CET)
GRAVACIÓN DO SEMINARIO
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We study the distributional impacts of real-time pricing (RTP) in the Spanish electricity market, which rolled out RTP as the default tariff for a large share of residential customers. We complement aggregate patterns of distributional effects with a method to infer individual households’ income using zip-code income distributions. We identify three channels for the distributional impacts of RTP: consumption patterns, 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 prices differences are greater across months than within months, the regressive effect dominated. Using counterfactual experiments, we find that RTP makes low-income households particularly vulnerable to adverse weather 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.
We study the distributional impacts of real-time pricing (RTP) in the Spanish electricity market, which rolled out RTP as the default tariff for a large share of residential customers. We complement aggregate patterns of distributional effects with a method to infer individual households’ income using zip-code income distributions. We identify three channels for the distributional impacts of RTP: consumption patterns, 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 prices differences are greater across months than within months, the regressive effect dominated. Using counterfactual experiments, we find that RTP makes low-income households particularly vulnerable to adverse weather 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.