Customers' price elasticity in delivering event-based flexibility in France

Last registered on January 19, 2024


Trial Information

General Information

Customers' price elasticity in delivering event-based flexibility in France
Initial registration date
January 16, 2024

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
January 19, 2024, 2:11 PM EST

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


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Primary Investigator

University of Southern California

Other Primary Investigator(s)

PI Affiliation
Centre for Net Zero
PI Affiliation
Cente for Net Zero

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Centre for Net Zero (CNZ), in partnership with Octopus Energy France, will undertake a field trial during Winter 2023 - 2024. The trial will rigorously evaluate the price sensitivity of demand flexibility response from domestic customers in France.

We are building on the success of previous campaigns in the UK 2022-2023 and 2023-2024 Saving Sessions, which showcased domestic customers' willingness to curtail energy consumption during peak periods, and CNZ’s analysis thereof (Jacob et al., 2023). The French Eco-sessions is a scale-up from research in the UK and taking it to another country.
External Link(s)

Registration Citation

Bernard , Louise , Robert Metcalfe and Andrew Schein. 2024. "Customers' price elasticity in delivering event-based flexibility in France." AEA RCT Registry. January 19.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Electricity Consumption in kWh per half-hour: We will analyze the electricity consumption (kWh per half-hour) of participating customers during and around Eco-sessions.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We will randomize customers to recieve different incentives for curtailing energy demand.
Experimental Design Details
Not available
Randomization Method
To ensure the robustness and applicability of our findings, we block units into experimental blocks of 5 units. We first find exact matches on the categorical variables (geographic locations, housing type, whether or not they answered the energy consumption survey) and then find the closest match on the numerical variable using Mahalanobis distance. Blocking begins by creating a measure of multivariate distance between all possible pairs of units. Within each category, the algorithm optGreedy calls an optimal-greedy algorithm, repeatedly finding the best remaining match. The optGreedy algorithm breaks ties by randomly selecting one of the minimum-distance pairs. Once the groups of 5 closest units are created, we randomly allocate a number from 1 to 5 to each unit within each group. Finally, we recode units 3 to 5 to be in the low incentive group. If there are more than 5,000 in units 1 and 2 (medium and high), I randomly select the extra groups and allocate all units in these groups to be in the low incentive group. Some groups do not have 5 matches. These units are randomly allocated to one of the 5 groups.

The following variables are used to create the groups:
- Geographic locations at the sub-national level
- Housing type
- Average annual consumption - we will create a measure of distance between all possible pairs of units
- Whether or not they answered the survey about energy consumption patterns
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
We will invite 157k eligible customers to participate in Eco-sessions. We will divide the customers that accept the invitation into 3 groups: 10k, 5k, 5k.
Sample size: planned number of observations
157k eligible customers with half-hourly data on energy use prior, during, and after the trial.
Sample size (or number of clusters) by treatment arms
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
In this section, we use the standard errors we obtained from the differences-in-differences analysis of UK Saving Sessions 2022-2023 in Jacob et al. (2023) to estimate the Minimum Detectable Effect Size for Eco-sessions in France. We first give a rough estimation of how the change in group size affects the standard errors of the analysis, and then calculate the MDES based on a power of 80% and significance level of 5%. Based on treatment groups of 5,000 customers, we calculate a MDES of 0.08 kWh per half-hour. We calculated this MDES by “scaling up” the standard error of the difference in differences between Octopus and Bulb customers in Table AT.4 from by Jacob et al. (2023) based on the smaller sample sizes (5,000 per treatment group) involved in the French Eco-sessions. We then multiplied these scaled up standard errors by 2.8, according to the rule of 2.8. This MDES (0.08 kWh per half-hour) is lower than the average demand reduction of 0.1 kWh per half-hour for signed-up customers and 0.17kWh per half-hour for opted-in customers during Winter 2022-2023 Saving Sessions in Great Britain identified in Jacob et al. (2023). Note that the standard errors we have used come from a difference-in-differences (DiD) estimator used in Jacob et al. (2023). We expect the RCT design will produce smaller standard errors than a DiD. However, we have two reasons to expect a smaller treatment effect. We will compare different treatment incentive levels in the main analysis on electricity consumption reduction instead of a simple treated versus control group. In addition, the French Eco-sessions (seven hours of the day) will be longer than the Great Britain Saving Sessions (which were typically one hour, and at most two). Based on the better design (RCT) but smaller treatment effect, we expect to be sufficiently powered to capture a treatment effect as small as twice the effect found during saving sessions.

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

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