ISO-NE Methodology

Modo Energy provides benchmark data for battery energy storage systems across global energy markets, applying a standardized methodology to ensure consistency and transparency across all produced Indices.

1. Introduction and rationale

The Modo Energy ISO-NE Methodology sets out how Modo Energy's battery energy storage benchmarks for ISO New England (ISO-NE) are constructed. It explains:

• what the Benchmarks measure and why this method is used (§1);
• the key terms used (§2);
• the representative asset and how values are calculated (§3);
• the revenue components included (§4);
• the data inputs, fallbacks and minimum data requirements (§5);
• the dispatch model (§6);
• how expert judgment is exercised (§7);
• how the Benchmarks are determined when data is missing or markets are disrupted (§8);
• when published values may be revised (§9);
• how the methodology is reviewed and changed (§10–11);
• and the Benchmarks' limitations (§12).

1.1 What the ISO-NE Benchmarks measure

The ME vBESS ISO-NE Benchmarks measure the simulated market revenue of a representative grid-scale lithium-ion BESS in ISO-NE, expressed per MW of rated power. The family of benchmarks is grouped by load zone and system duration so revenues can be compared across New England and across battery configurations.

Each Benchmark reflects revenue a representative asset could earn from ISO-NE's energy and ancillary service markets rather than a measurement of any individual operator.

1.2 Why ISO-NE requires a simulated benchmark

The ISO-NE Benchmark is simulated, not measured.

ISO-NE does not publish the asset-level data needed to account for the operations and revenues of individual batteries on its grid. It publishes market clearing prices for energy (locational marginal prices, LMPs) and for each ancillary service, but not per-asset dispatch, awards or revenue. By contrast, ERCOT publishes the ancillary service awards and telemetered net output of individual units, which allows Modo Energy to construct measured benchmarks for ERCOT.

1.3 Why this method is used

In the absence of asset-level data, the most direct way to estimate what a battery could earn in ISO-NE is to simulate a battery trading against the prices ISO-NE actually published. Modo Energy does this with its Global Dispatch Model (GDM), a mixed-integer linear program (MILP) that maximizes battery revenue subject to market rules and physical constraints (§6). This approach:

• uses only public ISO-NE price data, so every input can be traced to an ISO-NE publication;
• applies a fixed, disclosed asset specification, so changes in Benchmark values reflect changes in market prices rather than changes in the asset or its operator; and
• uses the same model and structure as Modo Energy's simulated benchmarks in other markets, so ISO-NE values can be compared with them.

Because the model optimizes with perfect knowledge of the day's prices, its raw output overstates what a real operator would earn. A calibration factor is applied to account for this (§6.6).

2. Definitions

3. Benchmark Construction

This section details the parameters that define the simulated battery in a Modo Energy virtual asset benchmark. The default values for those parameters in the ME vBESS ISO-NE Benchmarks are also provided.

3.1 The ME vBESS ISO-NE Benchmarks

Modo Energy produces a family of 24 benchmarks for ISO-NE. The benchmarks vary in the duration and load zone of the simulated battery:

• The durations are: 1-, 2-, and 4-hours
• The pricing nodes are all eight load zones in ISO-NE: Connecticut, Maine, NE Mass., New Hampshire, Rhode Island, SE Mass., Vermont, WC Mass.

The naming convention for this family of benchmarks is ME vBESS ISO-NE [load-zone] ([duration]H) — for example, the 2-hour benchmark for the WC Mass hub is "ME vBESS WC Mass (2H)".

This family of benchmarks are produced using the methodology described in this document, with a common set of operational and behavioral parameters. These parameters are specified below.

3.2 Representative vBESS specification

These parameters are set by Modo Energy, and are under the purview of the US modeling team. They define the representative asset and the behavior of the dispatch model. No input data (prices) are modified.

All parameters listed in §3.1 and §3.2 are customizable in user-defined vBESS benchmarks, with the exception of AS expected throughput and calibration factor.

3.3 How the Benchmark is calculated

For each operating day, the Benchmark is calculated by:

1. Running the GDM for the operating day using the representative asset (§3.2) and the input data (§5).
2. Computing simulated revenue in each modeled market, in nominal USD, as the cleared price multiplied by the simulated position in each interval.
3. Multiplying revenue in every energy and ancillary market by the calibration factor of 80% (§6.6).
4. Summing revenue across markets and dividing by rated power (100 MW) to give a value in USD/MW for the operating day.

Values for longer periods (for example, a month) are the sum of the daily values in that period.

3.4 Benchmark value representation

All Benchmark values are reported as revenue per unit of rated power, in the following units:

• USD/MW (period): total Benchmark revenue for the period (e.g. USD/MW/month).
• USD/MW/year (annualized): period revenue divided by the number of days in the period and multiplied by 365.

3.5 Publication cadence

The Benchmarks are updated daily. Modo Energy ingests ISO-NE day-ahead and real-time energy, reserve and regulation clearing prices and publishes for the most recent operating day for which the minimum data in §5.5 is available, after a short lag to allow for ISO-NE settlement and late postings.

3.6 Market evolution reflected in the Benchmark

4. Revenue Components

Modo Energy's virtual asset BESS benchmarks for ISO-NE only consider wholesale revenue opportunities: energy and ancillary service. Contracts like the CPEC or Forward Reserve Market are not considered.

4.1 Revenue components included in the Benchmarks

All ancillary products are capacity products: the battery is paid per MW held. The three reserve products are upward products delivered by discharging. Regulation is offered symmetrically: the battery holds equal upward and downward capacity and earns the regulation capacity clearing price once. ISO-NE operates no day-ahead regulation market. Energy delivered when a service is deployed moves the battery's state of charge and counts toward cycling (§6.5), but only the capacity payment is counted as revenue.

4.2 Excluded revenues and costs

• Energy Imbalance Reserve (EIR): day-ahead product introduced with day-ahead ancillary services in March 2025; not modeled.
• Forward Reserve Market (FRM): retired on March 1, 2025, before the start of the Benchmark history.
• Regulation mileage payments: real-time regulation mileage is paid per MW of movement rather than per MW of capacity; not modeled.
• Energy settlement of ancillary deployments: deployed energy affects SOC but is not separately settled.
• Capacity market (Forward Capacity Market and its successor): not a continuously cleared, publicly priced market product.
• Massachusetts Clean Peak Energy Certificates (CPEC): state-program revenue available only to qualifying Massachusetts assets; not included.
• Transmission congestion rights / congestion hedging: portfolio-specific; not modeled.
• Curtailment compensation: site-specific; not modeled.
• Network / transmission access charges: site-specific; set to zero.
• Bilateral contracts (PPAs, tolls, floors): not modeled.
• Operator fees, warranty, O&M: the Benchmark reports gross market revenue, not net of operating costs.

5. Data Inputs

5.1 Inputs and sources

The Benchmark uses five datasets, all published by ISO-NE through its web services. No other data sources are used for prices.

In addition, the model uses the parameters in §3.2 and the expected-throughput values in §5.4.

5.2 Load zones and reserve zones

Energy is priced at load-zone LMPs. Reserves clear by reserve zone: Rest of System (ROS), plus the import-constrained local zones (NEMA/Boston, Connecticut and Southwest Connecticut) when they separate. Regulation clears system-wide. Each load zone is mapped to a reserve zone:

None of the load zone aggregated pricing nodes maps to the nested Southwest Connecticut (SWCT) reserve zone. Individual pricing nodes do in custom benchmarks, however.

5.3 How inputs are combined

• All inputs are aligned to a common 5-minute grid in Eastern Prevailing Time. Hourly prices apply to each 5-minute interval within their hour.
• Energy and ancillary prices are used as published by ISO-NE. Modo Energy does not cap, filter or adjust prices, including during scarcity events.
• Revenue from each market is computed separately and then summed (§3.3).

5.4 Ancillary expected throughput

ISO-NE does not publish per-service activation data suitable for the representative asset. The model applies fixed expected-throughput values to translate ancillary capacity held into energy that moves SOC and counts toward cycling (§6.5). The values are conservative estimates, derived as reductions from the activation rates of comparable ERCOT services, applied to both the day-ahead and real-time legs of each service, and do not vary by time of day.

5.5 Data priority, fallbacks and minimum data

• Priority. ISO-NE-published prices are the only price source. Where ISO-NE corrects, resettles or republishes prices, the latest version available when the Benchmark is computed is used.
• Gaps within a day. Where an interval is missing from an otherwise available dataset, the most recent preceding value is used (or, at the start of a day, the following value).
• Minimum data. A Benchmark value is only determined for an operating day when all five datasets in §5.1 are available for that day at the load zone and reserve zone. If any dataset is absent for the day, no value is published for that day until the data becomes available.

6. Modeling Methodology

6.1 What the model solves

The GDM answers one question: given the prices observed on a given day and the physical and regulatory limits of the asset, what is the highest revenue a well-run battery could have earned?

It allocates the battery's power and stored energy across energy (day-ahead and real-time) and ancillary capacity (TMSR, TMNSR, TMOR and Regulation). Revenue is the sum of these streams net of charging cost, subject to the constraints in §6.3–6.5.

6.2 Three-step optimization

ISO-NE clears energy and reserves together in the day-ahead and real-time markets, and clears regulation in a separate real-time market. The model mirrors this with three sequential steps per day:

1. Day-ahead step (60-minute granularity): day-ahead energy, TMSR, TMNSR and TMOR are optimized together. The resulting positions are committed.
2. Real-time step (5-minute granularity): holding the day-ahead positions fixed, the model optimizes real-time energy together with real-time TMSR, TMNSR and TMOR. Deviations from the day-ahead schedule settle at real-time prices.
3. Regulation step (5-minute granularity): holding all earlier positions fixed, the model optimizes Regulation within the remaining headroom. Because regulation clears in a separate market, it is solved after energy and reserves rather than co-optimized with them.

Each step covers one operating day. The model has perfect foresight of energy and ancillary prices over the operating day.

6.3 Market stacking and physical limits

1. Power headroom: the sum of discharging ancillary positions (upward Regulation, TMSR, TMNSR, TMOR) cannot exceed rated power, and nor can downward Regulation.
2. Energy headroom: each reserve and upward Regulation position must be backed by enough stored energy, and each downward Regulation position by enough empty capacity, to deliver at full power for 1 hour.
3. Grid limits: combined charge and discharge cannot exceed the 100 MW import and export limits.
4. Daily cycling cap: total discharge throughput per day, including energy delivered through ancillary deployment, cannot exceed 1.0 cycles of usable capacity.

6.4 The battery's physical arrangement

• Round-trip efficiency (88%) is applied to charging: 1 MWh drawn stores 0.88 MWh.
• State of charge in each interval equals the previous level plus charging (net of efficiency) minus discharging, plus the expected energy flow from ancillary deployment (§6.5). The net flow within an interval is used, so offsetting trades within an interval incur no efficiency loss.
• SOC window is 0%–100%, starting and ending each operating day at 0%.
• Minimum spread of 10 USD/MWh is applied to discharge in the objective only.
• Degradation is disabled.

6.5 Capacity vs deployed energy

The battery is paid the cleared capacity price (USD/MW/h) for each MW of ancillary capacity held: the zonal reserve clearing price for TMSR, TMNSR and TMOR, and the system-wide regulation capacity clearing price for Regulation. Holding capacity reduces the power and energy available to the energy markets. The energy actually delivered when reserves are deployed, or when the regulation signal moves the asset, is not observable in ISO-NE public data. The model therefore moves SOC by the held capacity multiplied by the expected throughput for that service (§5.4). That energy counts toward cycling but is not separately remunerated.

6.6 Revenue calibration

A flat calibration factor of 80% is applied to simulated revenue from every energy and ancillary market. The factor accounts for the gap between perfect-foresight modeled revenue and what a real battery earns, which has three sources:

• Foresight: the model sees prices a real trader does not have when offers are submitted.
• Availability: real assets have outages and maintenance; the representative asset is always available.
• Execution: trading frictions and slippage.

The value of 80% is the factor Modo Energy uses across its Eastern US benchmarks. It is informed by comparing perfect-foresight GDM revenues with realized revenues in markets where asset-level data is published: ERCOT and Great Britain. It is applied after dispatch is optimized, so it does not change dispatch or the trade-offs between markets; it scales the result.

7. Expert judgment

7.1 Where judgment is exercised

The Benchmark is determined by the model from published prices; there is no discretionary adjustment of individual daily values. Expert judgment is exercised in setting the methodology itself, specifically:

• the representative asset specification and minimum spread (§3.2);
• the ancillary expected-throughput values (§5.4);
• the sequencing of Regulation after energy and reserves (§6.2); and
• the calibration factor (§6.6).

7.2 Keeping judgment consistent

These parameters are fixed in the GDM's version-controlled codebase and applied identically to every operating day and every Benchmark. A parameter is changed only through a code change that is reviewed by a member of Modo Energy's modeling team other than its author and passes automated tests before release. Changes to parameters are treated as methodology changes under §11.

8. Market stress, disruption and missing data

Modo Energy's current practice is:

• Late or missing data. Where ISO-NE data for an operating day is late or incomplete, Modo Energy waits for the data rather than estimating it. No value is published for that day until the minimum data in §5.5 is available.
• Extreme prices. Prices during scarcity, stress or other extreme events (for example, winter cold snaps) are used as published. The Benchmark is intended to reflect those conditions, so they are not removed or capped.
• Market suspension or rule changes. If ISO-NE suspends a market or changes its design, the Benchmark reflects the prices ISO-NE publishes for the affected period. Changes to the methodology needed in response are handled under §11.

9. Revisions to published values

Published values may be revised, for example where:

• an error is identified in Modo Energy's processing of the input data; or
• a methodology change (§11) is applied to historical periods.

10. Methodology review

The methodology is reviewed internally by Modo Energy, including when ISO-NE changes its market design (for example, if Energy Imbalance Reserve is considered for inclusion), when new data becomes available (for example, ISO-NE-specific ancillary activation data), or when changes are made to the GDM. Any changes that result from a review are made under §11.

11. Methodology changes

Changes are classified as material or non-material. A change is material where it may meaningfully affect benchmark values, the inputs used, or what the benchmark measures. Material changes are announced with not less than 30 days' written notice before they take effect, together with an impact analysis.

Non-material changes are corrections and clarifications that do not alter the substance of the methodology. They may be made without prior consultation and are disclosed as soon as practicable.

Emergency changes needed to protect the integrity of the benchmark or to comply with law may be made without prior consultation, with notice given as promptly as circumstances permit.

All changes are recorded, with their effective date, in the change log (Appendix I).

12. Limitations

• Simulated, not observed. The Benchmark is not derived from transactions or the revenues of any real asset in ISO-NE, and cannot be validated directly against ISO-NE asset revenues because ISO-NE does not publish them.
• Foresight and calibration. The model optimizes with perfect foresight. The 80% calibration factor is a single flat value derived from ERCOT and Great Britain, not from ISO-NE. Actual foresight losses vary over time and between markets.
• Price-taker assumption. The representative asset is assumed not to affect prices and can clear its full offered capacity in any market. ISO-NE's regulation requirement is small and its reserve requirements are modest relative to its energy market, so in practice a single asset or a growing battery fleet may not clear the volumes modeled or may depress clearing prices. The Benchmark may overstate ancillary revenue where markets are thin or concentrated.
• Regulation. Only the regulation capacity payment is modeled. The service (mileage) payment, which depends on signal tracking, is not.
• Excluded products. Energy Imbalance Reserve is not modeled, so revenue available from it is not captured.
• Ancillary activation. Expected-throughput values are derived from ERCOT, not ISO-NE, and do not vary by time of day.
• Asset assumptions. The asset is always available, does not degrade, and starts and ends each day empty. Real assets differ on each point.
• Excluded revenues. The revenues and costs listed in §4.2, including capacity market and Clean Peak revenue, are not captured.

Appendix I — Change log

Disclaimer

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