Split Standing Charge Tariff Trials: Encouraging demand flexibility to meet clean power goals

Last registered on November 03, 2025

Pre-Trial

Trial Information

General Information

Title
Split Standing Charge Tariff Trials: Encouraging demand flexibility to meet clean power goals
RCT ID
AEARCTR-0017049
Initial registration date
November 03, 2025

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
November 03, 2025, 10:45 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

Affiliation
Newcastle University

Other Primary Investigator(s)

PI Affiliation
Ofgem
PI Affiliation
Ofgem
PI Affiliation
Ofgem
PI Affiliation
Ofgem
PI Affiliation
Ofgem
PI Affiliation
Ofgem
PI Affiliation
Ofgem

Additional Trial Information

Status
In development
Start date
2025-11-04
End date
2026-03-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
The supply of electricity requires the development and maintenance of infrastructure, the cost of which are generally recovered through a Standing Charge (SC), which is separate from electricity prices. Consumers have often reported feelings of unfairness on the existence of the Standing Charge, prompting the need to look for alternative ways to recover the costs of energy infrastructure investment. Here, we run three different RCTs with different suppliers to explore novel approaches to standing charges. In all trials, we propose a novel approach that links the standing charge to usage during peak times, whereby consumers pay less (more) standing charge if they limit (do not limit) electricity consumption during peak time. The literature currently presents limited evidence on the impacts of ongoing long-term peak-time price signals in encouraging consumers to reduce energy at peak times; these trials aim to explore how a broad range of energy consumers respond to peak time price signals. Importantly, this novel approach to SC has the potential to address consumer perceptions of unfairness and encourage energy usage patterns that substantially reduce network strain – and costs – at peak times.
External Link(s)

Registration Citation

Citation
Almond, Rosie et al. 2025. "Split Standing Charge Tariff Trials: Encouraging demand flexibility to meet clean power goals." AEA RCT Registry. November 03. https://doi.org/10.1257/rct.17049-1.0
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Experimental Details

Interventions

Intervention(s)
The trial aims to test the potential of a restructured standing charge linked to peak-time electricity usage in encouraging consumers to shift their demand away from peak periods.  
We aim to learn: 
• The magnitude of peak-time demand reduction potential
• Substitution patterns: are consumers reducing demand or shifting it to off-peak periods?
• The impact of the tariff on household bills and system cost recovery 
• Whether consumers find the restructured standing charges fair, acceptable, understandable, and actionable 
The trials introduce a split standing charge mechanism where consumers stand to earn up to 50% off their daily standing charge based on their level of peak-time electricity usage that day relative to other consumers. The rebate is calculated daily, providing participants with a fresh opportunity each day to lower their peak energy usage and earn a higher rebate off their standing charge. At the end of every [month/two weeks] customers will be notified of their cumulative rebate earnt off their daily standing charges.
Intervention Start Date
2025-11-04
Intervention End Date
2026-03-31

Primary Outcomes

Primary Outcomes (end points)
The key outcome variable is the amount of electricity consumed by participating, measured in kWh. We will split between peak and off-peak energy consumption.
Primary Outcomes (explanation)
We will separately measure how much electricity households consumed, in kWh, during peak time, defined as between 4-7pm on working days (Mon-Fri) only; and electricity consumed by the same households, in kWh, off-peak, that is outside 4-7pm on working days. These two elements should sum to total household electricity consumption.

Secondary Outcomes

Secondary Outcomes (end points)
customer satisfaction and approval of standing charges.
Secondary Outcomes (explanation)
This is going to be collected in a post-trial survey, and will be available only for the subset of consumers who will respond to the survey (expected: 5-10% of the sample). We have not done a power analysis for this component of the study.

Experimental Design

Experimental Design
The trial introduces a split standing charge mechanism where consumers stand to earn up to 50% off their daily standing charge based on their level of peak-time electricity usage that day relative to other consumers.
All 3 suppliers will have a sample of 20,000 participants each (10000 in the control group), and will reward participants on the basis of their peak electricity consumption, with reward size inversely related to peak consumption.
The design changes slightly across consumers, as suppliers personalised the intervention based on their own infrastructure.
Experimental Design Details
Not available
Randomization Method
Suppliers 1 and 2 will randomise using a random number generator that allocates participants to treatment vs control.
Supplier 3 will ask participants to volunteer into treatment, and then generate a matched control group.
Randomization Unit
Individual consumer.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
20,000 participants (i.e., households) in each of the three trials, for a total of 60,000 participants.
Sample size: planned number of observations
20,000 participants (i.e., households) in each of the three trials, for a total of 60,000 participants. Time periods: 3-5 months, where consumption is recorded at 30-min intervals from smart meters. For a single month (30 days), we would expect 28.8 million data points per supplier, which we will aggregate at daily level to aggregate peak vs off-peak electricity consumption..
Sample size (or number of clusters) by treatment arms
In trial 1, we expect 10,000 households in the control, 5,000 in treatment 1, and 5,000 in treatment 2. We will also have two smaller groups of around 300 households each for additional A/B testing.
In trial 2 and 3, we will have 10,000 participants in the control, and 10,000 in the treatment group.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
For this calculatuion we set power= 80%, and significance 5%, We have 20,000 participants, allocated into 2 equal groups, with 13 weeks of data before the start of the trial, and (up to) 22 weeks afterwards. Assuming a DID design, and using pc_dd_analytic in Stata, we obtain the following: Supplier 1 (5 months, 22 weeks): mde= 0.0179 Supplier 2 (4 months, 17 weeks): mde= 0.0184 Supplier 1 (3 months, 13 weeks): mde= 0.0204
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan

Analysis Plan Documents

Pre-analysis plan

MD5: e733d3dd468a53283a45ca4ee1d1b778

SHA1: fe9831e4821be60e15964c5d35ceb40a27dd8713

Uploaded At: October 30, 2025