ERCOT 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 ERCOT Virtual Asset Methodology sets out how Modo Energy's simulated battery energy storage benchmarks for the Electric Reliability Council of Texas (ERCOT) 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 ERCOT virtual asset Benchmarks measure

The ME vBESS ERCOT Benchmarks measure the simulated market revenue of a standardized grid-scale lithium-ion BESS in ERCOT, expressed per MW of rated power. The family of benchmarks is grouped by pricing node and system duration, so revenues can be compared across locations and battery configurations. Each Benchmark reflects revenue a representative asset could earn from ERCOT's energy and ancillary service markets rather than a measurement of any individual operator.

1.2 Why ERCOT has a simulated benchmark alongside a measured one

The ERCOT virtual asset Benchmark is simulated, not measured.

ERCOT is notable among US ISOs in that it publishes asset-level data. Through its 60-day disclosure reports, ERCOT publishes the ancillary service awards and telemetered net output of individual units. Combined with settlement point prices and ancillary service clearing prices, this data allows Modo Energy to produce the measured ME BESS ERCOT Index, which tracks the realized revenues of the operating fleet.

A measured index answers the question "what did batteries in ERCOT earn?" It cannot isolate what a standardized asset would have earned at a specific location, independent of operator strategy, outages, contract positions, or the mix of durations in the fleet. The virtual asset Benchmark fills that gap.

1.3 Why this method is used

Modo Energy simulates a representative battery trading against the prices ERCOT actually published, using 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 ERCOT data, so every input can be traced to an ERCOT publication;
• applies a fixed, disclosed asset specification at each pricing node, so changes in Benchmark values reflect changes in market prices rather than changes in the asset or its operator;
• uses the same model and structure as Modo Energy's simulated benchmarks in other ISOs (CAISO, SPP, PJM, MISO, NYISO, ISO-NE), so ERCOT values can be compared with them; and
• can be compared against the measured ME BESS ERCOT Index, because ERCOT publishes both the prices and the asset-level outcomes.

The model optimizes with perfect knowledge of the day's prices, so its raw output can overstate 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 ERCOT Benchmarks are also provided.

3.1 The ME vBESS ERCOT Benchmarks

Modo Energy produces a family of ten benchmarks for ERCOT. The benchmarks vary in the duration and settlement point of the simulated battery:

• The durations are: 1-, and 2-hours.
• The settlement points are the five zonal hubs in ERCOT: the north (HB_NORTH), south (HB_SOUTH), west (HB_WEST), Houston (HB_HOUSTON), and the panhandle (HB_PAN) hubs.

The naming convention for this family of benchmarks is ME vBESS ERCOT [settlement point] ([duration]H) — for example, the 2-hour benchmark for the western hub is "ME vBESS ERCOT West (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 acceptance rates, AS expected throughput and calibration factor, the start- and end-of-day SoC, and the minimum spread.

3.3 How the Benchmark is calculated

For each operating day, the benchmark is calculated as follows:

1. Run the GDM for the day using the representative battery (§3.2) and the input data (§5).
2. Compute the revenues earned in each modeled market, in nominal USD, as the cleared price multiplied by the simulated position in each interval.
3. Multiply the revenue earned in each market by the calibration factor (§6.6).
4. Sum revenue across markets and divide by the asset's rated power to arrive at the USD/MW value of the Benchmark for the operating day.

Values for longer periods (a month, a week) 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 ERCOT 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 ERCOT settlement and late postings.

3.6 Market evolution reflected in the benchmark

4. Revenue Components

Modo Energy's virtual asset BESS benchmarks for ERCOT only consider wholesale revenue opportunities: energy and ancillary service. PPAs and other contracted agreements are not included.

4.1 Revenue components in the Benchmark

Real-time ancillary revenues are only available from the RTC+B go-live on 5 December 2025. Before that date, ancillary services are modeled in the day-ahead market only.

Real-time energy revenue is earned on deviations from the day-ahead schedule: the day-ahead position settles at the day-ahead price, and additional charging or discharging in real time settles at the real-time price.

All ancillary products are capacity products: the battery is paid per MW of reserve held. 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 settlement of ancillary deployments: deployed energy affects SOC but is not separately settled.
• Four Coincident Peak (4CP) transmission cost avoidance: depends on the asset's load-serving and transmission arrangements; not modeled.
• Congestion revenue rights / congestion hedging: portfolio-specific; not modeled.
• Network / transmission access charges: site-specific; set to zero.
• Bilateral contracts (tolls, index swaps, floors): not modeled.
• Operator fees, warranty, O&M: the Benchmark reports gross market revenue, not net of operating costs.

5. Data Inputs

5.1 Sources

The Benchmark uses four price datasets, all published by ERCOT. No other price sources are used.

5.2 Derived inputs

Two further inputs describe how ancillary awards behave in practice. Both are built by Modo Energy from ERCOT publications and are held as fixed input files, updated when Modo Energy revises them (§7):

• Acceptance rates, derived from the day-ahead Ancillary Service Plan (NP4-33-CD) and the 60-day DAM disclosure reports (NP3-966-ER), which give ancillary awards by resource type.
• Expected throughput, derived from ERCOT's regulation deployment analysis and ancillary service capacity monitoring data.

5.3 How inputs are combined

• All inputs are aligned to a common 15-minute grid on the operating-day basis (UTC−06:00). Hourly day-ahead prices apply to each 15-minute interval within their hour.
• Energy and ancillary prices are used as published by ERCOT. Modo Energy does not cap, filter or adjust prices, including during scarcity events.
• Acceptance rates and expected throughput are applied hourly, and set the limits and SOC effects of ancillary positions (§6.3, §6.5).
• Revenue from each market is computed separately and then summed (§3.3).

5.4 Acceptance rates and expected throughput

Acceptance rates. A single battery offering into ERCOT's ancillary markets does not clear its full capacity every hour: each service's requirement is finite and shared with the rest of the fleet and with load resources. The model caps the capacity the representative asset can hold in each service, each hour, at rated power multiplied by the acceptance rate. The acceptance rate is the BESS share of each service's requirement divided by BESS fleet capacity, by month and hour. It limits volume, not price.

As the fleet has grown, acceptance rates have fallen sharply: for example, from roughly 20% to under 5% of rated power for Regulation Up between 2023 and 2026.

Expected throughput. Held capacity is converted into energy that moves SOC using a fixed profile per service and hour of day. Regulation profiles are built from ERCOT regulation deployment data (deployed MW divided by procured MW). Reserve products are deployed rarely, so they carry very small values.

5.5 Data priority, fallbacks and minimum data

• Priority. ERCOT-published prices are the only price source. Where ERCOT republishes or corrects 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).
• Products not yet in existence. Prices for ECRS before 10 June 2023, and for real-time ancillary services before 5 December 2025, are set to zero, so the representative asset earns nothing from them.
• Acceptance rates before May 2023. The acceptance-rate input begins on 30 April 2023. For prior periods the earliest observation is backfilled.
• Minimum data. A Benchmark value is only determined for an operating day when the day-ahead energy, real-time energy and day-ahead ancillary price datasets (and, from 5 December 2025, real-time ancillary prices) are available for that day. If any required 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 Up/Down, RRS, ECRS and Non-Spin, in the day-ahead market and, from RTC+B, the real-time market). Revenue is the sum of these streams net of charging cost, subject to the constraints in §6.3–6.5.

6.2 Sequential day-ahead and real-time optimization

1. Day-ahead step (60-minute granularity): day-ahead energy and the five day-ahead ancillary products are optimized together, mirroring ERCOT's co-optimized DAM. The resulting schedule and awards are committed.
2. Real-time step (15-minute granularity): holding the day-ahead commitments fixed, the model optimizes real-time energy and, from 5 December 2025, the five 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 (Regulation Up, RRS, ECRS, Non-Spin) cannot exceed rated power, and nor can charging positions (Regulation Down).
2. State-of-charge duration requirements: each discharging position must be backed by enough stored energy to deliver it for the duration ERCOT requires, and each charging position by enough empty capacity:

   Service	Before RTC+B	From 5 Dec 2025
   Regulation Up / Down	1 hour	0.5 hours
   RRS	1 hour	0.5 hours
   ECRS	2 hours	1 hour
   Non-Spin	4 hours	4 hours

3. Acceptance limits: capacity held in each service is capped at rated power times that hour's acceptance rate (§5.4).
4. Grid limits: combined charge and discharge cannot exceed the 100 MW import and export limits.
5. 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%. SOC at the end of each operating day must equal SOC at the start; the model chooses that level.
• 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 model moves SOC by the held capacity multiplied by the expected throughput for that service and hour (§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 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 informed by comparing perfect-foresight GDM revenues with realized revenues in markets where asset-level data is published: ERCOT and Great Britain. Because the same factor applies to every revenue stream, it does not change the trade-offs between markets in the optimization; 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, minimum spread and price smoothing window (§3.2);
• the choice of trading hubs (§3.1);
• the construction of acceptance rates and expected throughput (§5.4), including how ERCOT source data is interpreted, for example where awards for a service appear on both the generation and load side of a battery; and
• the decision not to apply a calibration factor (§6.6).

7.2 Keeping judgment consistent

Parameters and derived input files are held in the GDM's version-controlled codebase and its input store, and applied identically to every operating day and every ERCOT Benchmark. They are changed only through a code or input change that is reviewed by a member of Modo Energy's modeling team other than its author and, for code, passes automated tests before release. Changes to parameters or derived inputs 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 ERCOT 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 events, extreme weather or other stress 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 ERCOT's published data. Publication for the affected days may be held until the issue is resolved.
• Market redesign or suspension. If ERCOT changes its market design (as with RTC+B) or suspends a market, the Benchmark reflects the prices ERCOT 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:

• ERCOT corrects or republishes prices for a covered period;
• an error is identified in Modo Energy's processing of the input data; or
• a methodology change (§11), including an update to acceptance rates or expected throughput, is applied to historical periods.

10. Methodology review

The methodology is reviewed internally by Modo Energy, including when ERCOT changes its market design, when new data becomes available (for example, updated ancillary award or deployment data), or when changes are made to the GDM. Because ERCOT publishes asset-level data, reviews may include comparing the Benchmark with the measured ME BESS ERCOT Index. 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 the transactions or revenues of any real asset. It may differ materially from the measured ME BESS ERCOT Index and from any individual asset.
• Foresight and calibration. The model optimizes with perfect foresight. The 80% calibration factor is a single flat value derived from Great Britain. Actual foresight losses vary over time and between markets.
• Thin and concentrated ancillary markets. ERCOT's ancillary requirements are small relative to the battery fleet, and batteries now hold most of some services. Acceptance rates are fleet-average, pro-rata values: an individual asset may clear more or less than its share. The asset is assumed not to affect clearing prices.
• No acceptance limit before May 2023. Ancillary revenue for earlier periods may be overstated (§5.5).
• Ancillary activation. Expected-throughput profiles are fixed by hour of day and do not reflect specific deployment events. Deployed energy is not settled.
• Time basis. Operating days are on a fixed UTC−06:00 basis. During daylight saving time, each modeled day is offset by one hour from ERCOT's prevailing-time operating day.
• Asset assumptions. The asset is always available and does not degrade. Real assets differ on both.
• Excluded revenues. The revenues and costs listed in §4.2, including 4CP, 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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