This document has been prepared by the OpenEAC Alliance, a collaborative group of energy engineering and measurement and verification professionals focused on creating transparent and standardized methods to measure the environmental and capacity impacts of demand-side interventions. These methodologies are designed to align with best practices and reflect input from industry experts. The OpenEAC Alliance aims to enhance accountability and consistency in documenting energy, carbon, and capacity contributions from distributed energy resources (DERs).

About the OpenEAC Alliance

The OpenEAC Alliance is an organization of professionals dedicated to standardizing the measurement and verification of DERs. By focusing on clarity and rigor, the Alliance aims to overcome challenges that have hindered the proper valuation of DERs for their climate and capacity benefits. Central to this effort is the Energy Attribute Certificate (EAC), a standard that provides a detailed, auditable record of energy project impacts, including attributes such as type, timing, and location of energy production or reduction.

The OpenEAC Alliance collaborates to develop open-source methodologies, ensuring that DER projects can be accurately measured, verified, and valued in a way that promotes trust and drives market adoption.

Introduction

This document introduces a standardized approach for quantifying the capacity contribution of savings-producing DER projects (e.g., energy efficiency retrofits, building shell improvements, electrification) for the purposes of generating Energy Attribute Certificates (EACs) with capacity attributes.

The core challenge addressed by this methodology is the measurement gap between a DER’s physical capacity (what a device or retrofit can do under ideal conditions) and its available capacity (what the grid can reliably count on during peak demand). Physical capacity alone is insufficient for capacity markets; what matters is the demand reduction that is actually delivered during the hours when the grid needs it most.

Scope

This document outlines the methodology, assumptions, and data sources used to quantify the capacity contribution of savings-producing DER projects — those that reduce demand relative to a counterfactual baseline. The primary purpose is to specify how hourly, weather-normalized demand reductions can be translated into capacity-valued Energy Attribute Certificates (EACs).

Scope: This methodology applies to DER projects that produce persistent demand reductions through physical changes to buildings or equipment, including but not limited to:

• Energy efficiency retrofits (building shell, HVAC, controls)

• Heat Pump projects replacing less efficient electric HVAC systems

• Weatherization and envelope improvements

• Demand Response events

Normative References

The following documents are essential for the application of this specification. For references with no date, the latest edition applies:

• PJM - Manual 18: Capacity Market

• ERCOT - 2024 Biennial ORDC Report

• CAISO/CPUC - 2026 RA Slice of Day Guide

• ISO-NE - Action During a Capacity Deficiency

• NYISO - System Conditions

• MISO - Wind and Solar Capacity Credit Report PY 2024-2025

• MISO - DR and Emergency Resources Reforms (2025)

• NYISO - ICAP Manual, Section 4 (Intermittent Resource UCAP)

Terms and Definitions

• Evaluation Period

  • A period of time (can be defined monthly, seasonally, annually) at which capacity values and subsequent calculations are measured.

• Installed Capacity/Asset Peak Capacity

  • The nameplate or measured peak demand reduction of a DER project under design or observed conditions. For savings-producing resources, this is the maximum hourly demand reduction observed relative to the counterfactual baseline over the Evaluation Period.

• Asset Available Capacity

  • The power supplied by the DER project during a particular period of time. This can either be based on an amount of power generated or an amount of power saved.

• Capacity Factor (CF)

  • A factor between 0 and 1 (expressed as a percentage) that represents the fraction of the Asset Capacity that was made available at a particular period of time.

• Coincident Peak (CP)/At-Risk Hours

  • The set of hours that a given system particularly values demand reduction or generation to take place (such as peak demand, peak price, operational constraints, or minimum reserve margin).

• Coincident Peak Contribution (MWh)

  • The portion of the Asset Capacity that has been supplied to the system over a period of CP Hours.

• Coincident Peak Contribution Ratio

  • The ratio of the average Coincident Peak Contribution to the average Asset Available Capacity.

• Effective Load Carrying Capability (ELCC)

  • The fraction of an Asset’s (or portfolio of Assets’) Peak Capacity that the grid can reliably count on during At-Risk Hours, derived from a probabilistic simulation across many possible future At-Risk Hour scenarios. Expressed as a percentage of Asset Peak Capacity.

Methodology

1 Coincident Peak Contribution

1.1 Sum the hourly delivered Coincident Peak Contribution.

2 ELCC Calculation

2.1 The ELCC calculation can be performed for a single asset, or a portfolio of assets. All assets must exist within the same grid geography that is used to determine the CP hours.

2.2 Collect each asset’s savings during each CP hour.

2.3 Construct a covariance matrix based on the savings at each hour.

2.4 Build a multivariate normal distribution based on the mean and covariance matrix and draw thousands of random samples. Each sample is one simulated “peak hour” giving a savings value for every project simultaneously.

2.5 Sum across projects for each simulated hour to get a portfolio total.

2.6 Choose a confidence level that represents your minimum acceptable reliability (the percentage of peak hours that the portfolio needs to deliver at least a certain amount).

2.7 Based on the samples, find the portfolio output at that threshold. This is the value the portfolio exceeds X% of the time during peaks, and represents the amount of firm, dispatchable capacity the portfolio is equivalent to. Divide this by the total Asset Peak Capacity to get a percentage.

3 Coincident Peak Hours Determination Methods

3.1 Gross Load Peak: The system peak demand of a relevant Balancing Authority (BA) or individual Utility.

3.1.1 Examples: PJM 5CP, ERCOT 4CP, ISO-NE 1CP, NYISO 1CP/12CP, MISO 8 Seasonal Peaks

3.2 Slice-of-Day: The 24 hours that contain the peak gross load hour.

3.2.1 Example: CAISO/CPUC Monthly SOD

3.3 System Reserve/Capacity Price Signal: A price signal that represents the capacity constraints.

3.3.1 Example: ERCOT ORDC Price Adders (such as the Real-Time Online Reserve Price Adder)

3.4 Locational Marginal Pricing: Using energy market prices as a proxy for grid stress during periods of tight supply or transmission congestion.

3.4.1 Examples: PJM Shortage Pricing, ERCOT System Lambda + ORDC Adders

3.5 Pre-Defined Period: The coincident peak is defined by a predetermined set of hours based on historical peak patterns and regulatory definitions. This option is simpler to implement but less precise. The following are a few example methods of pre-defining these periods.

3.5.1 Temporal Periods

3.5.1.1 Summer peak period: (such as Weekday non-holiday hours from 2:00 PM to 7:00 PM local time, June through September).

3.5.1.2 Winter peak period: (such as Weekday non-holiday hours from 5:00 PM to 9:00 PM local time, December through February).

3.5.2 Example: NYISO defines summer peak hours and winter peak hours for intermittent resource capacity accreditation.

3.6 Temperature-Based:

3.6.1 Include hours where outdoor temperature exceeds the 90th percentile (summer) or falls below the 10th percentile (winter) for the measurement period.

3.7 New Large Load Impact: Combine the gross load peak approach with a modeled load for a new large power consumer and use that to determine the coincident peak hours.

3.8 Ensemble Method: Combine any set of the approaches above.

Appendix

Appendix 1: ELCC Calculation Example

There are 4 DER assets from a single developer and 5 At-Risk hours identified over the past summer. Each cell shows the demand reduction (kW) that asset delivered during that CP hour. The total installed capacity is 145 kW.

At-Risk Hour	Asset A (50 kW nameplate)	Asset B (30 kW nameplate)	Asset C (40 kW nameplate)	Asset D (25 kW nameplate)	Portfolio Total
1	42	18	35	20	115
2	38	25	28	22	113
3	45	12	37	15	109
4	30	22	32	24	108
5	48	20	38	18	124

Compute the mean and standard deviation of each asset’s delivered reduction across the five at-risk hours, using sample statistics (dividing by n − 1):

Stat	Asset A	Asset B	Asset C	Asset D
Mean	40.6	19.4	34.0	19.8
Std Dev	6.99	4.88	4.06	3.49

Then compute the sample covariance matrix. Its diagonal is each asset’s variance, so it is the square of the standard deviation row above:

	Asset A	Asset B	Asset C	Asset D
Asset A	48.8	−18.05	20.25	−21.10
Asset B	−18.05	23.8	−15.00	14.35
Asset C	20.25	−15.00	16.5	−10.75
Asset D	−21.10	14.35	−10.75	12.2

Feed the mean vector and the covariance matrix into a multivariate normal distribution. Draw random samples. Each sample produces a savings value for all 4 assets simultaneously, respecting their individual variances and co-movement.

Here is an example of what some of the samples might look like:

Draw #	Asset A	Asset B	Asset C	Asset D	Portfolio Total
1	44	15	36	17	112
2	35	24	30	23	112
3	48	11	39	14	112
4	32	26	28	25	111
5	41	19	34	20	114
…	…	…	…	…	…

For each of the draws, sum the 4 asset values to get a portfolio total.

Choose a % reliability, in this case we will use 85%. This means that the portfolio must deliver at least this much 85% of the time during peaks.Sort all portfolio totals from low to high. The value at the 15th percentile is the ELCC.

Percentile	Portfolio Total (kW)
5th	100
10th	104
15th	107
25th	110
50th (median)	114
Mean	113.8

The portfolio delivers at least 107 kW during 85% of simulated peak hours. The portfolio converts 145 kW of nameplate capacity into 107 kW of firm equivalent capacity, or 107/145=73.8% ELCC.

Appendix 2: At-Risk Hours Examples and Context

The method used to identify At-Risk Hours fundamentally shapes the capacity value assigned to a resource. Different ISOs and utilities use different approaches — some based on observed load peaks, some on price signals, some on operational emergency declarations, and some on all-hours frameworks.

Gross Load Peak

The system peak demand of a relevant Balancing Authority (BA) or individual Utility. ISOs and even different markets within them can create different sets of hours based on gross load peaks, based on monthly, seasonal, or annual time periods.

Examples:

• PJM 5CP: PJM identifies the 5 highest gross load hours during each summer period (June–September). A resource’s Peak Load Contribution (PLC) is its average output during those 5 hours. These same hours form the basis for allocating capacity obligations to load-serving entities. PJM also uses Performance Assessment Intervals (PAIs) — 5-minute intervals triggered during emergency actions — to assess capacity resource performance under its pay-for-performance framework.

• ERCOT 4CP: ERCOT identifies the single highest 15-minute system peak in each of the four summer months (June–September). These 4 coincident peaks are used purely for transmission cost allocation — not for capacity accreditation, since ERCOT operates an energy-only market with no formal capacity product.

• ISO-NE 1CP: ISO-NE uses a single annual coincident peak for capacity cost allocation. The system peak hour determines each LSE’s share of regional capacity obligations.

• NYISO 1CP / 12CP: NYISO uses the single coincident peak during July–August for ICAP capacity tagging. Separately, 12 monthly coincident peaks (one per month) are used for transmission cost allocation.

• MISO 8 Seasonal Peaks: For wind Seasonal Accredited Capacity (SAC) allocation, MISO uses the top 8 coincident system peak hours per season across the most recent 3 planning years.

Slice-of-Day (SOD)

All 24 hours of the peak gross load day in a given month. Rather than focusing on a handful of peak hours, this method evaluates resource contributions across every hour.

Examples:

• CAISO/CPUC Monthly Slice-of-Day: Starting in 2025, California’s RA program requires LSEs to demonstrate sufficient capacity for all 24 hours on CAISO’s “worst day” each month — defined as the day containing the hour with the highest coincident peak load forecast. Solar and wind resources are assigned monthly 24-hour profiles based on an exceedance methodology applied to historical production data. This replaced the prior peak-hour-only framework to better capture the reliability needs of a grid with high solar penetration, where risk has shifted to evening and net-peak hours.

System Reserve / Capacity Price Signal

A price signal that represents real-time or day-ahead capacity constraints on the system. When reserves tighten, these price adders rise, signaling scarcity.

Examples:

• ERCOT ORDC Price Adders (RTORPA/RTOFFPA): ERCOT’s Operating Reserve Demand Curve (ORDC) calculates real-time price adders based on the level of online reserves (RTOLCAP) and offline reserves (RTOFFCAP). As RTOLCAP declines, the Real-Time On-Line Reserve Price Adder (RTORPA) increases — reflecting the rising probability of load shed. The adder is the product of the loss-of-load probability at that reserve level and the Value of Lost Load (currently $5,000/MWh). Additional price floors of $10/MWh (RTOLCAP ≤ 7,000 MW) and $20/MWh (RTOLCAP ≤ 6,500 MW) were implemented in 2024 as a bridge mechanism.

• PJM Shortage Pricing: PJM applies reserve penalty factors when operating reserve requirements are violated in the real-time market, causing LMPs to spike. These scarcity-driven price signals indicate hours when the system is capacity-constrained.

Locational Marginal Pricing (LMP)

Using energy market prices as a proxy for grid stress. Hours with elevated LMPs indicate periods when the marginal cost of serving load is high — often correlated with tight supply conditions, transmission congestion, or scarcity.

Examples:

• PJM LMP Spikes During Emergency Procedures: During emergency procedures, PJM applies reserve constraint penalty factors that can push real-time LMPs to $1,000–$2,000+/MWh. These price signals coincide with PAIs and reflect the locational and temporal value of capacity.

• ERCOT System Lambda + ORDC Adders: In ERCOT, the settlement price is the sum of the base energy price (System Lambda) plus the ORDC price adder (RTORPA). During scarcity events, this composite price can approach or hit the System-Wide Offer Cap ($5,000/MWh), making high-LMP hours a direct indicator of at-risk conditions.

• NYISO Zonal Pricing During Thunderstorm Alerts: During TSA events, transmission constraints into New York City cause Zone J and K real-time LMPs to spike dramatically — 5-minute prints have reached $8,000/MWh — as local generation must ramp up to replace restricted imports.

Pre-Defined Period

The At-Risk Hours are defined by a predetermined set of hours based on historical peak patterns and regulatory definitions. Simpler to implement but less precise than dynamic methods.

Temporal Periods

Fixed clock-hour windows by season.

Examples:

• NYISO Intermittent Resource Peak Windows: NYISO defines summer peak hours as 2:00 PM – 6:00 PM (June–August) and winter peak hours as 4:00 PM – 8:00 PM (December–February). Average production during these windows over the Prior Equivalent Capability Period determines the UCAP value for intermittent resources.