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

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

• the representative asset the Benchmark is built on (§2);
• the revenue components that make up simulated revenues (§3);
• the data inputs that feed the simulation (§4); and
• the optimization model that simulates dispatch (§5).

1.1 What the ISO-NE Benchmark represents

The ME vBESS ISO-NE Benchmarks represent the simulated revenue performance of grid-scale lithium-ion BESS in ISO-NE. The family of benchmarks is grouped by system duration so revenues can be compared across battery configurations. Each Benchmark reflects revenue a representative asset could earn 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.

The RTO does not publish the requisite asset-level data necessary to reliably account for the operations and revenues of individual generators on its grid. As a point of comparison, ERCOT publishes the ancillary service awards and telemetered net output of individual units. This data, along with the market clearing service prices and locational marginal prices, allows Modo Energy to create reliable benchmarks for ERCOT's grid-scale batteries.

ISO-NE does not make equivalent asset-level data publicly available. ISO-NE publishes market clearing prices for the energy markets (locational marginal prices; abbreviated LMP) and for each ancillary service (the market clearing price, or MCP), but not per-asset dispatch or revenue.

To benchmark under these conditions, Modo Energy simulates the dispatch of a representative battery against publicly available ISO-NE market data, using its Global Dispatch Model (GDM) — a mixed-integer linear program (MILP) that maximizes battery revenue subject to market and physical constraints.

2. Benchmark Construction

2.1 The ME vBESS ISO-NE Benchmarks

Modo Energy currently produces a family of three benchmarks for ISO-NE:

All three benchmarks are constructed using the same published methodology, with differences only in the duration parameter of the representative asset. Each duration is published at the eight ISO-NE load zones (§4.1).

2.2 Representative BESS specification

Key modeling choices:

• Round-trip efficiency losses are applied to the charging flow only.
• State of charge returns to its start-of-day level at the day boundary, so each day solves independently and is comparable.
• Degradation is disabled to keep the asset's duration fixed across the whole history.

2.3 How the Benchmark is calculated

The Benchmark is calculated by:

1. Running the GDM over the assessed period using the representative asset (§2.2) and the input data (§4.2).
2. Computing simulated revenue across each modeled market, in nominal USD.
3. Summing revenue per settlement period and normalising by rated power (100 MW) to produce a benchmark value in USD/MW per period.
4. Applying a post-hoc calibration factor of 80% to the computed energy and ancillary service revenues. This value accounts for the perfect-foresight nature of the GDM, and brings the simulated energy revenues in-line with realized ones. The 80% factor is the Modo Energy Eastern US standard, informed by historical analysis of the GDM's performance in regions that publish asset-level data (ERCOT and Great Britain).

Because the haircut is applied after the dispatch solve, it does not change the optimized dispatch or the trade-offs between markets.

2.4 Benchmark value representation

All Benchmark values are reported as net revenues per unit of rated power. Revenue and Benchmark values can be represented using 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.

2.5 Publication cadence and revisions

The Benchmarks update daily. Modo Energy ingests ISO-NE energy and ancillary service clearing prices each day for both the day-ahead and real-time markets, and publishes for the most recent settled delivery day, after a short lag for settlement and any late ISO-NE postings. ISO-NE market data is timestamped in Eastern Prevailing Time (local New England time, observing daylight saving) and the benchmark follows this convention.

Published values may be revised if ISO-NE corrects or resettles prices for a covered period, if better source data materially improves a historical value, or if a methodology change (§7) applies retrospectively. Revisions are logged with the affected period and reason, and communicated per §7.2.

2.6 Market evolution reflected in the Benchmark

3. Revenue Components

3.1 Revenue components included in the Benchmarks

The Modo Energy ISO-NE Benchmarks capture the primary revenue opportunities available to a representative BESS in ISO-NE's wholesale and ancillary markets. The table below details each component, the market from which it is modeled, and the price signal used.

All four ancillary products are capacity products: the battery is paid per MW held. The three reserve products (TMSR, TMNSR, TMOR) are upward products delivered by discharging, so the model offers them only against discharge headroom and stored energy. Regulation is symmetric and bidirectional: the asset offers a single regulation capacity and earns the capacity clearing price on it, whether the signal moves it up or down. Since 1 March 2025 the reserves clear both day-ahead (hourly) and in real time (5-minute); regulation clears in real time only, as ISO-NE operates no day-ahead regulation market. Energy delivered when a service is activated moves the battery's state-of-charge and counts toward cycling (§5.5), but the revenue attributed to that market is the capacity payment.

3.2 Excluded revenues

There are revenues and costs associated with operating a grid-scale battery which are not included in the Modo Energy ISO-NE Benchmark:

• Energy Imbalance Reserve (EIR): day-ahead product introduced with the day-ahead ancillary services market (March 2025). Not modeled in the current benchmark; under review for inclusion (§7).
• Regulation service payment: ISO-NE pays regulation resources a capacity payment plus a service payment for mileage/tracking. How well an asset tracks the regulation signal is asset-specific and unobservable in public data, so the Benchmark models the capacity payment only.
• Forward Reserve Market (FRM): ISO-NE's forward procurement of TMNSR and TMOR. Retired 1 March 2025 with the introduction of day-ahead ancillary services; predates the published benchmark history.
• Capacity market (Forward Capacity Market / capacity auction reform): cleared in forward capacity auctions (transitioning to prompt, seasonal auctions under ISO-NE's capacity auction reform), not a continuously cleared, publicly priced energy-market product.
• Massachusetts Clean Peak certificates (CPEC): state-program revenue available only to qualifying Massachusetts-located assets.
• Financial transmission rights / congestion hedging: portfolio-specific; not modeled.
• Curtailment compensation: site-specific; not modeled.
• Network / transmission access charges: site-specific; default to zero.
• Bilateral contracts (PPAs, tolls, floors): do not affect optimal dispatch except in some co-located cases.
• Operator fees, warranty, O&M: the Benchmark reports gross market revenue, not net-of-OpEx.

4. Data Inputs and Use of Discretion

4.1 Sources

The Benchmark is built exclusively from publicly available ISO-NE market data, sourced from ISO-NE's web services. Five datasets are used: hourly day-ahead and 5-minute real-time energy prices (LMP) for the eight ISO-NE load zones; hourly day-ahead ancillary service clearing prices; 5-minute real-time reserve clearing prices by reserve zone; and 5-minute real-time regulation clearing prices.

There are configurable parameters set by Modo Energy (§2.2). These parameters only determine the constraints and behavior of the dispatch model. The data itself is not altered before ingestion by the model.

ISO-NE settles energy at locational marginal prices, which decompose into marginal energy, congestion, and loss components. Energy LMPs are published at nodal and load-zone level; the Benchmark uses load-zone prices. Reserve prices 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 published benchmark maps its load zone to a reserve zone:

Regulation is procured system-wide and shares a single clearing price across all locations; reserves clear at each location's reserve zone. No published location currently maps to the nested Southwest Connecticut (SWCT) zone.

4.2 Use of discretion

Discretion is applied within predefined parameters and subject to internal governance. Two choices are material to ISO-NE:

• Load-zone and reserve-zone selection. The published benchmarks are run at the eight ISO-NE load zones. Each takes energy prices at its zonal LMP and reserve prices at its mapped reserve zone (§4.1).
• Ancillary expected throughput. ISO-NE does not publish a per-service expected-activation series suitable for the representative asset. The model therefore applies fixed expected-throughput assumptions per service to translate held capacity into the energy that moves state of charge and counts toward cycling (§5.5). These are conservative best-guess values, derived as haircuts from the activation rates of comparable ERCOT services (regulation, RRS, ECRS, non-spin), with no intra-day variation. They are reviewed under §7 as ISO-NE-specific data accumulates.

The assumed throughput values, applied to both the day-ahead and real-time legs of each service, are:

5. Modelling Methodology

5.1 What the model solves

The GDM answers one practical 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 two families of opportunity:

• Energy — the ISO-NE day-ahead and real-time energy markets, earning the LMP on discharge and paying it on charge.
• Ancillary capacity — the TMSR, TMNSR, and TMOR reserves plus Regulation (up and down), where the battery is paid to hold capacity available for ISO-NE.

Revenue is the sum of these streams net of charging cost, subject to constraints that rule out positions a physical battery could not execute (§5.4).

5.2 Co-optimization and sequencing

ISO-NE clears energy and reserves together in a single co-optimized scheduling step in the day-ahead market (which, since March 2025, produces hourly TMSR, TMNSR, and TMOR awards alongside energy) and again in real time. Regulation clears in a separate market. The dispatch model mirrors this structure with four sequential solves per day:

1. Day-ahead: hourly day-ahead energy co-optimized with day-ahead TMSR, TMNSR, and TMOR.
2. Regulation (hourly): a regulation schedule solved as its own step, taking the day-ahead energy and reserve schedule as given — because regulation clears in a separate market, a price-taking asset cannot co-optimize across it, and solving it sequentially prevents the model from cherry-picking between products.
3. Real-time: 5-minute real-time energy co-optimized with real-time TMSR, TMNSR, and TMOR, re-optimizing around the day-ahead schedule with deviations settling at real-time prices.
4. Regulation (real-time): the regulation position refined at 5-minute granularity, again as its own sequential step.

• Granularity: 60 minutes for day-ahead, 5 minutes for real-time. These align with ISO-NE's clearing schedule.
• Foresight: there is perfect foresight of energy and ancillary prices over the optimization horizon.

5.3 Market stacking and physical limits

ISO-NE lets a battery hold simultaneous positions across energy and the ancillary products, subject to physical limits. The model enforces:

1. Headroom for ancillary delivery — each timestep must have enough discharge headroom and stored energy to deliver the reserves sold. TMSR and TMNSR require 10 minutes of stored energy, and TMOR requires 30 minutes. Headroom for regulation in both directions is required as well. Reserves are discharge-side products only: the model earns no reserve revenue against charging headroom.
2. Grid limits — the combined charge must be less than the import limit, and combined discharge must be below the export limit. Both limits are set at 100 MW. Given that the rated power of the simulated battery is 100 MW, this means that grid limitations do not place a limit on the battery's operations.
3. Daily cycling cap — total discharge throughput per day must be less than 1.0 cycles times usable capacity, counting both wholesale discharge and energy delivered through ancillary activation (warranties are written on total throughput, not on the market sold into).

5.4 The battery's physical arrangement

• Round-trip efficiency (88%) applied to charging: 1 MWh drawn stores 0.88 MWh.
• Continuous state of charge — each timestep's stored energy equals the prior level plus charging (net of efficiency) minus discharging, plus the expected energy flow from ancillary activation (§5.5). The adjustment uses the net flow within a timestep, so paper trades that net out incur no efficiency penalty.
• SOC window — constrained to 0%–100% of capacity; terminal SOC is fixed at the start-of-day level so each day is self-contained.
• Cycling — capped at 1.0 cycles/day (§5.3).

Degradation is disabled to keep duration constant across the history (§2.2).

5.5 Capacity vs activated energy

All modeled ISO-NE ancillary products are capacity products: the battery is paid to hold capacity available. The asset earns the cleared capacity price (USD/MW/h), which is 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 SOC available to the energy market.

Being activated to deliver energy is distinct. The energy delivered when reserves are deployed, or when the regulation signal moves the asset, varies by asset, by day, and by time of day. In ISO-NE that quantity is unobservable in public data. The Modo Energy ISO-NE Benchmark counts the capacity payment as benchmarked revenue, and does not separately remunerate the energy delivered. However, energy imports and exports do affect the asset's SOC and count towards cycling.

To capture activation's impact on a battery's state of charge, the model holds a contracted volume each timestep and applies an expected-throughput fraction per product. In this context, "expected-throughput fraction" is the share of held capacity expected to be exported/imported within that timestep. These fractions are conservative best-guess values (§4.2): regulation carries the highest expected throughput; the ten- and thirty-minute reserves carry very little.

5.6 Locational pricing

ISO-NE is nodal, and the published benchmarks settle energy at load-zone LMPs. Because LMPs embed congestion and losses, zone choice materially affects energy revenue; the published benchmarks span the eight ISO-NE load zones (§4.1). Reserve prices clear by reserve zone and do not vary by location within a zone (Connecticut and NEMA/Boston carry their local zone prices; all other published locations take the Rest-of-System price); regulation clears system-wide.

5.7 Revenue calibration

Modo Energy's simulated ISO-NE Benchmark applies a flat 80% calibration factor to the energy and ancillary service revenues produced by the dispatch model. This factor is meant to bridge the gap between perfect-foresight modeled revenue, and what a real battery earns. That gap has three sources:

• Foresight gap: the model sees prices a real trader does not have at gate closure.
• Availability gap: real assets have outages, maintenance, and retest windows.
• Execution gap: trading frictions and slippage.

The 80% factor is the Modo Energy Eastern US standard, informed by analysis of BESS fleets in regions with asset-level data (ERCOT and Great Britain), pending an ISO-NE-specific observed-versus-modeled benchmark.

6. Governance and Compliance

Modo Energy is committed to transparency by providing detailed explanations of calculation methodologies, revenue components, and benchmark updates. All key elements of the methodology are publicly available, ensuring stakeholders can fully understand the benchmark's structure and operation. Transparency measures include:

• Publication of methodology documents outlining calculation processes and revenue components.
• Historical data updates to maintain accuracy and consistency.
• Advance notification of significant changes with a two-week consultation period.
• Documentation of stakeholder feedback and responses, available upon request by emailing team@modoenergy.com.

7. Methodology changes

7.1 Review and update process

The methodology undergoes a structured review process to ensure it remains aligned with evolving market conditions and regulatory requirements. Reviews are conducted:

• Annually by the Benchmark Oversight Function.
• Quarterly manual audits to assess data accuracy and consistency.
• Upon identification of material market changes or data availability (e.g. inclusion of the Energy Imbalance Reserve; ISO-NE-specific ancillary activation data).
  • Direct back-testing against observed transaction data is not possible because per-asset revenue is not publicly disclosed in ISO-NE. Instead, Modo Energy validates the revenue-stack composition against its published ISO-NE BESS market analysis, drawing on regions where both perfect-foresight modelled revenues and realised asset earnings are observable.

Each review follows a documented approval process, ensuring updates are thoroughly evaluated before implementation.

7.2 Notification of changes

Significant methodology changes are communicated to stakeholders with sufficient advance notice and a clear timeline for review and feedback. The notification process includes:

• Publishing proposed changes with a detailed impact analysis.
• Allowing stakeholders a two-week consultation period to provide comments.
• Providing formal responses to stakeholder feedback and incorporating adjustments where appropriate.
• Maintaining an archive of all changes to ensure historical comparability and transparency.

8. Consistency and continuity

8.1 Quality assurance

Modo Energy employs rigorous quality assurance processes to ensure benchmark integrity. These include:

• Continuous automated validation checks to identify discrepancies in input data.
• Automated regression tests on every change to the Global Dispatch Model.
• Quarterly manual audits to verify data sources and methodology compliance.
• Internal audits to ensure alignment with regulatory standards.

8.2 Data integrity

Data integrity is maintained through:

• Secure data management protocols, including access controls and regular backups.
• Clear traceability from raw ISO-NE source data into the model feed.

8.3 Handling data quality issues

Modo Energy has clear procedures to address instances where the quantity or quality of input data falls below the standards required for accurate and reliable benchmark determination:

1. Data issue verification: when data quality issues are identified, Modo Energy confirms the issue with the upstream data provider.
2. Customer communication: customers are informed of any confirmed data issues and corrective actions taken to maintain transparency within 48 hours of confirmation.
3. Data unavailability: in cases where the data provider is unable to supply the required data, Modo Energy notifies customers of impacts and publishes the Index only once finalised data is available.

8.4 Traceability and verification

Modo Energy ensures all benchmark calculations are fully traceable and verifiable through:

• Maintaining comprehensive records of input data, model version, and calculation outputs.
• Reproducibility: every published value can be reproduced from the archived input data and the model version in use at the time of publication.
• Public disclosure of material methodology changes.

Appendix I — Methodology changes

Methodology changes since first publication will be tracked here.

Clarification updates

None at initial publication.

Disclaimer

This document, including the methodologies and benchmarks described herein, is the proprietary work of MODO ENERGY LIMITED ("Modo Energy") and is provided solely for informational purposes. These benchmarks are designed for use in financial analysis, benchmarking, and decision-making. However, they do not constitute investment advice or a recommendation regarding any specific financial instrument, asset, or strategy.

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