SPP 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 SPP Methodology Framework sets out how Modo Energy's battery energy storage benchmarks for the Southwest Power Pool (SPP) 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 SPP Benchmark represents

The ME BESS SPP Benchmarks represent the simulated revenue performance of grid-scale lithium-ion BESS in SPP. 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 SPP requires a simulated benchmark

The SPP 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.

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

To benchmark under these conditions, Modo Energy simulates the dispatch of a representative battery against publicly available SPP 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 BESS SPP Benchmarks

Modo Energy currently produces a family of three benchmarks for SPP:

All three benchmarks are constructed using the same published methodology, with differences only in the duration parameter of the representative asset.

2.2 Representative BESS specification

The representative asset is specified as follows:

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 revenues. This value accounts for the perfect-foresight nature of the GDM, and brings the simulated revenues in-line with realized ones. The precise value of 80% is informed by historical analysis of the GDM's performance in ERCOT and Great Britain (both operators publish asset-level data).

The haircut is applied uniformly to all revenue streams that the simulated asset engages in. By applying it across both energy and ancillary service markets, the trade-offs between different markets are not modified during optimization.

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 SPP 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 SPP postings.

Published values may be revised if SPP corrects 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 SPP Benchmarks capture the primary revenue opportunities available to a representative BESS in SPP's wholesale and ancillary markets. The table below details each component, the market from which it is modeled, and the price signal used.

All the ancillary products are capacity products: the battery is paid per MW of reserve held, with their bids co-optimized alongside those for the energy market. Energy delivered when an ancillary service is called upon moves the battery's state-of-charge and counts toward cycling (§5.6), 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 SPP Benchmark:

• Capacity-accreditation / resource-adequacy payments: not a continuously cleared, publicly priced market product.
• 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 SPP market data. The four datasets used are the nodal energy prices (LMP) for the day-ahead and real-time markets; and the zonal ancillary service clearing prices, also for the day-ahead and real-time markets.

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

Locational marginal prices are nodal, whereas ancillary service prices clear at the reserve zone-level. Modo Energy uses a mapping provided by SPP to determine which reserve zone a node belongs to. Two published ME BESS SPP Benchmarks use the SPP East hubs; the third uses the new SPP West:

4.2 Use of discretion

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

• Node and reserve-zone selection. The published benchmarks affix energy prices to the SPPSOUTH_HUB, SPPNORTH_HUB, and SWPW_HUB hubs, with ancillary service prices drawn from SPP and SWPW as described above. These hubs are chosen as representative reference points for the prices that utility-scale batteries are exposed to in SPP.
• Ancillary expected throughput. SPP 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 (§5.6). These are conservative best-guess values derived from observed ERCOT behavior, reviewed under §7 as SPP-specific data accumulates.

The assumed throughput values for day-ahead and real-time ancillary services in the SPP benchmark 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 SPP day-ahead and real-time energy markets, earning the LMP on discharge and paying it on charge.
• Ancillary capacity — Regulation Up/Down, Spinning, Supplemental, Ramp Up/Down, and Uncertainty, where the battery is paid to hold reserve available for SPP.

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 Two-step co-optimization

SPP's day-ahead energy and ancillary markets clear together in one market scheduling step — to reflect this, the dispatch model optimizes all eight products simultaneously in a single day-ahead process. The same is done for the real-time markets each day.

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

5.3 Market stacking and physical limits

SPP 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 stored energy and discharge headroom for discharging products (Reg Up, Spin, Supplemental, Ramp Up, Uncertainty), and enough charging headroom for charging products (Reg Down, Ramp Down).
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 seven SPP ancillary products are capacity products: the battery is paid to hold energy reserves. The asset bids into the co-optimized market and earns the cleared capacity price (USD/MW/h). Holding capacity reduces the power and SOC available to the energy market.

Being activated to deliver energy is distinct. Upon being awarded an ancillary service contract, the amount of energy delivered in fulfillment of said contract varies by asset, by day, and by time of day (in SPP that quantity of energy is also unobservable in public data). The Modo Energy SPP Benchmark counts the capacity payment associated with the contract as benchmarked revenues, 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 from observed ERCOT behavior (§4.2): regulation carries the highest expected throughput; reserve, ramp, and uncertainty carry very little.

5.6 Locational pricing

SPP is nodal. Energy revenues are earned from the LMP at the asset's connection node (SPPSOUTH_HUB in the published benchmark). Because LMPs embed congestion and losses, node choice materially affects energy revenue; the South hub is a representative, liquid reference. Ancillary prices clear by reserve zone and do not vary by node within a zone.

5.7 Revenue calibration

Modo Energy's simulated SPP Benchmark applies a flat 80% calibration factor to the 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.

This same calibration factor is applied to all revenue streams in the Modo Energy SPP Benchmark. The specific value of the calibration factor, 80%, is informed by analysis of BESS fleets in regions with asset-level data: ERCOT, and Great Britain.

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. the SWPW hub/zone; SPP-specific ancillary activation data).
  • Direct back-testing against observed transaction data is not possible because per-asset revenue is not publicly disclosed in SPP. Instead, Modo Energy validates the revenue-stack composition against its published SPP BESS market analysis, drawing on regions where both perfect-foresight modelled revenues and realised asset earnings are observable (e.g. Great Britain, ERCOT).

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 SPP 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.

While Modo Energy strives to ensure the accuracy, reliability, and transparency of the benchmarks and methodologies, all information is provided "as is", without any express or implied warranties, including but not limited to warranties of merchantability or fitness for a particular purpose. Users should be aware that the benchmarks are derived from publicly available market data that may be subject to revisions, delays, or inaccuracies, and that the SPP Benchmarks are simulated from a representative asset rather than observed from underlying transactions. Past performance is not indicative of future results, and external factors such as regulatory changes, market conditions, and asset-specific characteristics may impact benchmark performance.

Modo Energy encourages users to conduct their own due diligence and consult with qualified financial professionals before making any investment or operational decisions based on the benchmarks or methodologies herein. Modo Energy disclaims any liability for direct, indirect, incidental, or consequential losses or damages arising from the use of the benchmarks, methodologies, or related data.

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