We've developed a methodology that reveals what percentage of your electricity is actually renewable—measured half-hour by half-hour. Some suppliers delivering "100% renewable" electricity score as low as 55% when we apply temporal matching.
This post is a high-level explainer of the methodology behind the Matched Clean Power Index, covering the data sources and key calculations.
Calculating hourly matching
Calculating half-hourly renewable matching scores is conceptually simple. You need three things:
- Half-hourly demand: How much electricity did the supplier's customers use in each 30-minute period?
- Renewable ownership: Which wind farms, solar assets, and other renewable generators does the supplier have contracts with or REGO certificates from?
- Half-hourly production data: How much electricity did each of those renewable assets actually generate in each 30-minute period?
With perfect data, you'd simply compare demand against renewable production for each half-hour. If customers used 100 MWh at 7pm and the supplier's renewable generators produced 60 MWh at that time, that's 60% matching for that period.
The challenge? This data exists but it's scattered across different systems that weren't designed to work together. That's where Matched comes in.
The three data sources we connect
Our methodology combines three public datasets that have never been connected before:
- Elexon settlement data: For every 30 minute period, Elexon records how much electricity is passing through meters. We download these "S0142" files which show actual power being produced or consumed.
- Ofgem's REGO database: For every unit of renewable energy generated in Great Britain, there is a certificate ("REGO) which details the wind farm or solar park that generated it, the supplier that claimed the energy, and when the energy was produced (though only at monthly granularity). We take a snapshot after each compliance period ends.
- National Grid mix: The system operator publishes the breakdown of electricity generation of the overall grid every 30 minutes—how much came from wind, solar, gas, nuclear, etc. This gives us the temporal patterns of renewable generation.
Tracking demand: the foundation
First, we identify which Balancing and Settlement Code (BSC) parties belong to each supplier. British Gas might trade as "CENTRICA" while Octopus uses "MERCURY" and "FRST01"—we map all these relationships.
Each BSC party manages at least 14 metered entities called Balancing Mechanism Units (BMUs)—one for each geographical region of the British grid. Consumer demand is represented as negative BMU energy volumes - we aggregate all negative volumes across all BMUs to calculate total demand.
One complexity: for some suppliers, BMU energy volumes also include the output of small-scale wind and solar generators. Good Energy, for instance, shows 26% less demand than reality because some renewable generators offset customer demand in their BMU data. We calibrate for this effect specifically, usually using Elexon's quarterly reports of gross demand.
Adding half-hourly generation to REGO certificates
This is where it gets interesting. REGO certificates tell us a supplier owns the claim on, say, 100 MWh from "Walney Wind Farm" generated in March. But when in March? Morning or evening? Windy days or calm ones? Without this information, we can't calculate a half hourly matching score.
We get to a half hourly resolution for the production output in two ways:
Method 1: Direct metering
Large generators (over 50 MW) must register as BMUs, creating settlement data we can track on a half-hourly basis. We've built a matching engine that connects REGO generator names to BMU codes using:
- Fuzzy name matching (accounting for variations like "Valley Wind Farm" vs "The Valley Wind Farm LTD")
- Fuel type validation (both must be wind, solar, etc.)
- Capacity cross-checking (to check that the size of the assets match)
- Monthly volume verification (to check that the energy output of the assets are the same)
When we successfully match a REGO generator to a BMU, we use that BMU's actual half-hourly output. If a supplier owns 10% of that generator's March REGOs, they get credited with 10% of its half-hourly generation throughout March.
Method 2: Grid mix inference
Smaller generators—rooftop solar, community wind turbines—aren't metered at the BMU level. For these, we use the national grid mix as a proxy.
When a supplier has REGO certificates (i.e. for solar or wind generation), we spread them across each half-hour period based on how much renewable energy was generated nationwide during those times.
Example: A supplier has 100 MWh of solar certificates for March. We look at the UK's total solar generation for every half-hour in March. If 2pm on March 15th had 0.05% of March's total solar generation, we allocate 0.05% of the supplier's REGO certificates (0.05 MWh out of 100 MWh) to that specific half-hour slot.
This preserves average generation patterns—solar certificates generate during daylight, wind certificates follow weather patterns.
The matching calculation
For each half-hour period, we now have:
- D: Customer demand in MWh
- R: Renewable supply from REGO certificates in MWh
We calculate:
- Deficit = max(0, D - R) — the shortfall when demand exceeds renewable supply
- Surplus = max(0, R - D) — excess renewables that can't be used
The crucial rule: surpluses can't be banked. Excess wind power at 3am doesn't offset fossil fuel demand at 7pm.
The matching score for any period is: M = 1 - (Total Deficit / Total Demand)
We calculate this at three granularities:
- Half-hourly: Sum all deficits and demand across the year, preserving 30-minute resolution
- Monthly: Sum within each month first, then across the year
- Yearly: Simple annual totals (this should roughly match suppliers' Fuel Mix Disclosures)
Real example: Bryt Energy
Let's walk through actual numbers for Bryt Energy (compliance period 2023-24):
Demand: 3.4 TWh total, tracked half-hourly across their BSC party "BULLION"
REGOs: 3.6 TWh from 236 generators. 24% directly metered at the BMU level. This is lower than most high-performers because Bryt specialises in exclusive offtake from smaller assets.
Results:
- Half-hourly matching: 78%
- Monthly matching: 90%
- Yearly matching: 100%
That 12-point gap between half-hourly and monthly is a material difference in matching. Even a good performer like Bryt can't perfectly match wind and solar generation to evening demand peaks.
Validation and limitations
We validate our methodology by comparing our annual calculations with suppliers' Fuel Mix Disclosures (FMDs) which are the obligatory annual disclosures that suppliers must make. FMDs don't account for all of a suppliers' demand so we expect them to be a little larger than our annual matching score, but nonetheless, for most suppliers, we are within 1-2%, confirming our approach captures real supply and demand patterns.
Future enhancements to the methodology will include:
- Regional specificity, where we infer the half-hourly shape for small generators
- Storage, which can temporally shift clean power
- Nuclear
The complete picture
When we apply this methodology to 26 major UK suppliers, matching scores range from 0% to 88% renewable on an hourly basis.
The top five suppliers are over 2/3rds renewable on a half-hourly basis; they're successfully procuring renewable power aligned with customer demand patterns.
Others, despite holding enough REGOs for "100% renewable" claims, show dramatic temporal mismatches. Their REGO certificates cluster in summer while demand peaks in winter evenings.
Try it yourself
We've worked closely with a number of suppliers to validate our calculations against their internal data, and invite others to do the same. Our complete methodology document includes:
- More detailed calculation explainers
- Examples of REGO-to-BMU asset matching
- Worked calculations
On October 27th, we published the full set of scores so that consumers can see how much renewable energy they are really receiving from their supplier. You can view the complete index at matched.energy/clean-power-index.
Please get in touch, if you'd like to discuss our methodology or the scores.