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Demand side response events in Spain

Last registered on February 04, 2026
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Pre-Trial

Trial Information

General Information

Title
Demand side response events in Spain
RCT ID
AEARCTR-0017787
Initial registration date
January 29, 2026

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
February 04, 2026, 9:43 AM EST

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

Locations

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Primary Investigator

Name
Andrew Schein
Affiliation
Centre for Net Zero
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Other Primary Investigator(s)

PI Name
Yixin Sun
PI Affiliation
Centre for Net Zero
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Additional Trial Information

Status
In development
Start date
2026-01-30
End date
2026-05-30
Keywords
Environment & Energy
Additional Keywords
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
As countries transition toward energy systems with higher penetration of renewable generation, balancing supply and demand is becoming increasingly complex. Demand flexibility provides a mechanism for customers to respond to system conditions by lowering peak demand and reducing grid congestion, thereby reducing reliance on carbon-intensive generators. We test the effectiveness of this mechanism by conducting a large randomized controlled trial in Spain to evaluate how behavioral and financial incentives can motivate households to reduce electricity consumption during periods of grid stress (“Saving Sessions”). With a sample of 108,163 residential customers, we will assess the effectiveness of several behavioral and financial incentives in shifting short-term household demand during event hours.
External Link(s)

Registration Citation

Citation
Schein, Andrew and Yixin Sun. 2026. "Demand side response events in Spain." AEA RCT Registry. February 04. https://doi.org/10.1257/rct.17787-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
See analysis plan.
Intervention Start Date
2026-01-30
Intervention End Date
2026-05-30

Primary Outcomes

Primary Outcomes (end points)
Associated CUPS (in Spanish,Código Universal del Punto de Suministro or universal supply point number) electricity consumption (import kWh only) at the hourly level during “Saving Sessions” events, in kWh
Primary Outcomes (explanation)
Consumption during Saving Sessions event hour, measured at the customer level

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
See analysis plan
Experimental Design Details
Not available
Randomization Method
Matched pairs randomization; in office by a computer; see analysis plan for details.
Randomization Unit
Customer email address
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
108,163 clusters, where regressions are clustered at the customer email address level
Sample size: planned number of observations
For 108,163, if each event is one hour, with a total of 5 events, this results in approximately 432,795 observations in the main analysis, clustered at the customer level (108,163 clusters).
Sample size (or number of clusters) by treatment arms
Pure Control: 9,999
Control: 16,360
Green: 16,361
High: 16,361
Low: 16,361
Prepay: 16,361
Prepay2: 16,360
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
We conducted simple power calculations of our trial design using electricity consumption data from Spanish Octopus Energy customers. We randomly selected five “event dates” between November 1 and December 15, 2025, and used customers’ actual observed consumption during these events. The sample consists of customers who will participate in our Saving Sessions, with approximately 16,000 customers assigned to each treatment arm. We exclude the pure control group from the analysis, which we believe makes our power calculation conservative. With this consumption dataset, we estimated the same regression model that will be used in the final analysis, regressing electricity consumption on treatment indicators and control variables, as specified in Equation (1). We applied the rule of 2.8, under which the minimum detectable effect size (MDES) at 80% power and a 5% significance level is equal to 2.8 times the standard error of the treatment coefficient. Using this approach, we estimate an MDES of 0.00789 kWh per hour for each treatment arm, corresponding to approximately 1.4% of average hourly consumption during event periods.
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
Heartland Institutional Review Board (HIRB)
IRB Approval Date
2026-01-22
IRB Approval Number
012226-1310
Analysis Plan

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