What GridScore measures
GridScore answers a simple question: when the grid needs help most, is this resource delivering?
Specifically, GridScore is the percentage of a distributed energy resource’s monthly energy savings that occur during the top 10% of demand hours on the grid. It is expressed as a percentage from 0 to 100.
A GridScore of 80% means that 80% of the resource’s energy savings happen during the hours when grid demand is highest. A GridScore of 10% means the savings are concentrated during off-peak hours.
Why it matters
Not all kilowatt-hours are equal. A kilowatt-hour saved during a summer afternoon peak, when the grid is strained and wholesale prices spike, is worth significantly more than one saved at 3 AM when demand is low and generation is abundant.
Traditional energy metrics treat all savings the same. GridScore differentiates resources by when they deliver value, which is exactly what grid operators, utilities, and capacity markets care about.
This has practical implications:
- Resource adequacy. Utilities need to know which DERs can be counted toward planning reserve margins. A high GridScore means the resource reliably delivers during the hours that matter for reliability.
- Program design. Program administrators can use GridScore to evaluate which types of DERs deliver the most capacity value per dollar spent.
- Competitive differentiation. Energy service providers with high GridScore portfolios can demonstrate that their assets are more valuable than portfolios with savings spread across all hours.
How it’s calculated
The calculation has three steps:
Step 1: Identify peak hours. For a given month, rank all hours by total grid demand in the resource’s region. The top 10% are classified as peak hours. For a 30-day month with 720 hours, this is the 72 highest-demand hours.
Step 2: Measure verified savings. Using meter-based M&V (via Aristotle), calculate the resource’s actual energy savings for every hour of the month. This uses a statistical baseline model that accounts for weather, occupancy, and other variables.
Step 3: Calculate the ratio. Divide the savings that occurred during peak hours by total savings for the month.
GridScore = (savings during top 10% demand hours) / (total savings) × 100If a resource saved 1,000 kWh in a month and 800 of those kWh were during peak hours, its GridScore is 80%.
What drives high and low scores
Different resource types tend to have characteristic GridScore profiles:
Typically high GridScore:
- Air conditioning efficiency improvements (savings correlate with cooling load, which correlates with peak demand)
- Demand response (dispatched specifically during peak events)
- Battery storage (discharged during peak periods)
Typically moderate GridScore:
- Heat pumps replacing resistance heating (depends on climate and whether summer or winter peaks dominate)
- Solar with smart inverters (generation correlates with afternoon peaks but drops off in evening)
Typically lower GridScore:
- Lighting efficiency (savings spread across operating hours, not concentrated during peaks)
- Baseload efficiency measures (constant savings regardless of grid conditions)
These are generalizations. Actual GridScores depend on the specific asset, its location, and the local grid’s demand profile. That’s why meter-based verification matters: modeled estimates give a rough idea, but measured data from Aristotle gives the real number.
Modeled vs. verified scores
Assets enrolled in Aristotle receive verified GridScores calculated from actual meter data. These are updated continuously as new data arrives.
Assets not yet enrolled can receive modeled GridScores estimated from the asset type, location, and typical performance characteristics. Modeled scores are useful for initial assessment but carry less certainty.
On the DER Registry, verified scores are marked with a “Verified” badge and modeled scores with a “Modeled” badge. Enrolling an asset in Aristotle upgrades its score from modeled to verified.
Open methodology
GridScore is not a proprietary metric. The calculation methodology is published as open methodology by the OpenEAC Alliance, where it is subject to peer review and public comment. Any stakeholder can review the approach, validate the math, and propose improvements.
This transparency is deliberate. A scoring system that influences investment decisions and program design needs to be auditable and trustworthy, not a black box.