CAISO 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 CAISO Methodology Framework sets out how Modo Energy's virtual battery energy storage benchmarks for the California Independent System Operator (CAISO) 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 CAISO Benchmark represents

The ME BESS CAISO Benchmarks represent the simulated revenue performance of grid-scale BESS in CAISO. 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 CAISO requires a simulated asset-level benchmark

The CAISO virtual asset 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.

CAISO does not make equivalent asset-level data publicly available. CAISO publishes market clearing prices for energy markets (locational marginal prices) and each ancillary service (the marginal clearing 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 CAISO market data, using its Global Dispatch Model (GDM) — a mixed-integer linear program (MILP) that maximizes battery revenue subject to market and physical constraints.

Unlike other ISOs in the United States, however, CAISO does report the operations of the BESS fleet as a whole. Their Daily Energy Storage Reports publicize the capacity awards for each of the ancillary services, as well as the energy export/import instructions for the three energy market runs. This data is used to construct the Modo Energy CAISO System Benchmark.

2. Benchmark Construction

2.1 The ME BESS CAISO Benchmarks

Modo Energy currently produces six virtual benchmarks for CAISO:

All six benchmarks are constructed using the same published methodology, with differences only in the location parameter of the representative asset. The benchmarks all represent the revenues earned by a four-hour battery at their respective nodes — this is the most common duration for grid-scale BESS in CAISO.

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.

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 CAISO 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 CAISO postings. CAISO market data is timestamped in Pacific Prevailing Time (PPT), which observes daylight saving time (PST in winter, PDT in summer), and the benchmark follows this convention.

Published values may be revised if CAISO 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.

3. Revenue Components

3.1 Revenue components included in the Benchmarks

The Modo Energy CAISO Benchmarks capture the primary wholesale revenue opportunities available to a representative BESS in CAISO's energy 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 (§5.5), 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 CAISO 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

CAISO's OASIS interface is used to collate the energy and ancillary service market prices. The three energy datasets are:

• Locational Marginal Prices: the Integrated Forward Market (IFM) prices for day-ahead energy.
• FMM Locational Marginal Prices: the Fifteen-Minute Market (FMM) prices for real-time pre-dispatch energy.
• Interval Locational Marginal Prices: the Real-Time Dispatch (RTD) prices for real-time dispatch energy.

The day-ahead and RTPD ancillary prices are sourced from the AS Clearing Prices and Interval AS Clearing Prices datasets, respectively. Modo Energy's CAISO asset database is used to calibrate the "acceptance rate" for ancillary services (§4.2).

Users are not limited to selecting hub-level energy prices for their custom vBESS benchmarks: the Modo Energy vBESS CAISO product supports fully nodal siting for the virtual assets. In all cases, the reserve zone AS_CAISO_EXP is used to price the ancillary services. This aligns with the ME BESS CAISO system benchmark.

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.

4.2 Use of discretion

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

• Ancillary service expected throughput. CAISO publishes Attenuation Factors for regulation services at a quarterly frequency. This dataset reports the expected MWh of energy imports (for regulation down) and exports (for regulation up) in each hour of the day, for a 1MW regulation contract. The first of these reports relates to 2023-Q4, and the CAISO vBESS benchmark begins in April 2023. The daily profile of the attenuation factors are extrapolated backwards in time for observations prior to 2023-Q4. Expected throughput for Spinning Reserve is fixed at 0.10%; Non-Spinning Reserve expected throughput is 0.01%. Neither of these assume a daily profile, they are flat values at all hours of the day. For all ancillary services, the same throughput values are used in the day-ahead and RTPD markets.
• Ancillary service acceptance rates. Across grids, battery penetration within a grid and the proportion of ancillary service contracts awarded to BESS are positively related. CAISO's battery fleet is many multiples larger than the total AS requirement for any interval of the day — as a result, nearly all AS contracts are given to BESS units. However, the odds of an individual BESS unit being awarded an AS contract fall as the fleet grows. To account for this, the ancillary service prices fed into the dispatch model are multiplied by the ratio of the simulated battery's nameplate to the total rated power of the fleet. The fleet's total rated power is computed from the Modo Energy CAISO asset database.

5. Modeling 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 CAISO day-ahead, FMM, and RTD energy markets, earning the LMP on discharge and paying it on charge.
• Ancillary capacity — Regulation (bi-directional), Spinning Reserve, and Non-Spinning Reserve, where the battery is paid to hold reserve available for CAISO.

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 Three-step co-optimization

The dispatch model solves CAISO's three energy markets sequentially. At the day-ahead and FMM stages, it co-optimizes energy with the four ancillary services. Later stages respect the commitments made in earlier ones.

• Granularity: 60 minutes for day-ahead energy and ancillaries; 15 minutes for FMM energy and RTPD ancillaries; and 5 minutes for RTD energy. These align with CAISO's clearing schedule and the underlying data feed used.
• Foresight: there is perfect foresight of energy and ancillary prices over the optimization horizon.

5.3 Market stacking and physical limits

CAISO 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 (Regulation Up, Spinning and Non-Spinning Reserves), and enough charging headroom for Regulation 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 CAISO 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 CAISO that quantity of energy is also unobservable in public data). The Modo Energy CAISO 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.

The attenuation factors in (§4.2) affect the battery's expectation of energy throughput. This allows the dispatch model to more precisely balance the trade-off between energy arbitrage and ancillary service provision. As stated above, the battery is not separately paid for the energy exported/imported in fulfillment of said contracts. To use the market language, the "mileage" component of regulation is not paid in these virtual asset benchmarks.

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.
  • Direct back-testing against observed transaction data is not possible because per-asset revenue is not publicly disclosed in CAISO. Instead, Modo Energy validates the revenue-stack composition against its published CAISO 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 CAISO 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 CAISO 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.

It is not possible to invest directly in a benchmark. Benchmarks are intended to represent performance references, and exposure to an asset class represented by a benchmark may be available only through separate investable instruments. Modo Energy does not sponsor, endorse, or manage any financial products that aim to track the performance of its benchmarks.

All intellectual property rights to the benchmarks and methodologies are owned by Modo Energy. Unauthorised use, reproduction, or redistribution of this document, in whole or in part, is strictly prohibited without prior written consent from Modo Energy.

This document and the benchmarks it describes are subject to updates and revisions. Significant changes will be communicated to stakeholders as appropriate. For further information, including licensing inquiries, please contact Modo Energy directly.

By accessing or using this document, you acknowledge and accept the terms of this disclaimer.