NYISO 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 NYISO Methodology sets out how Modo Energy's battery energy storage benchmarks for the New York Independent System Operator (NYISO) 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 NYISO Benchmarks measure

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

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

1.2 Why NYISO requires a simulated benchmark

The NYISO Benchmark is simulated, not measured.

NYISO 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 Based Marginal Prices, LBMP) 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 NYISO is to simulate a battery trading against the prices NYISO 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 NYISO price data, so every input can be traced to a NYISO 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 NYISO 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 NYISO Benchmarks are also provided.

3.1 The ME vBESS NYISO Benchmarks

Modo Energy produces a family of 22 benchmarks for NYISO. The benchmarks vary in the duration and pricing location of the simulated battery:

• The durations are: 2-, and 4-hours
• The settlement locations are the 11 load zones (Zones A-K).

The naming convention for this family of benchmarks is ME vBESS NYISO [load zone] ([duration]H) — for example, the 2-hour benchmark for the NYC hub is "ME vBESS NYISO NYC (2H)". The Load Zone name is a more human-friendly version of the ID used for the pricing location.

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 net revenues per unit of rated power, in the following units:

• USD/MW/year (annualized): total revenue earned in a period, divided by the number of days in the period and multiplied by 365.
• USD/MW/hour (hourly): total revenue earned in a period, divided by the number of hours in that period.

In both the Modo Energy Terminal and Modo Energy API, users can optionally normalize by the battery's energy capacity. The headline representation remains the rated power-normalized value.

3.5 Publication cadence

The Benchmarks are updated daily. Modo Energy ingests NYISO day-ahead and real-time energy and ancillary service 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 NYISO settlement and late postings.

3.6 Market evolution reflected in the Benchmark

4. Revenue Components

4.1 Included revenues

All ancillary products are capacity products: the battery is paid per MW of reserve held. Regulation is offered symmetrically: the battery holds equal upward and downward capacity and is paid a single Regulation capacity price. 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

• Regulation movement (mileage) payments: 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.
• Installed capacity (ICAP) payments: not included in the Benchmark.
• Transmission congestion contracts / congestion hedging: portfolio-specific; not modeled.
• Curtailment compensation: site-specific; not modeled.
• Network / transmission access charges: site-specific; set to zero.
• Distribution-level programs (e.g. VDER) and bilateral contracts (PPAs, tolls, floors): not modeled in backtests.
• 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 four datasets, all published by NYISO. 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 regions

NYISO publishes LBMP for each of its 11 load zones. Regulation clears at a single statewide (NYCA) price. The three reserve products vary by location, reflecting NYISO's nested locational reserve requirements (see the NYISO Locational Reserve Requirements):

• NYCA (Zones A-K, statewide): all three reserve products plus Regulation
• EAST (Zones F-K, east of the Central-East interface): 10-Min Spinning, 10-Min Total and 30-Min Operating
• SENY (Zones G-K, Southeastern New York): 30-Min Operating only
• NYC (Zone J): 10-Min Total and 30-Min Operating
• LI (Zone K): 10-Min Total and 30-Min Operating

The model uses the reserve prices NYISO publishes for the selected load zone, which already reflect the reserve regions that zone sits in. There is a single decision variable per product, settled at the zone's price; the nested reserve regions are not modeled as separate markets.

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 NYISO. 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

NYISO 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 from ERCOT, where activation is observable, and do not vary by time of day.

5.5 Data priority, fallbacks and minimum data

• Priority. NYISO-published prices are the only price source.
• 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 four datasets in §5.1 are available for that day at the load 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 (Regulation, 10-Minute Spinning, 10-Minute Non-Synchronous and 30-Minute Operating Reserve). Revenue is the sum of these streams net of charging cost, subject to the constraints in §6.3–6.5.

6.2 Two-step optimization

1. Day-ahead step (60-minute granularity): day-ahead energy and the four ancillary products are optimized together, mirroring NYISO's co-optimized DAM. The resulting positions are committed.
2. Real-time step (5-minute granularity): holding the day-ahead positions fixed, the model optimizes real-time energy and the four real-time ancillary products within the asset's remaining headroom.

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, 10-Min Spinning, 10-Min Non-Synchronous, 30-Min Operating) cannot exceed rated power, and nor can downward Regulation.
2. Energy headroom: each ancillary position must be backed by enough stored energy (or, for downward Regulation, enough empty capacity) to deliver at full power for the period below:

   Service	Day-ahead	Real-time
   Regulation	0.01 hours	0.01 hours
   10-Minute Spinning Reserve	0.5 hours	1 hour
   10-Minute Non-Synchronous Reserve	0.5 hours	1 hour
   30-Minute Operating Reserve	1 hour	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. Holding capacity reduces the power and energy available to the energy markets. The energy actually deployed against a capacity award varies by asset, day and time, and is not observable in NYISO 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 choice of load zone (§5.2);
• the ancillary expected-throughput values (§5.4); 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 modelling 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 NYISO 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 are used as published. The Benchmark is intended to reflect those conditions, so they are not removed or capped.
• Suspected data errors. Where input data appears erroneous, Modo Energy checks it against NYISO's published data. Publication for the affected days may be held until the issue is resolved.
• Market suspension or rule changes. If NYISO suspends a market or changes its design, the Benchmark reflects the prices NYISO 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 NYISO changes its market design, when new data becomes available (for example, NYISO-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 NYISO, and cannot be validated directly against NYISO asset revenues because NYISO 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 NYISO. 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. NYISO's reserve and regulation requirements are small, particularly in the NYC and LI localities, 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.
• Ancillary activation. Expected-throughput values are derived from ERCOT, not NYISO, 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 revenue, are not captured.

Appendix I — Change log

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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